[med-svn] [r-bioc-summarizedexperiment] 10/12: New upstream version 1.6.3
Andreas Tille
tille at debian.org
Mon Oct 2 09:54:52 UTC 2017
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository r-bioc-summarizedexperiment.
commit 28bc7c1db76af330cc707ca7cf2caeb8136f789d
Author: Andreas Tille <tille at debian.org>
Date: Mon Oct 2 11:52:32 2017 +0200
New upstream version 1.6.3
---
DESCRIPTION | 30 +
NAMESPACE | 123 +
NEWS | 120 +
NOTES | 35 +
R/Assays-class.R | 377 +++
R/RangedSummarizedExperiment-class.R | 572 ++++
R/SummarizedExperiment-class.R | 844 ++++++
R/coverage-methods.R | 15 +
R/findOverlaps-methods.R | 42 +
R/inter-range-methods.R | 22 +
R/intra-range-methods.R | 79 +
R/makeSummarizedExperimentFromDataFrame.R | 32 +
R/makeSummarizedExperimentFromExpressionSet.R | 265 ++
R/nearest-methods.R | 117 +
R/readKallisto.R | 196 ++
R/saveHDF5SummarizedExperiment.R | 127 +
R/zzz.R | 2 +
build/vignette.rds | Bin 0 -> 209 bytes
debian/README.test | 15 -
debian/changelog | 61 -
debian/compat | 1 -
debian/control | 30 -
debian/copyright | 107 -
debian/docs | 1 -
debian/patches/series | 1 -
.../skip_test_needing_not_packaged_database.patch | 54 -
debian/rules | 4 -
debian/source/format | 1 -
debian/tests/control | 3 -
debian/tests/run-unit-test | 6 -
debian/watch | 3 -
inst/doc/SummarizedExperiment.R | 101 +
inst/doc/SummarizedExperiment.Rmd | 345 +++
inst/doc/SummarizedExperiment.html | 541 ++++
inst/extdata/kallisto/abundance.h5 | Bin 0 -> 1086722 bytes
inst/extdata/kallisto/abundance.tsv | 2859 ++++++++++++++++++++
inst/extdata/kallisto/abundance.txt | 16 +
inst/extdata/kallisto/run_info.json | 8 +
inst/scripts/Find_and_update_objects/README | 68 +
.../collect_rda_objects_to_update.R | 133 +
.../data_store_RDA_OBJECTS_TO_UPDATE | 30 +
.../pkgs_RDA_OBJECTS_TO_UPDATE | 9 +
.../Find_and_update_objects/scan_rda_files.R | 68 +
.../Find_and_update_objects/update_rda_objects.R | 70 +
inst/unitTests/test_Assays-class.R | 88 +
.../test_RangedSummarizedExperiment-class.R | 375 +++
inst/unitTests/test_SummarizedExperiment-class.R | 339 +++
inst/unitTests/test_coverage-methods.R | 56 +
inst/unitTests/test_findOverlaps-methods.R | 92 +
inst/unitTests/test_inter-range-methods.R | 52 +
inst/unitTests/test_intra-range-methods.R | 94 +
.../test_makeSummarizedExperimentFromDataFrame.R | 56 +
...est_makeSummarizedExperimentFromExpressionSet.R | 187 ++
inst/unitTests/test_nearest-methods.R | 70 +
man/Assays-class.Rd | 172 ++
man/RangedSummarizedExperiment-class.Rd | 335 +++
man/SummarizedExperiment-class.Rd | 454 ++++
man/coverage-methods.Rd | 80 +
man/findOverlaps-methods.Rd | 105 +
man/inter-range-methods.Rd | 95 +
man/intra-range-methods.Rd | 151 ++
man/makeSummarizedExperimentFromDataFrame.Rd | 87 +
man/makeSummarizedExperimentFromExpressionSet.Rd | 112 +
man/nearest-methods.Rd | 149 +
man/readKallisto.Rd | 120 +
man/saveHDF5SummarizedExperiment.Rd | 144 +
tests/run_unitTests.R | 2 +
vignettes/SummarizedExperiment.Rmd | 345 +++
68 files changed, 11006 insertions(+), 287 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
new file mode 100644
index 0000000..5cb8739
--- /dev/null
+++ b/DESCRIPTION
@@ -0,0 +1,30 @@
+Package: SummarizedExperiment
+Title: SummarizedExperiment container
+Description: The SummarizedExperiment container contains one or more assays,
+ each represented by a matrix-like object of numeric or other mode.
+ The rows typically represent genomic ranges of interest and the columns
+ represent samples.
+Version: 1.6.3
+Encoding: UTF-8
+Author: Martin Morgan, Valerie Obenchain, Jim Hester, Hervé Pagès
+Maintainer: Bioconductor Package Maintainer <maintainer at bioconductor.org>
+biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage,
+ GenomeAnnotation
+Depends: R (>= 3.2), methods, GenomicRanges (>= 1.27.22), Biobase,
+ DelayedArray (>= 0.1.9)
+Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.15.3),
+ S4Vectors (>= 0.13.13), IRanges (>= 2.7.2), GenomeInfoDb (>=
+ 1.11.4)
+Suggests: annotate, AnnotationDbi, hgu95av2.db, GenomicFeatures,
+ TxDb.Hsapiens.UCSC.hg19.knownGene, BiocStyle, knitr, rmarkdown,
+ digest, jsonlite, rhdf5, HDF5Array (>= 1.4.8), airway, RUnit
+VignetteBuilder: knitr
+License: Artistic-2.0
+Collate: Assays-class.R SummarizedExperiment-class.R
+ RangedSummarizedExperiment-class.R intra-range-methods.R
+ inter-range-methods.R coverage-methods.R findOverlaps-methods.R
+ nearest-methods.R makeSummarizedExperimentFromExpressionSet.R
+ makeSummarizedExperimentFromDataFrame.R readKallisto.R
+ saveHDF5SummarizedExperiment.R zzz.R
+NeedsCompilation: no
+Packaged: 2017-05-29 00:08:18 UTC; biocbuild
diff --git a/NAMESPACE b/NAMESPACE
new file mode 100644
index 0000000..eaee9f8
--- /dev/null
+++ b/NAMESPACE
@@ -0,0 +1,123 @@
+import(methods)
+importFrom(utils, read.delim, .DollarNames)
+importFrom(stats, setNames)
+importFrom(tools, file_path_as_absolute)
+importClassFrom(Matrix, Matrix) # for the "acbind" and "arbind" methods
+import(BiocGenerics)
+import(S4Vectors)
+import(IRanges)
+import(GenomeInfoDb)
+import(GenomicRanges)
+import(Biobase)
+import(DelayedArray)
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Export S4 classes
+###
+
+exportClasses(
+ Assays, ShallowData, ShallowSimpleListAssays,
+ SummarizedExperiment,
+ RangedSummarizedExperiment
+)
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Export S4 methods for generics not defined in SummarizedExperiment
+###
+
+exportMethods(
+ length, names, "names<-",
+ dim, dimnames, "dimnames<-",
+ "[", "[<-", "[[", "[[<-", "$", "$<-",
+ as.data.frame,
+ coerce,
+ show,
+ c,
+ merge,
+ Compare,
+
+ ## Generics defined in BiocGenerics:
+ cbind, rbind,
+ duplicated, match,
+ is.unsorted, order, rank, sort,
+ start, "start<-", end, "end<-", width, "width<-",
+ strand, "strand<-",
+ updateObject,
+
+ ## Generics defined in S4Vectors:
+ "metadata<-",
+ mcols, "mcols<-",
+ elementMetadata, "elementMetadata<-",
+ values, "values<-",
+ extractROWS, replaceROWS,
+ subset,
+ pcompare,
+
+ ## Generics defined in IRanges:
+ ranges, "ranges<-",
+ shift, narrow, resize, flank, promoters, restrict, trim,
+ isDisjoint, disjointBins,
+ coverage,
+ findOverlaps,
+ precede, follow, nearest, distance, distanceToNearest,
+ acbind, arbind,
+
+ ## Generics defined in GenomeInfoDb:
+ seqinfo, "seqinfo<-",
+ seqnames, "seqnames<-",
+
+ ## Generics defined in DelayedArray:
+ rowRanges, realize
+)
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Export non-generic functions
+###
+
+export(
+ Assays,
+ .DollarNames.SummarizedExperiment,
+ .DollarNames.RangedSummarizedExperiment,
+ makeSummarizedExperimentFromExpressionSet,
+ makeSummarizedExperimentFromDataFrame,
+ naiveRangeMapper, probeRangeMapper, geneRangeMapper,
+ readKallisto, readKallistoBootstrap, KALLISTO_ASSAYS,
+ saveHDF5SummarizedExperiment, loadHDF5SummarizedExperiment
+)
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Export S4 generics defined in SummarizedExperiment + export corresponding
+### methods
+###
+
+export(
+ SummarizedExperiment,
+ "rowRanges<-",
+ rowData, "rowData<-",
+ colData, "colData<-",
+ assayNames, "assayNames<-",
+ assays, "assays<-",
+ assay, "assay<-"
+)
+
+### Exactly the same list as above.
+exportMethods(
+ SummarizedExperiment,
+ "rowRanges<-",
+ rowData, "rowData<-",
+ colData, "colData<-",
+ assayNames, "assayNames<-",
+ assays, "assays<-",
+ assay, "assay<-"
+)
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Register S3 methods
+###
+
+S3method(.DollarNames, SummarizedExperiment)
+S3method(.DollarNames, RangedSummarizedExperiment)
diff --git a/NEWS b/NEWS
new file mode 100644
index 0000000..cc48bc3
--- /dev/null
+++ b/NEWS
@@ -0,0 +1,120 @@
+CHANGES IN VERSION 1.6.1
+------------------------
+
+BUG FIXES
+
+ o '$' completion on SummarizedExperiment works in RStudio and on
+ RangedSummarizedExperiment.
+
+CHANGES IN VERSION 1.6.0
+------------------------
+
+NEW FEATURES
+
+ o Add saveHDF5SummarizedExperiment() and loadHDF5SummarizedExperiment() for
+ saving/loading HDF5-based SummarizedExperiment objects to/from disk.
+
+DEPRECATED AND DEFUNCT
+
+ o Remove SummarizedExperiment0 class (was introduced to ease transition
+ from old SummarizedExperiment class defined in GenomicRanges to new
+ RangedSummarizedExperiment class defined in SummarizedExperiment package).
+
+
+CHANGES IN VERSION 1.4.0
+------------------------
+
+NEW FEATURES
+
+ o Add makeSummarizedExperimentFromDataFrame() function.
+
+ o Add "acbind" and "arbind" methods for Matrix objects.
+
+SIGNIFICANT USER-VISIBLE CHANGES
+
+ o Speed up "cbind" method for SummarizedExperiment objects based on a
+ suggestion by Peter Hickey.
+
+DEPRECATED AND DEFUNCT
+
+ o Remove exptData() getter and setter (were defunct in BioC 3.3).
+
+BUG FIXES
+
+
+CHANGES IN VERSION 1.2.0
+------------------------
+
+NEW FEATURES
+
+ o Add 'rowData' argument to SummarizedExperiment() constructor. This allows
+ the user to supply the row data at construction time.
+
+ o The SummarizedExperiment() constructor function and the assay() setter
+ now both take any matrix-like object as long as the resulting
+ SummarizedExperiment object is valid.
+
+ o Support r/cbind'ing of SummarizedExperiment objects with assays of
+ arbitrary dimensions (based on a patch by Pete Hickey).
+
+ o Add "is.unsorted" method for RangedSummarizedExperiment objects.
+
+ o NULL colnames() supported during SummarizedExperiment construction.
+
+ o readKallisto() warns early when files need names.
+
+ o base::rank() gained a new 'ties.method="last"' option and base::order()
+ a new argument ('method') in R 3.3. Thus so do the "rank" and "order"
+ methods for RangedSummarizedExperiment objects.
+
+SIGNIFICANT USER-VISIBLE CHANGES
+
+ o Re-introduce the rowData() accessor (was defunt in BioC 3.2) as an alias
+ for mcols() and make it the preferred way to access the row data. There
+ is now a pleasant symmetry between rowData and colData.
+
+ o Rename SummarizedExperiment0 class -> SummarizedExperiment.
+
+ o Improved vignette.
+
+ o Remove updateObject() method for "old" SummarizedExperiment objects.
+
+DEPRECATED AND DEFUNCT
+
+ o exptData() is now defunct, metadata() should be used instead.
+
+BUG FIXES
+
+ o Fix bug in "sort" method for RangedSummarizedExperiment objects when
+ 'ignore.strand=TRUE' (the argument was ignored).
+
+ o Fix 2 bugs when r/cbind'ing SummarizedExperiment objects:
+ - r/cbind'ing assays without names would return only the first element.
+ See https://stat.ethz.ch/pipermail/bioc-devel/2015-November/008318.html
+ - r/cbind'ing assays with names in different order would stop() with
+ 'Assays must have the same names()"
+
+ o Fix validity method for SummarizedExperiment objects reporting incorrect
+ numbers when the nb of cols in assay(x) doesn't match the nb of rows in
+ colData(x).
+
+ o assay colnames() must agree with colData rownames()
+
+ o Fix bug where assays(se, withDimnames=TRUE) was dropping the dimnames of
+ the 3rd and higher-order dimensions of the assays. Thanks to Pete Hickey
+ for catching this and providing a patch.
+
+ o A couple of minor tweaks to the rowData() setter to make it behave
+ consistently with mcols()/elementMetadata() setters for Vector objects
+ in general.
+
+
+CHANGES IN VERSION 0.3.*
+------------------------
+
+NEW FEATURES
+
+ o readKallisto() and readKallistoBootstrap() input kallisto
+ transcript quantification output into SummarizedExperiment (and
+ other) instances.
+
diff --git a/NOTES b/NOTES
new file mode 100644
index 0000000..30ddb40
--- /dev/null
+++ b/NOTES
@@ -0,0 +1,35 @@
+We need a mechanism to save an HDF5Array-based (or more generally
+DelayedArray-based) SummarizedExperiment object to disk.
+
+- The object can have more than 1 assay. Even though most of the time these
+ assays are either all in memory (e.g. ordinary arrays or data frames) or all
+ on disk and using the same backend (i.e. all HDF5-based DelayedArray
+ objects), they can be a mix of in-memory and on-disk assays. Even a given
+ assay can use more than 1 kind of on-disk backend. For example it could be
+ the result of adding an HDF5-based DelayedArray object with a DelayedArray
+ object based on another backend. Since the addition of DelayedArray objects
+ is delayed, the result of this addition is a DelayedArray object with mixed
+ backends.
+
+- Standard mechanisms save() and saveRDS() cannot handle this complexity.
+
+- We need a mechanism that produces several files: one .rda (or .rds)
+ file containing the result of calling save() (or saveRDS()) on the
+ object + all the files (e.g. HDF5) containing the on-disk assay data.
+ The files containing the on-disk assay data can be a mix of HDF5 files
+ and other formats.
+ How should these files be bundled together? By putting them together in a
+ destination folder? By creating a tarball of this folder? Should the creation
+ of the tarball be left to the user or should the save function create it?
+
+- Should the on-disk assays with delayed operations on them be "realized"
+ before the SummarizedExperiment object is saved to disk? Doing this has
+ some significant advantages:
+ (1) It "simplifies" the object: it reduces the number of files needed to
+ store the on-disk assay data (only 1 file per on-disk assay).
+ (2) It relocates and reduces the size of the on-disk data needed to
+ represent the object.
+
+- Should the in-memory assays be converted into on-disk assays before saving?
+ Should this be controlled by the user?
+
diff --git a/R/Assays-class.R b/R/Assays-class.R
new file mode 100644
index 0000000..292ff8e
--- /dev/null
+++ b/R/Assays-class.R
@@ -0,0 +1,377 @@
+### =========================================================================
+### Assays objects
+### -------------------------------------------------------------------------
+###
+### The Assays API consists of:
+### (a) The Assays() constructor function.
+### (b) Lossless back and forth coercion from/to SimpleList. The coercion
+### method from SimpleList doesn't need (and should not) validate the
+### returned object.
+### (c) length, names, names<-, [[, [[<-, dim, [, [<-, rbind, cbind
+###
+### An Assays concrete subclass needs to implement (b) (required) plus
+### optionally any of the methods in (c).
+###
+### IMPORTANT: Methods that return a modified Assays object (a.k.a.
+### endomorphisms), that is, [ as well as replacement methods names<-, [[<-,
+### and [<-, must respect the copy-on-change contract. With objects that
+### don't make use of references internally, the developer doesn't need to
+### take any special action for that because it's automatically taken care of
+### by R itself. However, for objects that do make use of references internally
+### (e.g. environments, external pointers, pointer to a file on disk, etc...),
+### the developer needs to be careful to implement endomorphisms with
+### copy-on-change semantics. This can easily be achieved (and is what the
+### default methods for Assays objects do) by performaing a full (deep) copy
+### of the object before modifying it instead of trying to modify it in-place.
+### Note that the full (deep) copy is not always necessary in order to achieve
+### copy-on-change semantics: it's enough (and often preferrable for
+### performance reasons) to copy only the parts of the objects that need to
+### be modified.
+###
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Assays class
+###
+
+setClass("Assays")
+
+### Validity
+
+.valid.Assays <- function(x)
+{
+ assays <- as(x, "SimpleList", strict=FALSE)
+ if (!is(assays, "SimpleList"))
+ return("'assays' must be a SimpleList object")
+ if (length(assays) == 0L)
+ return(NULL)
+
+ ## Check dims.
+ all_dims <- sapply(assays, function(assay) dim(assay)[1:2])
+ if (any(is.na(all_dims)))
+ return(wmsg("all assays must be matrix-like objects ",
+ "with 2 (or more?) dimensions"))
+ if (!all(all_dims == all_dims[ , 1L]))
+ stop("all assays must have the same nrow and ncol")
+
+ NULL
+}
+
+setValidity2("Assays", .valid.Assays)
+
+### Constructor
+
+.normarg_assays <- function(assays)
+{
+ if (!is(assays, "SimpleList")) {
+ if (is.list(assays) || is.array(assays)) {
+ assays <- SimpleList(assays)
+ } else {
+ stop("'assays' must be a SimpleList, list or array")
+ }
+ }
+ assays
+}
+
+Assays <- function(assays=SimpleList())
+{
+ assays <- .normarg_assays(assays)
+ #ans <- as(assays, "SimpleListAssays")
+ ans <- as(assays, "ShallowSimpleListAssays")
+ #ans <- as(assays, "AssaysInEnv") # a *broken* alternative
+ validObject(ans)
+ ans
+}
+
+### Accessors
+
+.SL_get_length <- selectMethod("length", "SimpleList")
+setMethod("length", "Assays",
+ function(x)
+ {
+ assays <- as(x, "SimpleList", strict=FALSE)
+ .SL_get_length(assays)
+ }
+)
+
+.SL_get_names <- selectMethod("names", "SimpleList")
+setMethod("names", "Assays",
+ function(x)
+ {
+ assays <- as(x, "SimpleList", strict=FALSE)
+ .SL_get_names(assays)
+ }
+)
+
+.SL_set_names <- selectMethod("names<-", "SimpleList")
+setReplaceMethod("names", "Assays",
+ function(x, value)
+ {
+ assays <- as(x, "SimpleList", strict=FALSE)
+ assays <- .SL_set_names(assays, value)
+ as(assays, class(x))
+ }
+)
+
+setMethod("[[", "Assays",
+ function(x, i, j, ...)
+ {
+ assays <- as(x, "SimpleList", strict=FALSE)
+ getListElement(assays, i)
+ }
+)
+
+setReplaceMethod("[[", "Assays",
+ function(x, i, j, ..., value)
+ {
+ assays <- as(x, "SimpleList", strict=FALSE)
+ assays <- setListElement(assays, i, value)
+ ans <- as(assays, class(x))
+ validObject(ans)
+ ans
+ }
+)
+
+setMethod("dim", "Assays",
+ function(x)
+ {
+ if (length(x) == 0L)
+ return(c(0L, 0L))
+ dim(x[[1L]])
+ }
+)
+
+### 2D-Subsetting
+
+.extract_Assays_subset <- function(x, i, j)
+{
+ ## need to expand Rle's for subsetting standard matrix
+ if (!missing(i) && !missing(j)) {
+ fun <- function(x) {
+ switch(length(dim(x)),
+ stop("'[' on assays() with 1 dimension not supported"),
+ x[i, j, drop=FALSE],
+ x[i, j, , drop=FALSE],
+ x[i, j, , , drop=FALSE],
+ stop("'[' on assays() with >4 dimensions not supported"))
+ }
+ } else if (!missing(i)) {
+ fun <- function(x) {
+ switch(length(dim(x)),
+ stop("'[' on assays() with 1 dimension not supported"),
+ x[i, , drop=FALSE],
+ x[i, , , drop=FALSE],
+ x[i, , , , drop=FALSE],
+ stop("'[' on assays() with >4 dimensions not supported"))
+ }
+ } else if (!missing(j)) {
+ fun <- function(x) {
+ switch(length(dim(x)),
+ stop("'[' on assays() with 1 dimension not supported"),
+ x[, j, drop=FALSE],
+ x[, j, , drop=FALSE],
+ x[, j, , , drop=FALSE],
+ stop("'[' on assays() with >4 dimensions not supported"))
+ }
+ }
+ assays <- as(x, "SimpleList", strict=FALSE)
+ as(endoapply(assays, fun), class(x))
+}
+
+setMethod("[", "Assays",
+ function(x, i, j, ..., drop=TRUE) .extract_Assays_subset(x, i, j)
+)
+
+.replace_Assays_subset <- function(x, i, j, value)
+{
+ ## need to expand Rle's for subsetting standard matrix
+ if (!missing(i) && !missing(j)) {
+ fun <- function(x, value) {
+ switch(length(dim(x)),
+ stop("'[<-' on assays() with 1 dimension not supported"),
+ x[i, j] <- value,
+ x[i, j, ] <- value,
+ x[i, j, , ] <- value,
+ stop("'[<-' on assays() with >4 dimensions not supported"))
+ x
+ }
+ } else if (!missing(i)) {
+ fun <- function(x, value) {
+ switch(length(dim(x)),
+ stop("'[<-' on assays() with 1 dimension not supported"),
+ x[i, ] <- value,
+ x[i, , ] <- value,
+ x[i, , , ] <- value,
+ stop("'[<-' on assays() with >4 dimensions not supported"))
+ x
+ }
+ } else if (!missing(j)) {
+ fun <- function(x, value) {
+ switch(length(dim(x)),
+ stop("'[<-' on assays() with 1 dimension not supported"),
+ x[, j] <- value,
+ x[, j, ] <- value,
+ x[, j, , ] <- value,
+ stop("'[<-' on assays() with >4 dimensions not supported"))
+ x
+ }
+ }
+ a <- as(x, "SimpleList", strict=FALSE)
+ v <- as(value, "SimpleList", strict=FALSE)
+ as(mendoapply(fun, x=a, value=v), class(x))
+}
+
+setReplaceMethod("[", "Assays",
+ function(x, i, j, ..., value) .replace_Assays_subset(x, i, j, value)
+)
+
+### rbind/cbind
+
+.bind_Assays <- function(lst, bind)
+{
+ if (length(lst) == 0L)
+ return(Assays())
+ lens <- sapply(lst, length)
+ if (length(unique(lens)) != 1)
+ stop("assays must have the same length")
+ len1 <- lens[1L]
+ if (len1 == 0L)
+ return(Assays())
+ var <- lapply(lst, names)
+ uvar <- unique(unlist(var))
+ if (is.null(uvar)) {
+ ## no names, match by position
+ res <- lapply(seq_len(len1), function(index) {
+ e1 <- lapply(lst, "[[", index)
+ do.call(bind, e1)
+ })
+ } else {
+ ## match by name
+ ok <- all(vapply(var, function(x, y) identical(sort(x), y),
+ logical(1), sort(uvar)))
+ if (!ok)
+ stop("assays must have the same names()")
+ res <- lapply(uvar, function(index) {
+ e1 <- lapply(lst, "[[", index)
+ do.call(bind, e1)
+ })
+ names(res) <- uvar
+ }
+ as(SimpleList(res), class(lst[[1L]]))
+}
+
+setMethod("rbind", "Assays",
+ function(..., deparse.level=1) .bind_Assays(unname(list(...)), arbind)
+)
+
+setMethod("cbind", "Assays",
+ function(..., deparse.level=1) .bind_Assays(unname(list(...)), acbind)
+)
+
+### Having "arbind" and "acbind" methods for Matrix objects will make rbind()
+### and cbind() work on Assays objects with Matrix list elements.
+### Maybe these methods should be defined next to the arbind() and acbind()
+### generics (which are defined in the IRanges package) but that would require
+### to make IRanges depend on the Matrix package.
+setMethod("arbind", "Matrix", function(...) rbind(...))
+setMethod("acbind", "Matrix", function(...) cbind(...))
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### SimpleListAssays class
+###
+
+### The order of inheritance is important: first Assays, then SimpleList!
+setClass("SimpleListAssays", contains=c("Assays", "SimpleList"))
+
+### Lossless back and forth coercion from/to SimpleList are automatically
+### taken care of by automatic methods defined by the methods package.
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### ShallowSimpleListAssays class
+###
+### We implement the REQUIRED coercions only.
+###
+
+.ShallowData <- setRefClass("ShallowData",
+ fields = list( data = "ANY" ))
+
+.ShallowSimpleListAssays0 <- setRefClass("ShallowSimpleListAssays",
+ fields = list( data = "SimpleList" ),
+ contains = c("ShallowData", "Assays"))
+
+setAs("SimpleList", "ShallowSimpleListAssays",
+ function(from) .ShallowSimpleListAssays0(data=from)
+)
+
+setAs("ShallowSimpleListAssays", "SimpleList", function(from) from$data)
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### AssaysInEnv class
+###
+### A *broken* alternative to ShallowSimpleListAssays that does NOT respect
+### the copy-on-change contract (only provided for illustration purposes).
+###
+### We implement the REQUIRED coercions plus OPTIONAL methods: length, names,
+### names<-, [[, and [[<-.
+###
+
+setClass("AssaysInEnv",
+ contains="Assays",
+ representation(envir="environment")
+)
+
+.NAMES_SYMBOL <- ".names" # must begin with a . so is ommitted by ls()
+
+setMethod("length", "AssaysInEnv", function(x) length(x at envir) - 1L)
+
+setMethod("names", "AssaysInEnv", function(x) x at envir[[.NAMES_SYMBOL]])
+
+### Does NOT respect the copy-on-change contract!
+setReplaceMethod("names", "AssaysInEnv",
+ function(x, value)
+ {
+ value <- S4Vectors:::normalize_names_replacement_value(value, x)
+ x at envir[[.NAMES_SYMBOL]] <- value
+ x
+ }
+)
+
+setMethod("[[", "AssaysInEnv",
+ function(x, i, j, ...)
+ {
+ key <- setNames(ls(x at envir, sorted=TRUE), names(x))[[i]]
+ get(key, envir=x at envir)
+ }
+)
+
+### Does NOT respect the copy-on-change contract!
+setReplaceMethod("[[", "AssaysInEnv",
+ function(x, i, j, ..., value)
+ {
+ key <- setNames(ls(x at envir, sorted=TRUE), names(x))[[i]]
+ assign(key, value, envir=x at envir)
+ x
+ }
+)
+
+setAs("SimpleList", "AssaysInEnv",
+ function(from)
+ {
+ from <- as.list(from)
+ from_names <- names(from)
+ keys <- paste(sprintf("%09d", seq_along(from)), from_names, sep=":")
+ names(from) <- keys
+ envir <- list2env(from, parent=emptyenv())
+ envir[[.NAMES_SYMBOL]] <- from_names
+ new("AssaysInEnv", envir=envir)
+ }
+)
+
+setAs("AssaysInEnv", "SimpleList",
+ function(from)
+ SimpleList(setNames(as.list(from at envir, sorted=TRUE), names(from)))
+)
+
diff --git a/R/RangedSummarizedExperiment-class.R b/R/RangedSummarizedExperiment-class.R
new file mode 100644
index 0000000..9e7945f
--- /dev/null
+++ b/R/RangedSummarizedExperiment-class.R
@@ -0,0 +1,572 @@
+### =========================================================================
+### RangedSummarizedExperiment objects
+### -------------------------------------------------------------------------
+###
+
+
+### The 'elementMetadata' slot must contain a zero-column DataFrame at all time
+### (this is checked by the validity method). The top-level mcols are stored on
+### the rowRanges component.
+setClass("RangedSummarizedExperiment",
+ contains="SummarizedExperiment",
+ representation(
+ rowRanges="GenomicRangesORGRangesList"
+ ),
+ prototype(
+ rowRanges=GRanges()
+ )
+)
+
+### Combine the new parallel slots with those of the parent class. Make sure
+### to put the new parallel slots *first*.
+setMethod("parallelSlotNames", "RangedSummarizedExperiment",
+ function(x) c("rowRanges", callNextMethod())
+)
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Validity.
+###
+
+### The names and mcols of a RangedSummarizedExperiment must be set on its
+### rowRanges slot, not in its NAMES and elementMetadata slots!
+.valid.RangedSummarizedExperiment <- function(x)
+{
+ if (!is.null(x at NAMES))
+ return("'NAMES' slot must be set to NULL at all time")
+ if (ncol(x at elementMetadata) != 0L)
+ return(wmsg("'elementMetadata' slot must contain a zero-column ",
+ "DataFrame at all time"))
+ rowRanges_len <- length(x at rowRanges)
+ x_nrow <- length(x)
+ if (rowRanges_len != x_nrow) {
+ txt <- sprintf(
+ "\n length of 'rowRanges' (%d) must equal nb of rows in 'x' (%d)",
+ rowRanges_len, x_nrow)
+ return(txt)
+ }
+ NULL
+}
+
+setValidity2("RangedSummarizedExperiment", .valid.RangedSummarizedExperiment)
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Constructor.
+###
+
+.new_RangedSummarizedExperiment <- function(assays, rowRanges, colData,
+ metadata)
+{
+ elementMetadata <- S4Vectors:::make_zero_col_DataFrame(length(rowRanges))
+ if (!is(assays, "Assays"))
+ assays <- Assays(assays)
+ new("RangedSummarizedExperiment", rowRanges=rowRanges,
+ colData=colData,
+ assays=assays,
+ elementMetadata=elementMetadata,
+ metadata=as.list(metadata))
+}
+
+.get_colnames_from_assays <- function(assays)
+{
+ if (length(assays) == 0L)
+ return(NULL)
+ colnames(assays[[1L]])
+}
+
+.get_rownames_from_assays <- function(assays)
+{
+ if (length(assays) == 0L)
+ return(NULL)
+ rownames(assays[[1L]])
+}
+
+setGeneric("SummarizedExperiment",
+ function(assays, ...) standardGeneric("SummarizedExperiment"))
+
+setMethod("SummarizedExperiment", "SimpleList",
+ function(assays, rowData=NULL, rowRanges=GRangesList(), colData=DataFrame(),
+ metadata=list())
+{
+ if (missing(colData) && 0L != length(assays)) {
+ assay <- assays[[1]]
+ nms <- colnames(assay)
+ colData <- DataFrame(x=seq_len(ncol(assay)), row.names=nms)[, FALSE]
+ } else if (!missing(colData)) {
+ colData <- as(colData, "DataFrame")
+ if (is.null(rownames(colData)))
+ rownames(colData) <- .get_colnames_from_assays(assays)
+ }
+ ans_colnames <- rownames(colData)
+
+ if (is.null(rowData)) {
+ if (missing(rowRanges)) {
+ ans_rownames <- .get_rownames_from_assays(assays)
+ } else {
+ if (is.null(names(rowRanges)))
+ names(rowRanges) <- .get_rownames_from_assays(assays)
+ ans_rownames <- names(rowRanges)
+ }
+ } else {
+ if (!missing(rowRanges))
+ stop("only one of 'rowData' and 'rowRanges' can be specified")
+ if (is(rowData, "GenomicRangesORGRangesList")) {
+ rowRanges <- rowData
+ if (is.null(names(rowRanges)))
+ names(rowRanges) <- .get_rownames_from_assays(assays)
+ ans_rownames <- names(rowRanges)
+ } else {
+ rowData <- as(rowData, "DataFrame")
+ ans_rownames <- rownames(rowData)
+ if (is.null(ans_rownames))
+ ans_rownames <- .get_rownames_from_assays(assays)
+ }
+ }
+
+ ## validate
+ ok <- vapply(assays, function(x) {
+ colnames <- colnames(x)
+ test <- is.null(colnames) || identical(colnames, ans_colnames)
+ if (!test)
+ stop("assay colnames() must be NULL or equal colData rownames()")
+
+ rownames <- rownames(x)
+ test <- test &&
+ is.null(rownames) || identical(rownames, ans_rownames)
+ if (!test) {
+ txt <- "assay rownames() must be NULL or equal rowData rownames() /
+ rowRanges names()"
+ stop(paste(strwrap(txt, exdent=2), collapse="\n"))
+ }
+
+ test
+ }, logical(1))
+
+ assays <- Assays(assays)
+
+ if (missing(rowRanges) && !is(rowData, "GenomicRangesORGRangesList")) {
+ new_SummarizedExperiment(assays, ans_rownames, rowData, colData,
+ metadata)
+ } else {
+ .new_RangedSummarizedExperiment(assays, rowRanges, colData, metadata)
+ }
+})
+
+setMethod("SummarizedExperiment", "ANY",
+ function(assays, ...)
+{
+ if (is.matrix(assays) && is.list(assays))
+ ## special case -- matrix of lists
+ assays <- list(assays)
+ SummarizedExperiment(SimpleList(assays), ...)
+})
+
+setMethod("SummarizedExperiment", "list",
+ function(assays, ...)
+{
+ SummarizedExperiment(do.call(SimpleList, assays), ...)
+})
+
+setMethod("SummarizedExperiment", "missing",
+ function(assays, ...)
+{
+ SummarizedExperiment(SimpleList(), ...)
+})
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Coercion.
+###
+### See makeSummarizedExperimentFromExpressionSet.R for coercion back and
+### forth between SummarizedExperiment and ExpressionSet.
+###
+
+.from_RangedSummarizedExperiment_to_SummarizedExperiment <- function(from)
+{
+ new_SummarizedExperiment(from at assays,
+ names(from at rowRanges),
+ mcols(from at rowRanges),
+ from at colData,
+ from at metadata)
+}
+
+setAs("RangedSummarizedExperiment", "SummarizedExperiment",
+ .from_RangedSummarizedExperiment_to_SummarizedExperiment
+)
+
+.from_SummarizedExperiment_to_RangedSummarizedExperiment <- function(from)
+{
+ partitioning <- PartitioningByEnd(integer(length(from)), names=names(from))
+ rowRanges <- relist(GRanges(), partitioning)
+ .new_RangedSummarizedExperiment(from at assays,
+ rowRanges,
+ from at colData,
+ from at metadata)
+}
+
+setAs("SummarizedExperiment", "RangedSummarizedExperiment",
+ .from_SummarizedExperiment_to_RangedSummarizedExperiment
+)
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Getters and setters.
+###
+
+### The rowRanges() generic is defined in the DelayedArray package.
+setMethod("rowRanges", "RangedSummarizedExperiment",
+ function(x, ...) x at rowRanges
+)
+
+setGeneric("rowRanges<-",
+ function(x, ..., value) standardGeneric("rowRanges<-"))
+
+.SummarizedExperiment.rowRanges.replace <-
+ function(x, ..., value)
+{
+ if (!is(x, "RangedSummarizedExperiment"))
+ x <- as(x, "RangedSummarizedExperiment")
+ x <- BiocGenerics:::replaceSlots(x, ...,
+ rowRanges=value,
+ elementMetadata=S4Vectors:::make_zero_col_DataFrame(length(value)),
+ check=FALSE)
+ msg <- .valid.SummarizedExperiment.assays_nrow(x)
+ if (!is.null(msg))
+ stop(msg)
+ x
+}
+
+setReplaceMethod("rowRanges", c("SummarizedExperiment", "GenomicRanges"),
+ .SummarizedExperiment.rowRanges.replace)
+
+setReplaceMethod("rowRanges", c("SummarizedExperiment", "GRangesList"),
+ .SummarizedExperiment.rowRanges.replace)
+
+setMethod("names", "RangedSummarizedExperiment",
+ function(x) names(rowRanges(x))
+)
+
+setReplaceMethod("names", "RangedSummarizedExperiment",
+ function(x, value)
+{
+ rowRanges <- rowRanges(x)
+ names(rowRanges) <- value
+ BiocGenerics:::replaceSlots(x, rowRanges=rowRanges, check=FALSE)
+})
+
+setMethod("dimnames", "RangedSummarizedExperiment",
+ function(x)
+{
+ list(names(x), rownames(colData(x)))
+})
+
+setReplaceMethod("dimnames", c("RangedSummarizedExperiment", "list"),
+ function(x, value)
+{
+ rowRanges <- rowRanges(x)
+ names(rowRanges) <- value[[1]]
+ colData <- colData(x)
+ rownames(colData) <- value[[2]]
+ BiocGenerics:::replaceSlots(x,
+ rowRanges=rowRanges,
+ colData=colData,
+ check=FALSE)
+})
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Subsetting.
+###
+
+.DollarNames.RangedSummarizedExperiment <-
+ .DollarNames.SummarizedExperiment
+
+setMethod("subset", "RangedSummarizedExperiment",
+ function(x, subset, select, ...)
+{
+ i <- S4Vectors:::evalqForSubset(subset, rowRanges(x), ...)
+ j <- S4Vectors:::evalqForSubset(select, colData(x), ...)
+ x[i, j]
+})
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+## colData-as-GRanges compatibility: allow direct access to GRanges /
+## GRangesList colData for select functions
+
+## Not supported:
+##
+## Not consistent SummarizedExperiment structure: length, names,
+## as.data.frame, c.
+## Length-changing endomorphisms: disjoin, gaps, reduce, unique.
+## 'legacy' data types / functions: as "RangedData", as "RangesList",
+## renameSeqlevels, keepSeqlevels.
+## Possile to implement, but not yet: Ops, map, window, window<-
+
+## mcols
+setMethod("mcols", "RangedSummarizedExperiment",
+ function(x, use.names=FALSE, ...)
+{
+ mcols(rowRanges(x), use.names=use.names, ...)
+})
+
+setReplaceMethod("mcols", "RangedSummarizedExperiment",
+ function(x, ..., value)
+{
+ BiocGenerics:::replaceSlots(x,
+ rowRanges=local({
+ r <- rowRanges(x)
+ mcols(r) <- value
+ r
+ }),
+ check=FALSE)
+})
+
+### mcols() is the recommended way for accessing the metadata columns.
+### Use of values() or elementMetadata() is discouraged.
+
+setMethod("elementMetadata", "RangedSummarizedExperiment",
+ function(x, use.names=FALSE, ...)
+{
+ elementMetadata(rowRanges(x), use.names=use.names, ...)
+})
+
+setReplaceMethod("elementMetadata", "RangedSummarizedExperiment",
+ function(x, ..., value)
+{
+ elementMetadata(rowRanges(x), ...) <- value
+ x
+})
+
+## Single dispatch, generic signature fun(x, ...)
+local({
+ .funs <-
+ c("duplicated", "end", "end<-", "ranges", "seqinfo", "seqnames",
+ "start", "start<-", "strand", "width", "width<-")
+
+ endomorphisms <- .funs[grepl("<-$", .funs)]
+
+ tmpl <- function() {}
+ environment(tmpl) <- parent.frame(2)
+ for (.fun in .funs) {
+ generic <- getGeneric(.fun)
+ formals(tmpl) <- formals(generic)
+ fmls <- as.list(formals(tmpl))
+ fmls[] <- sapply(names(fmls), as.symbol)
+ fmls[[generic at signature]] <- quote(rowRanges(x))
+ if (.fun %in% endomorphisms)
+ body(tmpl) <- substitute({
+ rowRanges(x) <- do.call(FUN, ARGS)
+ x
+ }, list(FUN=.fun, ARGS=fmls))
+ else
+ body(tmpl) <-
+ substitute(do.call(FUN, ARGS),
+ list(FUN=as.symbol(.fun), ARGS=fmls))
+ setMethod(.fun, "RangedSummarizedExperiment", tmpl)
+ }
+})
+
+setMethod("granges", "RangedSummarizedExperiment",
+ function(x, use.mcols=FALSE, ...)
+{
+ if (!identical(use.mcols, FALSE))
+ stop("\"granges\" method for RangedSummarizedExperiment objects ",
+ "does not support the 'use.mcols' argument")
+ rowRanges(x)
+})
+
+## 2-argument dispatch:
+## pcompare / Compare
+##
+.RangedSummarizedExperiment.pcompare <-
+ function(x, y)
+{
+ if (is(x, "RangedSummarizedExperiment"))
+ x <- rowRanges(x)
+ if (is(y, "RangedSummarizedExperiment"))
+ y <- rowRanges(y)
+ pcompare(x, y)
+}
+
+.RangedSummarizedExperiment.Compare <-
+ function(e1, e2)
+{
+ if (is(e1, "RangedSummarizedExperiment"))
+ e1 <- rowRanges(e1)
+ if (is(e2, "RangedSummarizedExperiment"))
+ e2 <- rowRanges(e2)
+ callGeneric(e1=e1, e2=e2)
+}
+
+local({
+ .signatures <- list(
+ c("RangedSummarizedExperiment", "ANY"),
+ c("ANY", "RangedSummarizedExperiment"),
+ c("RangedSummarizedExperiment", "RangedSummarizedExperiment"))
+
+ for (.sig in .signatures) {
+ setMethod("pcompare", .sig, .RangedSummarizedExperiment.pcompare)
+ setMethod("Compare", .sig, .RangedSummarizedExperiment.Compare)
+ }
+})
+
+## additional getters / setters
+
+setReplaceMethod("strand", "RangedSummarizedExperiment",
+ function(x, ..., value)
+{
+ strand(rowRanges(x)) <- value
+ x
+})
+
+setReplaceMethod("ranges", "RangedSummarizedExperiment",
+ function(x, ..., value)
+{
+ ranges(rowRanges(x)) <- value
+ x
+})
+
+## order, rank, sort
+
+setMethod("is.unsorted", "RangedSummarizedExperiment",
+ function(x, na.rm = FALSE, strictly = FALSE, ignore.strand = FALSE)
+{
+ x <- rowRanges(x)
+ if (!is(x, "GenomicRanges"))
+ stop("is.unsorted() is not yet supported when 'rowRanges(x)' is a ",
+ class(x), " object")
+ callGeneric()
+})
+
+setMethod("order", "RangedSummarizedExperiment",
+ function(..., na.last=TRUE, decreasing=FALSE,
+ method=c("auto", "shell", "radix"))
+{
+ args <- lapply(list(...), rowRanges)
+ do.call("order", c(args, list(na.last=na.last,
+ decreasing=decreasing,
+ method=method)))
+})
+
+setMethod("rank", "RangedSummarizedExperiment",
+ function(x, na.last = TRUE,
+ ties.method = c("average", "first", "last", "random", "max", "min"))
+{
+ ties.method <- match.arg(ties.method)
+ rank(rowRanges(x), na.last=na.last, ties.method=ties.method)
+})
+
+setMethod("sort", "RangedSummarizedExperiment",
+ function(x, decreasing = FALSE, ignore.strand = FALSE)
+{
+ x_rowRanges <- rowRanges(x)
+ if (!is(x_rowRanges, "GenomicRanges"))
+ stop("sort() is not yet supported when 'rowRanges(x)' is a ",
+ class(x_rowRanges), " object")
+ oo <- GenomicRanges:::order_GenomicRanges(x_rowRanges,
+ decreasing = decreasing,
+ ignore.strand = ignore.strand)
+ x[oo]
+})
+
+## seqinfo (also seqlevels, genome, seqlevels<-, genome<-), seqinfo<-
+
+setMethod("seqinfo", "RangedSummarizedExperiment",
+ function(x)
+{
+ seqinfo(x at rowRanges)
+})
+
+setReplaceMethod("seqinfo", "RangedSummarizedExperiment",
+ function (x, new2old= NULL, force=FALSE,
+ pruning.mode=c("error", "coarse", "fine", "tidy"),
+ value)
+{
+ if (!is(value, "Seqinfo"))
+ stop("the supplied 'seqinfo' must be a Seqinfo object")
+ dangling_seqlevels <-
+ GenomeInfoDb:::getDanglingSeqlevels(x at rowRanges, new2old=new2old,
+ force=force,
+ pruning.mode=pruning.mode,
+ seqlevels(value))
+ if (length(dangling_seqlevels) != 0L)
+ x <- x[!(seqnames(x) %in% dangling_seqlevels)]
+ x at rowRanges <-
+ update(x at rowRanges,
+ seqnames = GenomeInfoDb:::makeNewSeqnames(x, new2old,
+ seqlevels(value)),
+ seqinfo = value)
+ if (is.character(msg <- .valid.RangedSummarizedExperiment(x)))
+ stop(msg)
+ x
+})
+
+setMethod("split", "RangedSummarizedExperiment",
+ function(x, f, drop=FALSE, ...)
+{
+ splitAsList(x, f, drop=drop)
+})
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### updateObject()
+###
+
+.old_SummarizedExperiment_slots <- c(
+ "assays",
+ "rowData",
+ "colData",
+ "exptData"
+)
+
+.has_old_SummarizedExperiment_internal_structure <- function(object)
+ all(sapply(.old_SummarizedExperiment_slots, .hasSlot, object=object))
+
+.from_old_SummarizedExperiment_to_RangedSummarizedExperiment <- function(from)
+ .new_RangedSummarizedExperiment(from at assays,
+ from at rowData,
+ from at colData,
+ from at exptData)
+
+.update_old_SummarizedExperiment <- function(object, ..., verbose=FALSE)
+{
+ if (.has_old_SummarizedExperiment_internal_structure(object)) {
+ rse <- .from_old_SummarizedExperiment_to_RangedSummarizedExperiment(object)
+ } else if (!(.hasSlot(object, "NAMES") &&
+ .hasSlot(object, "elementMetadata"))) {
+ rse <- .new_RangedSummarizedExperiment(object at assays,
+ object at rowRanges,
+ object at colData,
+ object at metadata)
+ } else {
+ return(object)
+ }
+
+ if (!(extends(class(object), "RangedSummarizedExperiment") &&
+ class(object) != "RangedSummarizedExperiment"))
+ return(rse)
+
+ xslotnames <- setdiff(slotNames(class(object)), slotNames(class(rse)))
+ xslots <- attributes(object)[xslotnames]
+ #do.call("new", c(list(Class=class(object), rse), xslots))
+
+ ## The line above doesn't work because of a bug in R (see
+ ## https://stat.ethz.ch/pipermail/r-devel/2015-May/071130.html),
+ ## so we use the workaround below.
+ rse_slots <- attributes(rse)[slotNames(class(rse))]
+
+ ## Because of another bug in R, rse_slots$NAMES is broken when the NAMES
+ ## slot is NULL so we repair it (see
+ ## https://bugs.r-project.org/bugzilla/show_bug.cgi?id=16428).
+ if (is.name(rse_slots$NAMES))
+ rse_slots$NAMES <- NULL
+
+ do.call("new", c(list(Class=class(object)), rse_slots, xslots))
+}
+
+setMethod("updateObject", "SummarizedExperiment",
+ .update_old_SummarizedExperiment
+)
+
diff --git a/R/SummarizedExperiment-class.R b/R/SummarizedExperiment-class.R
new file mode 100644
index 0000000..4fbeb0a
--- /dev/null
+++ b/R/SummarizedExperiment-class.R
@@ -0,0 +1,844 @@
+### =========================================================================
+### SummarizedExperiment objects
+### -------------------------------------------------------------------------
+###
+
+
+setClass("SummarizedExperiment",
+ contains="Vector",
+ representation(
+ colData="DataFrame", # columns and their annotations
+ assays="Assays", # Data -- e.g., list of matricies
+ NAMES="character_OR_NULL",
+ elementMetadata="DataFrame"
+ ),
+ prototype(
+ assays=Assays()
+ )
+)
+
+### Combine the new parallel slots with those of the parent class. Make sure
+### to put the new parallel slots *first*.
+setMethod("parallelSlotNames", "SummarizedExperiment",
+ function(x) c("NAMES", callNextMethod())
+)
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Validity.
+###
+
+.valid.SummarizedExperiment.assays_nrow <- function(x)
+{
+ if (length(x at assays) == 0L)
+ return(NULL)
+ assays_nrow <- nrow(x at assays)
+ rowData_nrow <- length(x)
+ if (assays_nrow != rowData_nrow) {
+ txt <- sprintf(
+ "\n nb of rows in 'assay' (%d) must equal nb of rows in 'rowData' (%d)",
+ assays_nrow, rowData_nrow)
+ return(txt)
+ }
+ NULL
+}
+
+.valid.SummarizedExperiment.assays_ncol <- function(x)
+{
+ if (length(x at assays) == 0L)
+ return(NULL)
+ assays_ncol <- ncol(x at assays)
+ colData_nrow <- nrow(colData(x))
+ if (assays_ncol != colData_nrow) {
+ txt <- sprintf(
+ "\n nb of cols in 'assay' (%d) must equal nb of rows in 'colData' (%d)",
+ assays_ncol, colData_nrow)
+ return(txt)
+ }
+ NULL
+}
+
+.valid.SummarizedExperiment.assays_dim <- function(x)
+{
+ c(.valid.SummarizedExperiment.assays_nrow(x),
+ .valid.SummarizedExperiment.assays_ncol(x))
+}
+
+.valid.SummarizedExperiment <- function(x)
+{
+ .valid.SummarizedExperiment.assays_dim(x)
+}
+
+setValidity2("SummarizedExperiment", .valid.SummarizedExperiment)
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Low-level constructor (not exported).
+###
+
+new_SummarizedExperiment <- function(assays, names, rowData, colData,
+ metadata)
+{
+ if (!is(assays, "Assays"))
+ assays <- Assays(assays)
+ if (is.null(rowData)) {
+ if (is.null(names))
+ nrow <- nrow(assays)
+ else
+ nrow <- length(names)
+ rowData <- S4Vectors:::make_zero_col_DataFrame(nrow)
+ } else {
+ rownames(rowData) <- NULL
+ }
+ new("SummarizedExperiment", NAMES=names,
+ elementMetadata=rowData,
+ colData=colData,
+ assays=assays,
+ metadata=as.list(metadata))
+}
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Getters and setters.
+###
+
+setMethod("length", "SummarizedExperiment",
+ function(x) nrow(x at elementMetadata)
+)
+
+setMethod("names", "SummarizedExperiment", function(x) x at NAMES)
+
+setReplaceMethod("names", "SummarizedExperiment",
+ function(x, value)
+ {
+ NAMES <- S4Vectors:::normalize_names_replacement_value(value, x)
+ BiocGenerics:::replaceSlots(x, NAMES=NAMES, check=FALSE)
+ }
+)
+
+## rowData, colData seem too vague, but from eSet derived classes wanted to
+## call the rows / cols something different from 'features' or 'samples', so
+## might as well avoid the issue
+
+setGeneric("rowData", function(x, ...) standardGeneric("rowData"))
+
+setMethod("rowData", "SummarizedExperiment",
+ function(x, ...) mcols(x, ...)
+)
+
+setGeneric("rowData<-",
+ function(x, ..., value) standardGeneric("rowData<-"))
+
+setReplaceMethod("rowData", "SummarizedExperiment",
+ function(x, ..., value) `mcols<-`(x, ..., value=value)
+)
+
+setGeneric("colData", function(x, ...) standardGeneric("colData"))
+
+setMethod("colData", "SummarizedExperiment", function(x, ...) x at colData)
+
+setGeneric("colData<-",
+ function(x, ..., value) standardGeneric("colData<-"))
+
+setReplaceMethod("colData", c("SummarizedExperiment", "DataFrame"),
+ function(x, ..., value)
+{
+ if (nrow(value) != ncol(x))
+ stop("nrow of supplied 'colData' must equal ncol of object")
+ BiocGenerics:::replaceSlots(x, colData=value, check=FALSE)
+})
+
+setGeneric("assays",
+ function(x, ..., withDimnames=TRUE) standardGeneric("assays"),
+ signature="x")
+
+setMethod("assays", "SummarizedExperiment",
+ function(x, ..., withDimnames=TRUE)
+{
+ assays <- as(x at assays, "SimpleList")
+ if (withDimnames) {
+ assays <- endoapply(assays,
+ function(assay) {
+ dimnames(assay)[1:2] <- dimnames(x)
+ assay
+ }
+ )
+ }
+ assays
+})
+
+setGeneric("assays<-",
+ function(x, ..., withDimnames=TRUE, value) standardGeneric("assays<-"),
+ signature=c("x", "value"))
+
+.SummarizedExperiment.assays.replace <-
+ function(x, ..., withDimnames=TRUE, value)
+{
+ ## withDimnames arg allows names(assays(se, withDimnames=FALSE)) <- value
+ ok <- vapply(value, function(elt, xdimnames) {
+ e <- dimnames(elt)
+ (is.null(e[[1]]) || identical(e[[1]], xdimnames[[1]])) &&
+ (is.null(e[[2]]) || identical(e[[2]], xdimnames[[2]]))
+ }, logical(1), xdimnames=dimnames(x))
+ if (!all(ok))
+ stop("current and replacement dimnames() differ")
+ x <- BiocGenerics:::replaceSlots(x, assays=Assays(value), check=FALSE)
+ ## validObject(x) should be called below because it would then fully
+ ## re-validate objects that derive from SummarizedExperiment (e.g.
+ ## DESeqDataSet objects) after the user sets the assays slot with
+ ## assays(x) <- value. For example the assays slot of a DESeqDataSet
+ ## object must contain a matrix named 'counts' and calling validObject(x)
+ ## would check that but .valid.SummarizedExperiment(x) doesn't.
+ ## The FourC() constructor function defined in the FourCSeq package
+ ## actually takes advantage of the incomplete validation below to
+ ## purposedly return invalid FourC objects!
+ msg <- .valid.SummarizedExperiment(x)
+ if (!is.null(msg))
+ stop(msg)
+ x
+}
+
+setReplaceMethod("assays", c("SummarizedExperiment", "SimpleList"),
+ .SummarizedExperiment.assays.replace)
+
+setReplaceMethod("assays", c("SummarizedExperiment", "list"),
+ .SummarizedExperiment.assays.replace)
+
+setGeneric("assay", function(x, i, ...) standardGeneric("assay"))
+
+## convenience for common use case
+setMethod("assay", c("SummarizedExperiment", "missing"),
+ function(x, i, ...)
+{
+ assays <- assays(x, ...)
+ if (0L == length(assays))
+ stop("'assay(<", class(x), ">, i=\"missing\", ...) ",
+ "length(assays(<", class(x), ">)) is 0'")
+ assays[[1]]
+})
+
+setMethod("assay", c("SummarizedExperiment", "numeric"),
+ function(x, i, ...)
+{
+ tryCatch({
+ assays(x, ...)[[i]]
+ }, error=function(err) {
+ stop("'assay(<", class(x), ">, i=\"numeric\", ...)' ",
+ "invalid subscript 'i'\n", conditionMessage(err))
+ })
+})
+
+setMethod("assay", c("SummarizedExperiment", "character"),
+ function(x, i, ...)
+{
+ msg <- paste0("'assay(<", class(x), ">, i=\"character\", ...)' ",
+ "invalid subscript 'i'")
+ res <- tryCatch({
+ assays(x, ...)[[i]]
+ }, error=function(err) {
+ stop(msg, "\n", conditionMessage(err))
+ })
+ if (is.null(res))
+ stop(msg, "\n'i' not in names(assays(<", class(x), ">))")
+ res
+})
+
+setGeneric("assay<-", signature=c("x", "i"),
+ function(x, i, ..., value) standardGeneric("assay<-"))
+
+setReplaceMethod("assay", c("SummarizedExperiment", "missing"),
+ function(x, i, ..., value)
+{
+ if (0L == length(assays(x)))
+ stop("'assay(<", class(x), ">) <- value' ", "length(assays(<",
+ class(x), ">)) is 0")
+ assays(x)[[1]] <- value
+ x
+})
+
+setReplaceMethod("assay", c("SummarizedExperiment", "numeric"),
+ function(x, i = 1, ..., value)
+{
+ assays(x, ...)[[i]] <- value
+ x
+})
+
+setReplaceMethod("assay", c("SummarizedExperiment", "character"),
+ function(x, i, ..., value)
+{
+ assays(x, ...)[[i]] <- value
+ x
+})
+
+setGeneric("assayNames", function(x, ...) standardGeneric("assayNames"))
+
+setMethod("assayNames", "SummarizedExperiment",
+ function(x, ...)
+{
+ names(assays(x, withDimnames=FALSE))
+})
+
+setGeneric("assayNames<-",
+ function(x, ..., value) standardGeneric("assayNames<-"))
+
+setReplaceMethod("assayNames", c("SummarizedExperiment", "character"),
+ function(x, ..., value)
+{
+ names(assays(x, withDimnames=FALSE)) <- value
+ x
+})
+
+## cannonical location for dim, dimnames; dimnames should be checked
+## for consistency (if non-null) and stripped from assays on
+## construction, or added from assays if row/col names are NULL in
+## <SummarizedExperiment> but not assays. dimnames need to be added on
+## to assays when assays() invoked
+setMethod("dim", "SummarizedExperiment",
+ function(x)
+{
+ c(length(x), nrow(colData(x)))
+})
+
+setMethod("dimnames", "SummarizedExperiment",
+ function(x)
+{
+ list(names(x), rownames(colData(x)))
+})
+
+setReplaceMethod("dimnames", c("SummarizedExperiment", "list"),
+ function(x, value)
+{
+ NAMES <- S4Vectors:::normalize_names_replacement_value(value[[1]], x)
+ colData <- colData(x)
+ rownames(colData) <- value[[2]]
+ BiocGenerics:::replaceSlots(x, NAMES=NAMES, colData=colData, check=FALSE)
+})
+
+setReplaceMethod("dimnames", c("SummarizedExperiment", "NULL"),
+ function(x, value)
+{
+ dimnames(x) <- list(NULL, NULL)
+ x
+})
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Subsetting.
+###
+
+.SummarizedExperiment.charbound <-
+ function(idx, txt, fmt)
+{
+ orig <- idx
+ idx <- match(idx, txt)
+ if (any(bad <- is.na(idx))) {
+ msg <- paste(S4Vectors:::selectSome(orig[bad]), collapse=" ")
+ stop(sprintf(fmt, msg))
+ }
+ idx
+}
+
+setMethod("[", c("SummarizedExperiment", "ANY", "ANY"),
+ function(x, i, j, ..., drop=TRUE)
+{
+ if (1L != length(drop) || (!missing(drop) && drop))
+ warning("'drop' ignored '[,", class(x), ",ANY,ANY-method'")
+
+ if (missing(i) && missing(j))
+ return(x)
+
+ if (!missing(i)) {
+ if (is.character(i)) {
+ fmt <- paste0("<", class(x), ">[i,] index out of bounds: %s")
+ i <- .SummarizedExperiment.charbound(i, rownames(x), fmt)
+ }
+ ii <- as.vector(i)
+ ans_elementMetadata <- x at elementMetadata[i, , drop=FALSE]
+ if (is(x, "RangedSummarizedExperiment")) {
+ ans_rowRanges <- x at rowRanges[i]
+ } else {
+ ans_NAMES <- x at NAMES[ii]
+ }
+ }
+ if (!missing(j)) {
+ if (is.character(j)) {
+ fmt <- paste0("<", class(x), ">[,j] index out of bounds: %s")
+ j <- .SummarizedExperiment.charbound(j, colnames(x), fmt)
+ }
+ ans_colData <- x at colData[j, , drop=FALSE]
+ jj <- as.vector(j)
+ }
+
+ if (missing(i)) {
+ ans_assays <- x at assays[ , jj]
+ ans <- BiocGenerics:::replaceSlots(x, ...,
+ colData=ans_colData,
+ assays=ans_assays,
+ check=FALSE)
+ } else if (missing(j)) {
+ ans_assays <- x at assays[ii, ]
+ if (is(x, "RangedSummarizedExperiment")) {
+ ans <- BiocGenerics:::replaceSlots(x, ...,
+ elementMetadata=ans_elementMetadata,
+ rowRanges=ans_rowRanges,
+ assays=ans_assays,
+ check=FALSE)
+ } else {
+ ans <- BiocGenerics:::replaceSlots(x, ...,
+ elementMetadata=ans_elementMetadata,
+ NAMES=ans_NAMES,
+ assays=ans_assays,
+ check=FALSE)
+ }
+ } else {
+ ans_assays <- x at assays[ii, jj]
+ if (is(x, "RangedSummarizedExperiment")) {
+ ans <- BiocGenerics:::replaceSlots(x, ...,
+ elementMetadata=ans_elementMetadata,
+ rowRanges=ans_rowRanges,
+ colData=ans_colData,
+ assays=ans_assays,
+ check=FALSE)
+ } else {
+ ans <- BiocGenerics:::replaceSlots(x, ...,
+ elementMetadata=ans_elementMetadata,
+ NAMES=ans_NAMES,
+ colData=ans_colData,
+ assays=ans_assays,
+ check=FALSE)
+ }
+ }
+ ans
+})
+
+setReplaceMethod("[",
+ c("SummarizedExperiment", "ANY", "ANY", "SummarizedExperiment"),
+ function(x, i, j, ..., value)
+{
+ if (missing(i) && missing(j))
+ return(value)
+
+ ans_metadata <- c(metadata(x), metadata(value))
+
+ if (!missing(i)) {
+ if (is.character(i)) {
+ fmt <- paste0("<", class(x), ">[i,] index out of bounds: %s")
+ i <- .SummarizedExperiment.charbound(i, rownames(x), fmt)
+ }
+ ii <- as.vector(i)
+ ans_elementMetadata <- local({
+ emd <- x at elementMetadata
+ emd[i,] <- value at elementMetadata
+ emd
+ })
+ if (is(x, "RangedSummarizedExperiment")) {
+ ans_rowRanges <- local({
+ r <- x at rowRanges
+ r[i] <- value at rowRanges
+ names(r)[ii] <- names(value at rowRanges)
+ r
+ })
+ } else {
+ ans_NAMES <- local({
+ nms <- x at NAMES
+ nms[ii] <- value at NAMES
+ nms
+ })
+ }
+ }
+
+ if (!missing(j)) {
+ if (is.character(j)) {
+ fmt <- paste0("<", class(x), ">[,j] index out of bounds: %s")
+ j <- .SummarizedExperiment.charbound(j, colnames(x), fmt)
+ }
+ jj <- as.vector(j)
+ ans_colData <- local({
+ c <- x at colData
+ c[j,] <- value at colData
+ rownames(c)[jj] <- rownames(value at colData)
+ c
+ })
+ }
+
+ if (missing(i)) {
+ ans_assays <- local({
+ a <- x at assays
+ a[ , jj] <- value at assays
+ a
+ })
+ ans <- BiocGenerics:::replaceSlots(x, ...,
+ metadata=ans_metadata,
+ colData=ans_colData,
+ assays=ans_assays,
+ check=FALSE)
+ msg <- .valid.SummarizedExperiment.assays_ncol(ans)
+ } else if (missing(j)) {
+ ans_assays <- local({
+ a <- x at assays
+ a[ii, ] <- value at assays
+ a
+ })
+ if (is(x, "RangedSummarizedExperiment")) {
+ ans <- BiocGenerics:::replaceSlots(x, ...,
+ metadata=ans_metadata,
+ elementMetadata=ans_elementMetadata,
+ rowRanges=ans_rowRanges,
+ assays=ans_assays,
+ check=FALSE)
+ } else {
+ ans <- BiocGenerics:::replaceSlots(x, ...,
+ metadata=ans_metadata,
+ elementMetadata=ans_elementMetadata,
+ NAMES=ans_NAMES,
+ assays=ans_assays,
+ check=FALSE)
+ }
+ msg <- .valid.SummarizedExperiment.assays_nrow(ans)
+ } else {
+ ans_assays <- local({
+ a <- x at assays
+ a[ii, jj] <- value at assays
+ a
+ })
+ if (is(x, "RangedSummarizedExperiment")) {
+ ans <- BiocGenerics:::replaceSlots(x, ...,
+ metadata=ans_metadata,
+ elementMetadata=ans_elementMetadata,
+ rowRanges=ans_rowRanges,
+ colData=ans_colData,
+ assays=ans_assays,
+ check=FALSE)
+ } else {
+ ans <- BiocGenerics:::replaceSlots(x, ...,
+ metadata=ans_metadata,
+ elementMetadata=ans_elementMetadata,
+ NAMES=ans_NAMES,
+ colData=ans_colData,
+ assays=ans_assays,
+ check=FALSE)
+ }
+ msg <- .valid.SummarizedExperiment.assays_dim(ans)
+ }
+ if (!is.null(msg))
+ stop(msg)
+ ans
+})
+
+setMethod("extractROWS", "SummarizedExperiment",
+ function(x, i)
+ {
+ i <- normalizeSingleBracketSubscript(i, x)
+ x[i, ]
+ }
+)
+
+setMethod("replaceROWS", "SummarizedExperiment",
+ function(x, i, value)
+ {
+ i <- normalizeSingleBracketSubscript(i, x)
+ x[i, ] <- value
+ x
+ }
+)
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Quick colData access.
+###
+
+setMethod("[[", c("SummarizedExperiment", "ANY", "missing"),
+ function(x, i, j, ...)
+{
+ colData(x)[[i, ...]]
+})
+
+setReplaceMethod("[[", c("SummarizedExperiment", "ANY", "missing"),
+ function(x, i, j, ..., value)
+{
+ colData(x)[[i, ...]] <- value
+ x
+})
+
+.DollarNames.SummarizedExperiment <- function(x, pattern = "")
+ grep(pattern, names(colData(x)), value=TRUE)
+
+setMethod("$", "SummarizedExperiment",
+ function(x, name)
+{
+ colData(x)[[name]]
+})
+
+setReplaceMethod("$", "SummarizedExperiment",
+ function(x, name, value)
+{
+ colData(x)[[name]] <- value
+ x
+})
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Display.
+###
+
+setMethod("show", "SummarizedExperiment",
+ function(object)
+{
+ selectSome <- S4Vectors:::selectSome
+ scat <- function(fmt, vals=character(), exdent=2, ...)
+ {
+ vals <- ifelse(nzchar(vals), vals, "''")
+ lbls <- paste(S4Vectors:::selectSome(vals), collapse=" ")
+ txt <- sprintf(fmt, length(vals), lbls)
+ cat(strwrap(txt, exdent=exdent, ...), sep="\n")
+ }
+
+ cat("class:", class(object), "\n")
+ cat("dim:", dim(object), "\n")
+
+ ## metadata()
+ expt <- names(metadata(object))
+ if (is.null(expt))
+ expt <- character(length(metadata(object)))
+ scat("metadata(%d): %s\n", expt)
+
+ ## assays()
+ nms <- assayNames(object)
+ if (is.null(nms))
+ nms <- character(length(assays(object, withDimnames=FALSE)))
+ scat("assays(%d): %s\n", nms)
+
+ ## rownames()
+ dimnames <- dimnames(object)
+ dlen <- sapply(dimnames, length)
+ if (dlen[[1]]) scat("rownames(%d): %s\n", dimnames[[1]])
+ else scat("rownames: NULL\n")
+
+ ## rowData()
+ scat("rowData names(%d): %s\n", names(rowData(object)))
+
+ ## colnames()
+ if (dlen[[2]]) scat("colnames(%d): %s\n", dimnames[[2]])
+ else cat("colnames: NULL\n")
+
+ ## colData()
+ scat("colData names(%d): %s\n", names(colData(object)))
+})
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### Combine.
+###
+
+### Appropriate for objects with different ranges and same samples.
+setMethod("rbind", "SummarizedExperiment",
+ function(..., deparse.level=1)
+{
+ args <- unname(list(...))
+ .rbind.SummarizedExperiment(args)
+})
+
+.rbind.SummarizedExperiment <- function(args)
+{
+ if (!.compare(lapply(args, colnames)))
+ stop("'...' objects must have the same colnames")
+ if (!.compare(lapply(args, ncol)))
+ stop("'...' objects must have the same number of samples")
+
+ if (is(args[[1L]], "RangedSummarizedExperiment")) {
+ rowRanges <- do.call(c, lapply(args, rowRanges))
+ } else {
+ ## Code below taken from combine_GAlignments_objects() from the
+ ## GenomicAlignments package.
+
+ ## Combine "NAMES" slots.
+ NAMES_slots <- lapply(args, function(x) x at NAMES)
+ ## TODO: Use elementIsNull() here when it becomes available.
+ has_no_names <- sapply(NAMES_slots, is.null, USE.NAMES=FALSE)
+ if (all(has_no_names)) {
+ NAMES <- NULL
+ } else {
+ noname_idx <- which(has_no_names)
+ if (length(noname_idx) != 0L)
+ NAMES_slots[noname_idx] <-
+ lapply(elementNROWS(args[noname_idx]), character)
+ NAMES <- unlist(NAMES_slots, use.names=FALSE)
+ }
+ }
+ colData <- .cbind.DataFrame(args, colData, "colData")
+ assays <- do.call(rbind, lapply(args, slot, "assays"))
+ elementMetadata <- do.call(rbind, lapply(args, slot, "elementMetadata"))
+ metadata <- do.call(c, lapply(args, metadata))
+
+ if (is(args[[1L]], "RangedSummarizedExperiment")) {
+ BiocGenerics:::replaceSlots(args[[1L]],
+ rowRanges=rowRanges, colData=colData, assays=assays,
+ elementMetadata=elementMetadata, metadata=metadata)
+ } else {
+ BiocGenerics:::replaceSlots(args[[1L]],
+ NAMES=NAMES, colData=colData, assays=assays,
+ elementMetadata=elementMetadata, metadata=metadata)
+ }
+}
+
+### Appropriate for objects with same ranges and different samples.
+setMethod("cbind", "SummarizedExperiment",
+ function(..., deparse.level=1)
+{
+ args <- unname(list(...))
+ .cbind.SummarizedExperiment(args)
+})
+
+.cbind.SummarizedExperiment <- function(args)
+{
+ if (is(args[[1L]], "RangedSummarizedExperiment")) {
+ if (!.compare(lapply(args, rowRanges), TRUE))
+ stop("'...' object ranges (rows) are not compatible")
+ rowRanges <- rowRanges(args[[1L]])
+ mcols(rowRanges) <- .cbind.DataFrame(args, mcols, "mcols")
+ } else {
+ elementMetadata <- .cbind.DataFrame(args, mcols, "mcols")
+ }
+ colData <- do.call(rbind, lapply(args, colData))
+ assays <- do.call(cbind, lapply(args, slot, "assays"))
+ metadata <- do.call(c, lapply(args, metadata))
+
+ if (is(args[[1L]], "RangedSummarizedExperiment")) {
+ BiocGenerics:::replaceSlots(args[[1L]],
+ rowRanges=rowRanges,
+ colData=colData, assays=assays, metadata=metadata)
+ } else {
+ BiocGenerics:::replaceSlots(args[[1L]],
+ elementMetadata=elementMetadata,
+ colData=colData, assays=assays, metadata=metadata)
+ }
+}
+
+.compare <- function(x, GenomicRanges=FALSE)
+{
+ x1 <- x[[1]]
+ if (GenomicRanges) {
+ if (is(x1, "GRangesList")) {
+ x <- lapply(x, unlist)
+ x1 <- x[[1]]
+ }
+ for (i in seq_along(x)[-1]) {
+ if (!identicalVals(x1, x[[i]]))
+ return(FALSE)
+ }
+ return(TRUE)
+ } else {
+ all(sapply(x[-1],
+ function(xelt) all(identical(xelt, x[[1]]))))
+ }
+}
+
+.cbind.DataFrame <- function(args, accessor, accessorName)
+{
+ lst <- lapply(args, accessor)
+ if (!.compare(lst)) {
+ nms <- lapply(lst, names)
+ nmsv <- unlist(nms, use.names=FALSE)
+ names(nmsv) <- rep(seq_along(nms), elementNROWS(nms))
+ dups <- duplicated(nmsv)
+ ## no duplicates
+ if (!any(dups))
+ return(do.call(cbind, lst))
+ ## confirm duplicates are the same
+ lapply(nmsv[duplicated(nmsv)], function(d) {
+ if (!.compare(lapply(lst, "[", d)))
+ stop("column(s) '", unname(d),
+ "' in ", sQuote(accessorName),
+ " are duplicated and the data do not match")})
+ ## remove duplicates
+ do.call(cbind, lst)[,!dups]
+ } else {
+ lst[[1]]
+ }
+}
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### identicalVals()
+###
+### Internal generic and methods (i.e. not exported).
+### Provides a fast implementation of 'length(x) == length(y) && all(x == y)'
+### for various kinds of vector-like objects.
+### TODO: Move this to S4Vectors (for the generic and methods for factor and
+### Rle objects) and IRanges (for the method for Ranges objects).
+###
+
+setGeneric("identicalVals", function(x, y) standardGeneric("identicalVals"))
+
+### Semantically equivalent to identical(as.character(x), as.character(y))
+### but avoids turning the 2 factor objects into character vectors so is more
+### efficient.
+setMethod("identicalVals", c("factor", "factor"),
+ function(x, y)
+ {
+ m <- match(levels(y), levels(x), nomatch=0L)
+ identical(as.integer(x), m[y])
+ }
+)
+
+### Only support factor-Rle objects at the moment!
+### Semantically equivalent to identical(as.character(x), as.character(y))
+### but avoids turning the 2 factor-Rle objects into character vectors so is
+### more efficient.
+setMethod("identicalVals", c("Rle", "Rle"),
+ function(x, y) identical(runLength(x), runLength(y)) &&
+ identicalVals(runValue(x), runValue(y))
+)
+
+setMethod("identicalVals", c("Ranges", "Ranges"),
+ function(x, y) identical(start(x), start(y)) &&
+ identical(width(x), width(y))
+)
+
+### Like 'x == y' this method ignores circularity of the underlying sequences
+### e.g. ranges [1, 10] and [101, 110] represent the same position on a
+### circular sequence of length 100 so should be considered equal. However
+### for 'x == y' and the method below, they are not.
+### TODO: Take circularity of the underlying sequences into account.
+setMethod("identicalVals", c("GenomicRanges", "GenomicRanges"),
+ function(x, y)
+ {
+ ## Trying to merge 'seqinfo(x)' and 'seqinfo(y)' will raise an error
+ ## if 'x' and 'y' are not based on the same reference genome. This is
+ ## the standard way to check that 'x' and 'y' are based on the same
+ ## reference genome.
+ merge(seqinfo(x), seqinfo(y)) # we ignore the returned value
+
+ identicalVals(seqnames(x), seqnames(y)) &&
+ identicalVals(ranges(x), ranges(y)) &&
+ identicalVals(strand(x), strand(y))
+ }
+)
+
+
+### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+### On-disk realization.
+###
+
+setMethod("realize", "SummarizedExperiment",
+ function(x)
+ {
+ for (i in seq_along(assays(x))) {
+ ## We drop the dimnames of the individual assays for 2 reasons:
+ ## 1) These dimnames are kind of irrelevant. The dimnames that
+ ## really matter are 'dimnames(x)' and they are stored
+ ## somewhere else in 'x'. So we don't loose them by not
+ ## realizing the assay dimnames on disk. As a little extra
+ ## bonus, this actually saves a little bit of time and disk
+ ## space.
+ ## 2) Using the HDF5Array backend to realize an array-like object
+ ## on disk doesn't store the dimnames in the HDF5 file at the
+ ## moment.
+ a <- assay(x, i, withDimnames=FALSE)
+ dimnames(a) <- NULL
+ assay(x, i) <- realize(a)
+ }
+ x
+ }
+)
+
diff --git a/R/coverage-methods.R b/R/coverage-methods.R
new file mode 100644
index 0000000..2bc5f22
--- /dev/null
+++ b/R/coverage-methods.R
@@ -0,0 +1,15 @@
+### =========================================================================
+### "coverage" method
+### -------------------------------------------------------------------------
+###
+
+
+setMethod("coverage", "RangedSummarizedExperiment",
+ function(x, shift=0L, width=NULL, weight=1L,
+ method=c("auto", "sort", "hash"))
+ {
+ x <- rowRanges(x)
+ callGeneric()
+ }
+)
+
diff --git a/R/findOverlaps-methods.R b/R/findOverlaps-methods.R
new file mode 100644
index 0000000..d39afee
--- /dev/null
+++ b/R/findOverlaps-methods.R
@@ -0,0 +1,42 @@
+### =========================================================================
+### findOverlaps methods
+### -------------------------------------------------------------------------
+
+
+### findOverlaps
+
+setMethod("findOverlaps", c("RangedSummarizedExperiment", "Vector"),
+ function(query, subject, maxgap=0L, minoverlap=1L,
+ type=c("any", "start", "end", "within", "equal"),
+ select=c("all", "first", "last", "arbitrary"),
+ ignore.strand=FALSE)
+ {
+ query <- rowRanges(query)
+ callGeneric()
+ }
+)
+
+setMethod("findOverlaps", c("Vector", "RangedSummarizedExperiment"),
+ function(query, subject, maxgap=0L, minoverlap=1L,
+ type=c("any", "start", "end", "within", "equal"),
+ select=c("all", "first", "last", "arbitrary"),
+ ignore.strand=FALSE)
+ {
+ subject <- rowRanges(subject)
+ callGeneric()
+ }
+)
+
+setMethod("findOverlaps", c("RangedSummarizedExperiment",
+ "RangedSummarizedExperiment"),
+ function(query, subject, maxgap=0L, minoverlap=1L,
+ type=c("any", "start", "end", "within", "equal"),
+ select=c("all", "first", "last", "arbitrary"),
+ ignore.strand=FALSE)
+ {
+ query <- rowRanges(query)
+ subject <- rowRanges(subject)
+ callGeneric()
+ }
+)
+
diff --git a/R/inter-range-methods.R b/R/inter-range-methods.R
new file mode 100644
index 0000000..0a36037
--- /dev/null
+++ b/R/inter-range-methods.R
@@ -0,0 +1,22 @@
+### =========================================================================
+### Inter-range methods
+### -------------------------------------------------------------------------
+###
+
+
+setMethod("isDisjoint", "RangedSummarizedExperiment",
+ function(x, ignore.strand=FALSE)
+ {
+ x <- rowRanges(x)
+ callGeneric()
+ }
+)
+
+setMethod("disjointBins", "RangedSummarizedExperiment",
+ function(x, ignore.strand = FALSE)
+ {
+ x <- rowRanges(x)
+ callGeneric()
+ }
+)
+
diff --git a/R/intra-range-methods.R b/R/intra-range-methods.R
new file mode 100644
index 0000000..e9873c9
--- /dev/null
+++ b/R/intra-range-methods.R
@@ -0,0 +1,79 @@
+### =========================================================================
+### Intra-range methods
+### -------------------------------------------------------------------------
+###
+
+
+setMethod("shift", "RangedSummarizedExperiment",
+ function(x, shift=0L, use.names=TRUE)
+ {
+ x0 <- x
+ x <- rowRanges(x)
+ rowRanges(x0) <- callGeneric()
+ x0
+ }
+)
+
+setMethod("narrow", "RangedSummarizedExperiment",
+ function(x, start=NA, end=NA, width=NA, use.names=TRUE)
+ {
+ x0 <- x
+ x <- rowRanges(x)
+ rowRanges(x0) <- callGeneric()
+ x0
+ }
+)
+
+setMethod("resize", "RangedSummarizedExperiment",
+ function(x, width, fix="start", use.names=TRUE, ignore.strand=FALSE)
+ {
+ x0 <- x
+ x <- rowRanges(x)
+ rowRanges(x0) <- callGeneric()
+ x0
+ }
+)
+
+setMethod("flank", "RangedSummarizedExperiment",
+ function(x, width, start=TRUE, both=FALSE, use.names=TRUE,
+ ignore.strand=FALSE)
+ {
+ x0 <- x
+ x <- rowRanges(x)
+ rowRanges(x0) <- callGeneric()
+ x0
+ }
+)
+
+setMethod("promoters", "RangedSummarizedExperiment",
+ function(x, upstream=2000, downstream=200)
+ {
+ x0 <- x
+ x <- rowRanges(x)
+ rowRanges(x0) <- callGeneric()
+ x0
+ }
+)
+
+### Because 'keep.all.ranges' is FALSE by default, it will break if some
+### ranges are dropped.
+setMethod("restrict", "RangedSummarizedExperiment",
+ function(x, start=NA, end=NA, keep.all.ranges=FALSE, use.names=TRUE)
+ {
+ x0 <- x
+ x <- rowRanges(x)
+ rowRanges(x0) <- callGeneric()
+ x0
+ }
+)
+
+setMethod("trim", "RangedSummarizedExperiment",
+ function(x, use.names=TRUE)
+ {
+ x0 <- x
+ x <- rowRanges(x)
+ rowRanges(x0) <- callGeneric()
+ x0
+ }
+)
+
diff --git a/R/makeSummarizedExperimentFromDataFrame.R b/R/makeSummarizedExperimentFromDataFrame.R
new file mode 100644
index 0000000..57e07a2
--- /dev/null
+++ b/R/makeSummarizedExperimentFromDataFrame.R
@@ -0,0 +1,32 @@
+### =========================================================================
+### makeSummarizedExperimentFromDataFrame()
+### -------------------------------------------------------------------------
+
+### 'df' must be a data.frame or DataFrame object.
+makeSummarizedExperimentFromDataFrame <-
+ function(df,
+ ...,
+ seqinfo = NULL,
+ starts.in.df.are.0based = FALSE)
+ {
+ rowRanges <- makeGRangesFromDataFrame(
+ df,
+ ...,
+ keep.extra.columns = FALSE,
+ seqinfo = seqinfo,
+ starts.in.df.are.0based = starts.in.df.are.0based)
+
+ # Find column names for rowRanges
+ granges_cols <-
+ GenomicRanges:::.find_GRanges_cols(names(df), ...)
+
+ rangedNames <- names(df)[na.omit(granges_cols)]
+ idx <- match(rangedNames, names(df))
+ counts <- as.matrix(df[, -idx, drop = FALSE])
+
+ if (!is(as.vector(counts), "numeric"))
+ stop("failed to coerce non-range columns to 'numeric'")
+
+ SummarizedExperiment(
+ assays=SimpleList(counts), rowRanges=rowRanges)
+ }
diff --git a/R/makeSummarizedExperimentFromExpressionSet.R b/R/makeSummarizedExperimentFromExpressionSet.R
new file mode 100644
index 0000000..66d123f
--- /dev/null
+++ b/R/makeSummarizedExperimentFromExpressionSet.R
@@ -0,0 +1,265 @@
+##
+## makeSummarizedExperimentFromExpressionSet
+
+## coercion
+
+.from_rowRanges_to_FeatureData <- function(from)
+{
+ if (is(from, "GRanges")) {
+ fd <- .from_GRanges_to_FeatureData(from)
+ } else if (is(from, "GRangesList")) {
+ fd <- .from_GRangesList_to_FeatureData(from)
+ } else {
+ stop("class ", sQuote(class(from)),
+ " is not a supported type for rowRanges coercion")
+ }
+ featureNames(fd) <- names(from)
+ fd
+}
+
+.from_GRanges_to_FeatureData <- function(from)
+{
+ data <- as.data.frame(from)
+
+ ## the first mcols are automatically included in the data.frame from
+ ## as.data.frame, the secondary mcols holds the metadata for the first
+ ## metadata columns.
+ metaData <- mcols(mcols(from))
+ if (is.null(metaData)) {
+ metaData <- as.data.frame(matrix(ncol=0, nrow=NCOL(data)))
+ } else {
+ metaData <- as.data.frame(metaData)
+ }
+ AnnotatedDataFrame(data, metaData)
+}
+.from_GRangesList_to_FeatureData <- function(from)
+{
+ data <- as.data.frame(mcols(from))
+
+ ## the first mcols are automatically included in the data.frame from
+ ## as.data.frame, the secondary mcols holds the metadata for the first
+ ## metadata columns.
+ metaData <- mcols(mcols(from))
+ if (is.null(metaData)) {
+ metaData <- as.data.frame(matrix(ncol=0, nrow=NCOL(data)))
+ } else {
+ metaData <- as.data.frame(metaData)
+ }
+ AnnotatedDataFrame(data, metaData)
+}
+
+.from_AnnotatedDataFrame_to_DataFrame <- function(from)
+{
+ df <- DataFrame(pData(from), row.names=rownames(from))
+ mcols(df) <- DataFrame(varMetadata(from))
+ df
+}
+
+.from_DataFrame_to_AnnotatedDataFrame <- function(df)
+{
+ data <- as(df, "data.frame")
+ metaData <- mcols(df)
+ if (is.null(metaData)) {
+ metaData <- as.data.frame(matrix(ncol=0, nrow=NCOL(data)))
+ } else {
+ metaData <- as(metaData, "data.frame")
+ }
+ AnnotatedDataFrame(data, metaData)
+}
+
+## If the ExpressionSet has featureData with range information make
+## GRanges out of that, otherwise make an empty GRangesList with names
+## from the featureNames
+naiveRangeMapper <- function(from)
+{
+ nms <- featureNames(from)
+ res <- tryCatch({
+ makeGRangesFromDataFrame(pData(featureData(from)),
+ keep.extra.columns = TRUE)
+ }, error = function(e) {
+ res <- relist(GRanges(), vector("list", length=length(nms)))
+ mcols(res) <- .from_AnnotatedDataFrame_to_DataFrame(featureData(from))
+ res
+ })
+ names(res) <- nms
+ res
+}
+
+# Simple ProbeId to Range mapper
+# Probes with multiple ranges are dropped
+# The sign of the chromosome location is assumed to contain the strand
+# information
+probeRangeMapper <- function(from)
+{
+ annotation <- annotation(from)
+ if (identical(annotation, character(0))) {
+ return(naiveRangeMapper(from))
+ }
+ if (requireNamespace("annotate", quietly = TRUE)) {
+ annotationPackage <- annotate::annPkgName(annotation)
+ test <- require(annotationPackage, character.only = TRUE,
+ quietly = TRUE)
+ if (test) {
+ db <- get(annotationPackage, envir = asNamespace(annotationPackage))
+ pid <- featureNames(from)
+ locs <- AnnotationDbi::select(
+ db, pid, columns = c("CHR", "CHRLOC", "CHRLOCEND"))
+ locs <- na.omit(locs)
+ dups <- duplicated(locs$PROBEID)
+ if (any(dups)) {
+ locs <- locs[!dups, , drop = FALSE]
+ }
+ strand <- ifelse(locs$CHRLOC > 0, "+", "-")
+ res <- GRanges(seqnames = locs$CHR,
+ ranges = IRanges(abs(locs$CHRLOC),
+ abs(locs$CHRLOCEND)),
+ strand = strand)
+ names(res) <- locs$PROBEID
+
+ if (NROW(res) < length(pid)) {
+ warning(length(pid) - NROW(res),
+ " probes could not be mapped.", call. = FALSE)
+ }
+ res
+ } else {
+ stop("Failed to load ", sQuote(annotationPackage), " package",
+ call. = FALSE)
+ }
+ } else {
+ stop("Failed to load annotate package", call. = FALSE)
+ }
+}
+
+# Simple ProbeId to Gene mapper
+# Is there a way to get the txDb given the annotation package?
+geneRangeMapper <- function(txDbPackage, key = "ENTREZID")
+{
+ function(from) {
+ annotation <- annotation(from)
+ if (identical(annotation, character(0))) {
+ return(naiveRangeMapper(from))
+ }
+ if (requireNamespace("annotate", quietly = TRUE)) {
+ annotationPackage <- annotate::annPkgName(annotation)
+ test <- require(annotationPackage, character.only = TRUE,
+ quietly = TRUE)
+ if (test) {
+ db <- get(annotationPackage,
+ envir = asNamespace(annotationPackage))
+ pid <- featureNames(from)
+ probeIdToGeneId <-
+ AnnotationDbi::mapIds(db, pid, key, "PROBEID")
+ geneIdToProbeId <-
+ setNames(names(probeIdToGeneId), probeIdToGeneId)
+
+ if (requireNamespace(txDbPackage, quietly = TRUE)) {
+ txDb <- get(txDbPackage, envir = asNamespace(txDbPackage))
+ genes <- GenomicFeatures::genes(txDb)
+ probesWithAMatch <-
+ probeIdToGeneId[probeIdToGeneId %in% names(genes)]
+ res <- genes[probesWithAMatch]
+ names(res) <- geneIdToProbeId[names(res)]
+ if (NROW(res) < length(pid)) {
+ warning(length(pid) - NROW(res),
+ " probes could not be mapped.", call. = FALSE)
+ }
+ res
+ } else {
+ stop("Failed to load ", sQuote(txDbPackage), " package",
+ call. = FALSE)
+ }
+
+ } else {
+ stop("Failed to load ", sQuote(annotationPackage), " package",
+ call. = FALSE)
+ }
+ } else {
+ stop("Failed to load annotate package", call. = FALSE)
+ }
+ }
+}
+
+makeSummarizedExperimentFromExpressionSet <-
+ function(from, mapFun = naiveRangeMapper, ...)
+{
+ mapFun <- match.fun(mapFun)
+ rowRanges <- mapFun(from, ...)
+ matches <- match(names(rowRanges),
+ featureNames(from),
+ nomatch = 0)
+ from <- from[matches, drop = FALSE]
+ assays <- as.list(assayData(from))
+ colData <- .from_AnnotatedDataFrame_to_DataFrame(phenoData(from))
+ metadata <- SimpleList(
+ experimentData = experimentData(from),
+ annotation = annotation(from),
+ protocolData = protocolData(from)
+ )
+
+ SummarizedExperiment(
+ assays = assays,
+ rowRanges = rowRanges,
+ colData = colData,
+ metadata = metadata
+ )
+}
+
+setAs("ExpressionSet", "RangedSummarizedExperiment", function(from)
+{
+ makeSummarizedExperimentFromExpressionSet(from)
+})
+
+setAs("RangedSummarizedExperiment", "ExpressionSet",
+ function(from)
+{
+ assayData <- list2env(as.list(assays(from)))
+
+ numAssays <- length(assayData)
+
+ if (numAssays == 0) {
+ assayData$exprs <- new("matrix")
+ } else if (!"exprs" %in% ls(assayData)) {
+ ## if there isn't an exprs assay we need to pick one as exprs,
+ ## so rename the first element exprs and issue a warning.
+ exprs <- ls(assayData)[[1]]
+ warning("No assay named ", sQuote("exprs"), " found, renaming ",
+ exprs, " to ", sQuote("exprs"), ".")
+ assayData[["exprs"]] <- assayData[[exprs]]
+ rm(list=exprs, envir=assayData)
+ }
+
+ featureData <- .from_rowRanges_to_FeatureData(rowRanges(from))
+ phenoData <- .from_DataFrame_to_AnnotatedDataFrame(colData(from))
+
+ metadata <- metadata(from)
+
+ experimentData <- if (!is.null(metadata$experimentData)) {
+ metadata$experimentData
+ } else {
+ MIAME()
+ }
+
+ annotation <- if (!is.null(metadata$annotation)) {
+ metadata$annotation
+ } else {
+ character()
+ }
+
+ protocolData <- if (!is.null(metadata$protocolData)) {
+ metadata$protocolData
+ } else {
+ annotatedDataFrameFrom(assayData, byrow=FALSE)
+ }
+
+ ExpressionSet(assayData,
+ phenoData = phenoData,
+ featureData = featureData,
+ experimentData = experimentData,
+ annotation = annotation,
+ protocolData = protocolData
+ )
+})
+
+setAs("SummarizedExperiment", "ExpressionSet", function(from)
+ as(as(from, "RangedSummarizedExperiment"), "ExpressionSet")
+)
diff --git a/R/nearest-methods.R b/R/nearest-methods.R
new file mode 100644
index 0000000..7f20507
--- /dev/null
+++ b/R/nearest-methods.R
@@ -0,0 +1,117 @@
+### =========================================================================
+### nearest (and related) methods
+### -------------------------------------------------------------------------
+###
+
+
+### precede & follow
+
+for (f in c("precede", "follow")) {
+ setMethod(f, c("RangedSummarizedExperiment", "ANY"),
+ function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE)
+ {
+ x <- rowRanges(x)
+ callGeneric()
+ }
+ )
+ setMethod(f, c("ANY", "RangedSummarizedExperiment"),
+ function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE)
+ {
+ subject <- rowRanges(subject)
+ callGeneric()
+ }
+ )
+ setMethod(f, c("RangedSummarizedExperiment", "RangedSummarizedExperiment"),
+ function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE)
+ {
+ x <- rowRanges(x)
+ subject <- rowRanges(subject)
+ callGeneric()
+ }
+ )
+}
+
+### nearest
+
+setMethod("nearest", c("RangedSummarizedExperiment", "ANY"),
+ function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE)
+ {
+ x <- rowRanges(x)
+ callGeneric()
+ }
+)
+
+setMethod("nearest", c("ANY", "RangedSummarizedExperiment"),
+ function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE)
+ {
+ subject <- rowRanges(subject)
+ callGeneric()
+ }
+)
+
+setMethod("nearest", c("RangedSummarizedExperiment",
+ "RangedSummarizedExperiment"),
+ function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE)
+ {
+ x <- rowRanges(x)
+ subject <- rowRanges(subject)
+ callGeneric()
+ }
+)
+
+### distance
+
+setMethod("distance", c("RangedSummarizedExperiment", "ANY"),
+ function(x, y, ignore.strand=FALSE, ...)
+ {
+ x <- rowRanges(x)
+ callGeneric()
+ }
+)
+
+setMethod("distance", c("ANY", "RangedSummarizedExperiment"),
+ function(x, y, ignore.strand=FALSE, ...)
+ {
+ y <- rowRanges(y)
+ callGeneric()
+ }
+)
+
+setMethod("distance", c("RangedSummarizedExperiment",
+ "RangedSummarizedExperiment"),
+ function(x, y, ignore.strand=FALSE, ...)
+ {
+ x <- rowRanges(x)
+ y <- rowRanges(y)
+ callGeneric()
+ }
+)
+
+### distanceToNearest
+
+setMethod("distanceToNearest", c("RangedSummarizedExperiment", "ANY"),
+ function(x, subject, ignore.strand=FALSE, ...)
+ {
+ x <- rowRanges(x)
+ callGeneric()
+ }
+)
+
+setMethod("distanceToNearest", c("ANY", "RangedSummarizedExperiment"),
+ function(x, subject, ignore.strand=FALSE, ...)
+ {
+ subject <- rowRanges(subject)
+ callGeneric()
+ }
+)
+
+setMethod("distanceToNearest", c("RangedSummarizedExperiment",
+ "RangedSummarizedExperiment"),
+ function(x, subject, ignore.strand=FALSE, ...)
+ {
+ x <- rowRanges(x)
+ subject <- rowRanges(subject)
+ callGeneric()
+ }
+)
+
diff --git a/R/readKallisto.R b/R/readKallisto.R
new file mode 100644
index 0000000..9a160f2
--- /dev/null
+++ b/R/readKallisto.R
@@ -0,0 +1,196 @@
+.require <-
+ function(pkg)
+{
+ withCallingHandlers({
+ requireNamespace(pkg)
+ }, warning=function(w) {
+ invokeRestart("muffleWarning")
+ }) || {
+ msg <- sprintf('install %s with
+ source("http://bioconductor.org/biocLite.R"); biocLite("%s")',
+ pkg, pkg)
+ stop(paste(strwrap(msg, exdent=2), collapse="\n"))
+ }
+}
+
+.open <- function(files, h5) {
+ lapply(files, function(file) {
+ if (h5 && rhdf5::H5Fis_hdf5(file))
+ rhdf5::H5Fopen(file)
+ else file(file, open="rt")
+ })
+}
+
+.close <- function(cons)
+ UseMethod(".close")
+
+.close.list <- function(cons)
+ for (con in cons)
+ .close(con)
+
+.close.connection <- function(cons)
+ close(cons)
+
+.close.H5IdComponent <- function(cons)
+ rhdf5::H5Fclose(cons)
+
+.colData <- function(jsonfile) {
+ json <- jsonlite::fromJSON(jsonfile)
+ do.call("data.frame", c(json, stringsAsFactors=FALSE))
+}
+
+.KALLISTO_COLCLASSES <-
+ c("character", "integer" , "numeric", "numeric", "numeric")
+
+.KALLISTO_ROWDATA <- "length"
+
+KALLISTO_ASSAYS <- c("est_counts", "tpm", "eff_length")
+
+.read <- function(con)
+ UseMethod(".read")
+
+.read.connection <- function(con)
+ read.delim(con, header=TRUE, colClasses=.KALLISTO_COLCLASSES, row.names=1)
+
+.read.H5IdComponent <- function(con) {
+ eff_length <- rhdf5::h5read(con, "/aux/eff_lengths")
+ est_counts <- rhdf5::h5read(con, "/est_counts")
+ tpm0 <- est_counts / eff_length
+ data.frame(row.names=rhdf5::h5read(con, "/aux/ids"),
+ length=rhdf5::h5read(con, "/aux/lengths"),
+ eff_length=eff_length,
+ est_counts=est_counts,
+ tpm=tpm0 / (sum(tpm0) / 1e6))
+}
+
+readKallisto <-
+ function(files,
+ json=file.path(dirname(files), "run_info.json"),
+ h5=any(grepl("\\.h5$", files)),
+ what=KALLISTO_ASSAYS,
+ as=c("SummarizedExperiment", "list", "matrix"))
+{
+ as <- match.arg(as)
+ if (missing(what))
+ what <- what[1]
+ else {
+ whats <- eval(formals()[["what"]])
+ if (!all(what %in% KALLISTO_ASSAYS))
+ stop("'what' must be in ",
+ paste(sQuote(KALLISTO_ASSAYS), collapse=", "),
+ call.=FALSE)
+ }
+
+ stopifnot(is.character(files))
+ test <- file.exists(files)
+ if (!all(test))
+ stop("file(s) do not exist:\n ",
+ paste(files[!test], collapse="\n "))
+ if (is.null(names(files)))
+ names(files) <- basename(dirname(files))
+ if (anyDuplicated(names(files)))
+ stop("'names()' of 'files' must be unique")
+
+ if (as != "matrix") {
+ .require("jsonlite")
+ stopifnot(length(files) == length(json))
+ if (!is.null(names(json)))
+ stopifnot(identical(names(json), names(files)))
+ else
+ names(json) <- names(files)
+ test <- file.exists(json)
+ if (!all(test))
+ stop("json file(s) do not exist:\n ",
+ paste(json[!test], collapse="\n "))
+ }
+
+ if (h5)
+ .require("rhdf5")
+
+ cons <- .open(files, h5)
+ value <- .read(cons[[1]])
+ rowData <- value[, .KALLISTO_ROWDATA, drop=FALSE]
+ assay <- matrix(0, nrow(rowData), length(cons),
+ dimnames=list(rownames(rowData), names(cons)))
+ assays <- setNames(replicate(length(what), assay, FALSE), what)
+ for (w in what)
+ assays[[w]][,1] <- value[[w]]
+ for (i in seq_along(cons)[-1]) {
+ value <- .read(cons[[i]])
+ if (!identical(rowData, value[, .KALLISTO_ROWDATA, drop=FALSE]))
+ stop("rowData differs between files:\n ",
+ paste(files[c(1, i)], collapse="\n "))
+ for (w in what)
+ assays[[w]][,i] <- value[[w]]
+ }
+ .close(cons)
+
+ if (as != "matrix")
+ colData <- do.call("rbind", lapply(json, .colData))
+
+ switch(as, matrix={
+ if (length(assays) == 1L)
+ assays[[1]]
+ else assays
+ }, list={
+ c(setNames(list(colData, rowData), c("colData", "rowData")), assays)
+ }, SummarizedExperiment={
+ SummarizedExperiment(assays=assays,
+ rowData=as(rowData, "DataFrame"),
+ colData=as(colData, "DataFrame"))
+ })
+}
+
+.readIds <- function(con, i) {
+ if (!missing(i))
+ rhdf5::h5read(con, "/aux/ids", list(i))
+ else rhdf5::h5read(con, "/aux/ids")
+}
+
+readKallistoBootstrap <-
+ function(file, i, j)
+{
+ .require("rhdf5")
+ stopifnot(length(file) == 1L, is.character(file))
+ stopifnot(file.exists(file))
+ stopifnot(rhdf5::H5Fis_hdf5(file))
+ con <- rhdf5::H5Fopen(file)
+ on.exit(rhdf5::H5Fclose(con))
+ nboot <- as.integer(rhdf5::h5read(con, "/aux/num_bootstrap"))
+ if (nboot == 0L)
+ stop("file contains no bootstraps:\n ", file)
+
+ if (!missing(i) && is.character(i)) {
+ idx <- match(i, .readIds(con))
+ if (anyNA(i))
+ stop("unknown target id(s)", i[is.na(idx)])
+ i <- idx
+ }
+ if (!missing(j) && is.numeric(j)) {
+ if (any((j < 1L) || any(j > nboot)))
+ stop("'j' must be >0 and <=", nboot)
+ j <- paste0("bs", as.integer(j) - 1L)
+ }
+
+ m <- if (missing(i) && missing(j)) {
+ simplify2array(rhdf5::h5read(con, "/bootstrap"))
+ } else if (missing(i)) {
+ query <- setNames(sprintf("/bootstrap/%s", j), j)
+ simplify2array(lapply(query, rhdf5::h5read, file=con))
+ } else if (missing(j)) {
+ group <- rhdf5::H5Gopen(con, "/bootstrap")
+ name <- rhdf5::h5ls(group)$name
+ rhdf5::H5Gclose(group)
+ query <- setNames(sprintf("/bootstrap/%s", name), name)
+ simplify2array(lapply(query, rhdf5::h5read, file=con, index=list(i)))
+ } else {
+ query <- setNames(sprintf("/bootstrap/%s", j), j)
+ simplify2array(lapply(query, rhdf5::h5read, file=con, index=list(i)))
+ }
+
+ rownames(m) <- .readIds(con, i)
+ if (missing(j)) {
+ o <- order(as.integer(sub("bs", "", colnames(m), fixed=TRUE)))
+ m[,o]
+ } else m
+}
diff --git a/R/saveHDF5SummarizedExperiment.R b/R/saveHDF5SummarizedExperiment.R
new file mode 100644
index 0000000..52a1ba4
--- /dev/null
+++ b/R/saveHDF5SummarizedExperiment.R
@@ -0,0 +1,127 @@
+### =========================================================================
+### Save/load a HDF5-based SummarizedExperiment object
+### -------------------------------------------------------------------------
+
+
+.create_dir <- function(dir, replace)
+{
+ if (dir.exists(dir)) {
+ if (!replace)
+ stop(wmsg("Directory \"", dir, "\" already exists. ",
+ "Use 'replace=TRUE' to replace it. ",
+ "Its content will be lost!"))
+ if (unlink(dir, recursive=TRUE) != 0L)
+ stop("failed to delete directory \"", dir, "\"")
+ } else if (file.exists(dir)) {
+ stop(wmsg("\"", dir, "\" already exists and is a file, ",
+ "not a directory"))
+ }
+ if (!suppressWarnings(dir.create(dir)))
+ stop("cannot create directory \"", dir, "\"")
+}
+
+.write_h5_assays <- function(assays, h5_path, chunk_dim, level, verbose)
+{
+ nassay <- length(assays)
+ for (i in seq_len(nassay)) {
+ a <- assays[[i]]
+ h5_name <- sprintf("assay%03d", i)
+ if (verbose)
+ message("Start writing assay ", i, "/", nassay, " to '",
+ h5_path, "':")
+ a <- HDF5Array::writeHDF5Array(a, h5_path, h5_name, chunk_dim, level,
+ verbose=verbose)
+ if (verbose)
+ message("Finished writing assay ", i, "/", nassay, " to '",
+ h5_path, "'.")
+ assays[[i]] <- a
+ }
+ assays
+}
+
+.shorten_h5_paths <- function(assays)
+{
+ nassay <- length(assays)
+ for (i in seq_len(nassay)) {
+ a <- assays[[i]]
+ a at seed@file <- basename(a at seed@file)
+ assays[[i]] <- a
+ }
+ assays
+}
+
+### Save all the assays in HDF5 format, including in-memory assays.
+### Delayed assays with delayed operations on them are realized while they
+### are written to disk..
+saveHDF5SummarizedExperiment <- function(x, dir="my_h5_se", replace=FALSE,
+ chunk_dim=NULL, level=NULL,
+ verbose=FALSE)
+{
+ if (!is(x, "SummarizedExperiment"))
+ stop("'x' must be a SummarizedExperiment object")
+ if (!isSingleString(dir))
+ stop(wmsg("'dir' must be a single string specifying the path ",
+ "to the directory where to save the ", class(x),
+ " object (the directory will be created)"))
+ if (!isTRUEorFALSE(replace))
+ stop("'replace' must be TRUE or FALSE")
+ if (!isTRUEorFALSE(verbose))
+ stop("'verbose' must be TRUE or FALSE")
+
+ ## We try library(HDF5Array) before deleting or creating directory 'dir'.
+ ## That way if HDF5Array is not installed then we will stop without having
+ ## made changes to the file system.
+ library(HDF5Array) # for writeHDF5Array()
+ .create_dir(dir, replace)
+
+ h5_path <- file.path(dir, "assays.h5")
+ x at assays <- .write_h5_assays(x at assays, h5_path, chunk_dim, level, verbose)
+
+ rds_path <- file.path(dir, "se.rds")
+ ans <- x
+ x at assays <- .shorten_h5_paths(x at assays)
+ saveRDS(x, file=rds_path)
+
+ invisible(ans)
+}
+
+.THE_EXPECTED_STUFF <- c(
+ "a HDF5-based SummarizedExperiment object previously ",
+ "saved with saveHDF5SummarizedExperiment()"
+)
+
+.stop_if_bad_dir <- function(dir)
+ stop(wmsg("directory \"", dir, "\" does not seem to contain ",
+ .THE_EXPECTED_STUFF))
+
+### Does a lot of checking and tries to fail graciously if the content
+### of 'dir' doesn't look as expected.
+loadHDF5SummarizedExperiment <- function(dir="my_h5_se")
+{
+ library(rhdf5) # for h5ls()
+ library(HDF5Array) # for the HDF5Array class
+ if (!isSingleString(dir))
+ stop(wmsg("'dir' must be a single string specifying the path ",
+ "to the directory containing ", .THE_EXPECTED_STUFF))
+ h5_path <- file.path(dir, "assays.h5")
+ rds_path <- file.path(dir, "se.rds")
+ if (!file.exists(h5_path) || !file.exists(rds_path))
+ .stop_if_bad_dir(dir)
+ h5_content <- try(rhdf5::h5ls(h5_path), silent=TRUE)
+ if (inherits(h5_content, "try-error"))
+ .stop_if_bad_dir(dir)
+ h5_datasets <- h5_content[ , "name"]
+ ans <- readRDS(rds_path)
+ if (!is(ans, "SummarizedExperiment"))
+ .stop_if_bad_dir(dir)
+ for (i in seq_along(assays(ans))) {
+ a <- assay(ans, i, withDimnames=FALSE)
+ if (!is(a, "HDF5Array") || !identical(a at seed@file, "assays.h5") ||
+ !(a at seed@name %in% h5_datasets))
+ .stop_if_bad_dir(dir)
+ a at seed@file <- file_path_as_absolute(file.path(dir, a at seed@file))
+ assay(ans, i, withDimnames=FALSE) <- a
+ }
+ ans
+}
+
diff --git a/R/zzz.R b/R/zzz.R
new file mode 100644
index 0000000..dd7aaac
--- /dev/null
+++ b/R/zzz.R
@@ -0,0 +1,2 @@
+.test <- function() BiocGenerics:::testPackage("SummarizedExperiment")
+
diff --git a/build/vignette.rds b/build/vignette.rds
new file mode 100644
index 0000000..ea870a4
Binary files /dev/null and b/build/vignette.rds differ
diff --git a/debian/README.test b/debian/README.test
deleted file mode 100644
index d22f2c9..0000000
--- a/debian/README.test
+++ /dev/null
@@ -1,15 +0,0 @@
-Notes on how this package can be tested.
-────────────────────────────────────────
-
-Please make sure you have installed the Recommended package
-r-bioc-shortread. Then you should be able to successfully run the test
-suite by doing
-
-R --no-save <<EOT
-BiocGenerics:::testPackage('Rsamtools')
-EOT
-
-Currently one out of 73 tests is failing which is sorted out with
-upstream before an autopkgtest will be provided.
-
- -- Andreas Tille <tille at debian.org> Mon, 16 Jun 2014 16:28:55 +0200
diff --git a/debian/changelog b/debian/changelog
deleted file mode 100644
index 8ee346e..0000000
--- a/debian/changelog
+++ /dev/null
@@ -1,61 +0,0 @@
-r-bioc-summarizedexperiment (1.6.3-1) unstable; urgency=medium
-
- * Team upload
- * New upstream version
- * Add new Build-Depends: r-bioc-delayedarray
-
- -- Graham Inggs <ginggs at debian.org> Fri, 09 Jun 2017 16:16:24 +0200
-
-r-bioc-summarizedexperiment (1.4.0-2) unstable; urgency=medium
-
- * Exclude more parts of test suite which fail due to missing data package
- * debhelper 10
- * d/watch: version=4
-
- -- Andreas Tille <tille at debian.org> Sun, 15 Jan 2017 18:43:53 +0100
-
-r-bioc-summarizedexperiment (1.4.0-1) unstable; urgency=medium
-
- * New upstream version
- * Convert to dh-r
- * Generic BioConductor homepage
- * New Build-Depends: r-cran-matrix
- * New versioned Build-Depends: r-bioc-s4vectors (>= 0.11.7),
- r-bioc-iranges (>= 2.7.2),
-
- -- Andreas Tille <tille at debian.org> Wed, 26 Oct 2016 21:57:26 +0200
-
-r-bioc-summarizedexperiment (1.2.0-1) unstable; urgency=medium
-
- * New upstream version
- * Fix versioned Depends
-
- -- Andreas Tille <tille at debian.org> Sun, 08 May 2016 19:12:19 +0200
-
-r-bioc-summarizedexperiment (1.0.2-3) unstable; urgency=medium
-
- * Skip test needing unpackaged database (thanks to Gordon Ball
- <gordon at chronitis.net> for the hint)
-
- -- Andreas Tille <tille at debian.org> Tue, 03 May 2016 20:27:21 +0200
-
-r-bioc-summarizedexperiment (1.0.2-2) unstable; urgency=medium
-
- * Add missing Depends to autopkgtest
-
- -- Andreas Tille <tille at debian.org> Fri, 29 Apr 2016 08:41:09 +0200
-
-r-bioc-summarizedexperiment (1.0.2-1) unstable; urgency=medium
-
- * New upstream version
- * cme fix dpkg-control
- * Fix dependencies of autopkgtest
- * Increased versioned Depends r-bioc-genomicranges
-
- -- Andreas Tille <tille at debian.org> Wed, 27 Apr 2016 22:38:57 +0200
-
-r-bioc-summarizedexperiment (1.0.0-1) unstable; urgency=low
-
- * Initial release (closes: #803616)
-
- -- Andreas Tille <tille at debian.org> Sat, 31 Oct 2015 22:52:41 +0100
diff --git a/debian/compat b/debian/compat
deleted file mode 100644
index f599e28..0000000
--- a/debian/compat
+++ /dev/null
@@ -1 +0,0 @@
-10
diff --git a/debian/control b/debian/control
deleted file mode 100644
index 9cbdc91..0000000
--- a/debian/control
+++ /dev/null
@@ -1,30 +0,0 @@
-Source: r-bioc-summarizedexperiment
-Maintainer: Debian Med Packaging Team <debian-med-packaging at lists.alioth.debian.org>
-Uploaders: Andreas Tille <tille at debian.org>
-Section: gnu-r
-Priority: optional
-Build-Depends: debhelper (>= 10),
- dh-r,
- r-base-dev,
- r-bioc-biobase (>= 2.26.0),
- r-bioc-genomicranges (>= 1.24.0),
- r-bioc-iranges (>= 2.7.2),
- r-bioc-s4vectors (>= 0.11.7),
- r-bioc-delayedarray,
- r-cran-matrix
-Standards-Version: 3.9.8
-Vcs-Browser: https://anonscm.debian.org/viewvc/debian-med/trunk/packages/R/r-bioc-summarizedexperiment/trunk/
-Vcs-Svn: svn://anonscm.debian.org/debian-med/trunk/packages/R/r-bioc-summarizedexperiment/trunk/
-Homepage: https://bioconductor.org/packages/SummarizedExperiment/
-
-Package: r-bioc-summarizedexperiment
-Architecture: all
-Depends: ${R:Depends},
- ${misc:Depends},
-Recommends: ${R:Recommends}
-Suggests: ${R:Suggests}
-Description: BioConductor assay container
- The SummarizedExperiment container contains one or more assays, each
- represented by a matrix-like object of numeric or other mode. The rows
- typically represent genomic ranges of interest and the columns
- represent samples.
diff --git a/debian/copyright b/debian/copyright
deleted file mode 100644
index 360a81e..0000000
--- a/debian/copyright
+++ /dev/null
@@ -1,107 +0,0 @@
-Format: https://www.debian.org/doc/packaging-manuals/copyright-format/1.0/
-Upstream-Name: SummarizedExperiment
-Upstream-Contact: Bioconductor Package Maintainer <maintainer at bioconductor.org>
-Source: https://bioconductor.org/packages/SummarizedExperiment/
-
-Files: *
-Copyright: © 2014-2016 Martin Morgan, Valerie Obenchain, Jim Hester, Hervé Pagès
-License: Artistic-2.0
-
-
-Files: debian/*
-Copyright: 2015-2016 Andreas Tille <tille at debian.org>
-License: Artistic-2.0
-
-License: Artistic-2.0
- The "Artistic License"
- .
- Preamble
- .
- 1. You may make and give away verbatim copies of the source form of the
- Standard Version of this Package without restriction, provided that
- you duplicate all of the original copyright notices and associated
- disclaimers.
- .
- 2. You may apply bug fixes, portability fixes and other modifications
- derived from the Public Domain or from the Copyright Holder. A
- Package modified in such a way shall still be considered the Standard
- Version.
- .
- 3. You may otherwise modify your copy of this Package in any way,
- provided that you insert a prominent notice in each changed file stating
- how and when you changed that file, and provided that you do at least
- ONE of the following:
- .
- a) place your modifications in the Public Domain or otherwise make them
- Freely Available, such as by posting said modifications to Usenet or
- an equivalent medium, or placing the modifications on a major archive
- site such as uunet.uu.net, or by allowing the Copyright Holder to include
- your modifications in the Standard Version of the Package.
- .
- b) use the modified Package only within your corporation or organization.
- .
- c) rename any non-standard executables so the names do not conflict
- with standard executables, which must also be provided, and provide
- a separate manual page for each non-standard executable that clearly
- documents how it differs from the Standard Version.
- .
- d) make other distribution arrangements with the Copyright Holder.
- .
- 4. You may distribute the programs of this Package in object code or
- executable form, provided that you do at least ONE of the following:
- .
- a) distribute a Standard Version of the executables and library files,
- together with instructions (in the manual page or equivalent) on where
- to get the Standard Version.
- .
- b) accompany the distribution with the machine-readable source of
- the Package with your modifications.
- .
- c) give non-standard executables non-standard names, and clearly
- document the differences in manual pages (or equivalent), together
- with instructions on where to get the Standard Version.
- .
- d) make other distribution arrangements with the Copyright Holder.
- .
- 5. You may charge a reasonable copying fee for any distribution of this
- Package. You may charge any fee you choose for support of this Package.
- You may not charge a fee for this Package itself. However, you may
- distribute this Package in aggregate with other (possibly commercial)
- programs as part of a larger (possibly commercial) software distribution
- provided that you do not advertise this Package as a product of your
- own. You may embed this Package's interpreter within an executable of
- yours (by linking); this shall be construed as a mere form of
- aggregation, provided that the complete Standard Version of the
- interpreter is so embedded.
- .
- 6. The scripts and library files supplied as input to or produced as
- output from the programs of this Package do not automatically fall under
- the copyright of this Package, but belong to whoever generated them, and
- may be sold commercially, and may be aggregated with this Package. If
- such scripts or library files are aggregated with this Package via the
- so-called "undump" or "unexec" methods of producing a binary executable
- image, then distribution of such an image shall neither be construed as
- a distribution of this Package nor shall it fall under the restrictions
- of Paragraphs 3 and 4, provided that you do not represent such an
- executable image as a Standard Version of this Package.
- .
- 7. C subroutines (or comparably compiled subroutines in other
- languages) supplied by you and linked into this Package in order to
- emulate subroutines and variables of the language defined by this
- Package shall not be considered part of this Package, but are the
- equivalent of input as in Paragraph 6, provided these subroutines do
- not change the language in any way that would cause it to fail the
- regression tests for the language.
- .
- 8. Aggregation of this Package with a commercial distribution is always
- permitted provided that the use of this Package is embedded; that is,
- when no overt attempt is made to make this Package's interfaces visible
- to the end user of the commercial distribution. Such use shall not be
- construed as a distribution of this Package.
- .
- 9. The name of the Copyright Holder may not be used to endorse or promote
- products derived from this software without specific prior written permission.
- .
- 10. THIS PACKAGE IS PROVIDED "AS IS" AND WITHOUT ANY EXPRESS OR
- IMPLIED WARRANTIES, INCLUDING, WITHOUT LIMITATION, THE IMPLIED
- WARRANTIES OF MERCHANTIBILITY AND FITNESS FOR A PARTICULAR PURPOSE.
diff --git a/debian/docs b/debian/docs
deleted file mode 100644
index 50f6656..0000000
--- a/debian/docs
+++ /dev/null
@@ -1 +0,0 @@
-debian/README.test
diff --git a/debian/patches/series b/debian/patches/series
deleted file mode 100644
index af56ecc..0000000
--- a/debian/patches/series
+++ /dev/null
@@ -1 +0,0 @@
-skip_test_needing_not_packaged_database.patch
diff --git a/debian/patches/skip_test_needing_not_packaged_database.patch b/debian/patches/skip_test_needing_not_packaged_database.patch
deleted file mode 100644
index 639d1db..0000000
--- a/debian/patches/skip_test_needing_not_packaged_database.patch
+++ /dev/null
@@ -1,54 +0,0 @@
-Description: Skip test needing unpackaged database
-Author: Andreas Tille <tille at debian.org>
-Last-Update: Tue, 03 May 2016 20:27:21 +0200
-
---- a/inst/unitTests/test_makeSummarizedExperimentFromExpressionSet.R
-+++ b/inst/unitTests/test_makeSummarizedExperimentFromExpressionSet.R
-@@ -141,47 +141,3 @@ test_GenomicRanges_SummarizedExperiment_
- sampleNames(eset5))
- }
-
--test_GenomicRanges_SummarizedExperiment_coercion_mappingFunctions <- function()
--{
-- ## naiveRangeMapper
-- ## valid object from empty object
-- checkTrue(validObject(makeSummarizedExperimentFromExpressionSet(ExpressionSet())))
--
-- ## valid object from sample ExpressionSet
-- data("sample.ExpressionSet", package = "Biobase")
-- eset1 <- sample.ExpressionSet
-- checkTrue(validObject(makeSummarizedExperimentFromExpressionSet(eset1)))
--
-- ## makeSummarizedExperimentFromExpressionSet should be the same as `as`
-- ## with default args
-- checkEquals(makeSummarizedExperimentFromExpressionSet(eset1),
-- as(eset1, "RangedSummarizedExperiment"))
--
-- ## probeRangeMapper
-- ## valid object from empty object
-- checkTrue(validObject(
-- makeSummarizedExperimentFromExpressionSet(ExpressionSet(),
-- probeRangeMapper)))
--
-- ## valid object from sample ExpressionSet
-- se1 <- makeSummarizedExperimentFromExpressionSet(eset1, probeRangeMapper)
-- checkTrue(validObject(se1))
--
-- ## Granges returned have rownames that were from the featureNames
-- checkTrue(all(rownames(rowRanges(se1)) %in% featureNames(eset1)))
--
-- ## geneRangeMapper
-- ## valid object from empty object
-- checkTrue(validObject(
-- makeSummarizedExperimentFromExpressionSet(ExpressionSet(),
-- geneRangeMapper(NULL))))
--
-- ## valid object from sample ExpressionSet
-- se2 <- makeSummarizedExperimentFromExpressionSet(eset1,
-- geneRangeMapper("TxDb.Hsapiens.UCSC.hg19.knownGene"))
-- checkTrue(validObject(se2))
--
-- ## Granges returned have rownames that were from the featureNames
-- checkTrue(all(rownames(rowRanges(se2)) %in% featureNames(eset1)))
--}
--
diff --git a/debian/rules b/debian/rules
deleted file mode 100755
index 68d9a36..0000000
--- a/debian/rules
+++ /dev/null
@@ -1,4 +0,0 @@
-#!/usr/bin/make -f
-
-%:
- dh $@ --buildsystem R
diff --git a/debian/source/format b/debian/source/format
deleted file mode 100644
index 163aaf8..0000000
--- a/debian/source/format
+++ /dev/null
@@ -1 +0,0 @@
-3.0 (quilt)
diff --git a/debian/tests/control b/debian/tests/control
deleted file mode 100644
index 768f587..0000000
--- a/debian/tests/control
+++ /dev/null
@@ -1,3 +0,0 @@
-Tests: run-unit-test
-Depends: @, r-cran-runit, r-cran-digest, r-bioc-annotate
-Restrictions: allow-stderr
diff --git a/debian/tests/run-unit-test b/debian/tests/run-unit-test
deleted file mode 100644
index b5e9276..0000000
--- a/debian/tests/run-unit-test
+++ /dev/null
@@ -1,6 +0,0 @@
-#!/bin/sh -e
-
-LC_ALL=C R --no-save <<EOT
-SummarizedExperiment:::.test()
-EOT
-
diff --git a/debian/watch b/debian/watch
deleted file mode 100644
index 9eacbd4..0000000
--- a/debian/watch
+++ /dev/null
@@ -1,3 +0,0 @@
-version=4
-opts=downloadurlmangle=s?^(.*)\.\.?http:$1packages/release/bioc? \
- http://www.bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html .*/SummarizedExperiment_([\d\.]+)\.tar\.gz
diff --git a/inst/doc/SummarizedExperiment.R b/inst/doc/SummarizedExperiment.R
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+## ----style, echo=FALSE, results='asis'-----------------------------------
+BiocStyle::markdown()
+
+## ----include = FALSE-----------------------------------------------------
+# download current version of SE diagram
+#download.file("https://docs.google.com/feeds/download/drawings/Export?id=18OcDb80FpvSGRYnFl-8vUqwNNLaNHrG1I9SWKHCselo&exportFormat=svg", "SE.svg")
+download.file("https://docs.google.com/feeds/download/drawings/Export?id=1kiC8Qlo1mhSnLDqkGiRNPSo6GWn3C2duBszCFbJCB-g&exportFormat=svg", "SE.svg")
+
+## ---- echo=FALSE---------------------------------------------------------
+suppressPackageStartupMessages(library(SummarizedExperiment))
+suppressPackageStartupMessages(data(airway, package="airway"))
+
+## ------------------------------------------------------------------------
+library(SummarizedExperiment)
+data(airway, package="airway")
+se <- airway
+se
+
+## ----assays, eval = FALSE------------------------------------------------
+# assays(se)$counts
+
+## ----assays_table, echo = FALSE------------------------------------------
+knitr::kable(assays(se)$counts[1:10,])
+
+## ----rowRanges-----------------------------------------------------------
+rowRanges(se)
+
+## ----colData-------------------------------------------------------------
+colData(se)
+
+## ----columnSubset--------------------------------------------------------
+# subset for only those samples treated with dexamethasone
+se[, se$dex == "trt"]
+
+## ----metadata------------------------------------------------------------
+metadata(se)
+
+## ----metadata-formula----------------------------------------------------
+metadata(se)$formula <- counts ~ dex + albut
+
+metadata(se)
+
+## ----constructRSE--------------------------------------------------------
+nrows <- 200
+ncols <- 6
+counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
+rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),
+ IRanges(floor(runif(200, 1e5, 1e6)), width=100),
+ strand=sample(c("+", "-"), 200, TRUE),
+ feature_id=sprintf("ID%03d", 1:200))
+colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
+ row.names=LETTERS[1:6])
+
+SummarizedExperiment(assays=list(counts=counts),
+ rowRanges=rowRanges, colData=colData)
+
+## ----constructSE---------------------------------------------------------
+SummarizedExperiment(assays=list(counts=counts), colData=colData)
+
+## ----2d------------------------------------------------------------------
+# subset the first five transcripts and first three samples
+se[1:5, 1:3]
+
+## ----colDataExtraction---------------------------------------------------
+se[, se$cell == "N61311"]
+
+## ----getSet--------------------------------------------------------------
+counts <- matrix(1:15, 5, 3, dimnames=list(LETTERS[1:5], LETTERS[1:3]))
+
+dates <- SummarizedExperiment(assays=list(counts=counts),
+ rowData=DataFrame(month=month.name[1:5], day=1:5))
+
+# Subset all January assays
+dates[rowData(dates)$month == "January", ]
+
+## ----assay_assays--------------------------------------------------------
+assays(se)
+
+assays(se)[[1]][1:5, 1:5]
+
+# assay defaults to the first assay if no i is given
+assay(se)[1:5, 1:5]
+
+assay(se, 1)[1:5, 1:5]
+
+## ----overlap-------------------------------------------------------------
+# Subset for only rows which are in the interval 100,000 to 110,000 of
+# chromosome 1
+roi <- GRanges(seqnames="1", ranges=100000:1100000)
+subsetByOverlaps(se, roi)
+
+## ----rseSubclass---------------------------------------------------------
+setClass("MyRSESubclass",
+ contains="RangedSummarizedExperiment",
+ representation=representation(
+ slot1="integer",
+ slot2="function"
+ ## ... maybe more ...
+ )
+)
+
diff --git a/inst/doc/SummarizedExperiment.Rmd b/inst/doc/SummarizedExperiment.Rmd
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+---
+title: "_SummarizedExperiment_ for Coordinating Experimental Assays, Samples, and Regions of Interest"
+author: "Martin Morgan, Valerie Obenchain, Jim Hester, Hervé Pagès"
+date: "Revised: 22 June, 2016"
+output:
+ BiocStyle::html_document:
+ toc: true
+vignette: >
+ %\VignetteIndexEntry{SummarizedExperiment}
+ %\VignetteEngine{knitr::rmarkdown}
+ \usepackage[utf8]{inputenc}
+---
+
+```{r style, echo=FALSE, results='asis'}
+BiocStyle::markdown()
+```
+
+
+# Introduction
+
+The `SummarizedExperiment` class is used to store rectangular matrices of
+experimental results, which are commonly produced by sequencing and microarray
+experiments. Each object stores observations of one or more samples, along
+with additional meta-data describing both the observations (features) and
+samples (phenotypes).
+
+A key aspect of the `SummarizedExperiment` class is the coordination of the
+meta-data and assays when subsetting. For example, if you want to exclude a
+given sample you can do for both the meta-data and assay in one operation,
+which ensures the meta-data and observed data will remain in sync. Improperly
+accounting for meta and observational data has resulted in a number of
+incorrect results and retractions so this is a very desirable
+property.
+
+`SummarizedExperiment` is in many ways similar to the historical
+`ExpressionSet`, the main distinction being that `SummarizedExperiment` is more
+flexible in it's row information, allowing both `GRanges` based as well as those
+described by arbitrary `DataFrame`s. This makes it ideally suited to a variety
+of experiments, particularly sequencing based experiments such as RNA-Seq and
+ChIp-Seq.
+
+# Anatomy of a `SummarizedExperiment`
+
+The _SummarizedExperiment_ package contains two classes:
+`SummarizedExperiment` and `RangedSummarizedExperiment`.
+
+`SummarizedExperiment` is a matrix-like container where rows represent features
+of interest (e.g. genes, transcripts, exons, etc.) and columns represent
+samples. The objects contain one or more assays, each represented by a
+matrix-like object of numeric or other mode. The rows of a
+`SummarizedExperiment` object represent features of interest. Information
+about these features is stored in a `DataFrame` object, accessible using the
+function `rowData()`. Each row of the `DataFrame` provides information on the
+feature in the corresponding row of the `SummarizedExperiment` object. Columns
+of the DataFrame represent different attributes of the features of interest,
+e.g., gene or transcript IDs, etc.
+
+`RangedSummarizedExperiment` is the child of the `SummarizedExperiment` class
+which means that all the methods on `SummarizedExperiment` also work on a
+`RangedSummarizedExperiment`.
+
+The fundamental difference between the two classes is that the rows of a
+`RangedSummarizedExperiment` object represent genomic ranges of interest
+instead of a `DataFrame` of features. The `RangedSummarizedExperiment` ranges
+are described by a `GRanges` or a `GRangesList` object, accessible using the
+`rowRanges()` function.
+
+The following graphic displays the class geometry and highlights the
+vertical (column) and horizontal (row) relationships.
+
+
+```{r include = FALSE}
+# download current version of SE diagram
+#download.file("https://docs.google.com/feeds/download/drawings/Export?id=18OcDb80FpvSGRYnFl-8vUqwNNLaNHrG1I9SWKHCselo&exportFormat=svg", "SE.svg")
+download.file("https://docs.google.com/feeds/download/drawings/Export?id=1kiC8Qlo1mhSnLDqkGiRNPSo6GWn3C2duBszCFbJCB-g&exportFormat=svg", "SE.svg")
+```
+
+
+
+## Assays
+
+The `airway` package contains an example dataset from an RNA-Seq experiment of
+read counts per gene for airway smooth muscles. These data are stored
+in a `RangedSummarizedExperiment` object which contains 8 different
+experimental and assays 64,102 gene transcripts.
+
+```{r, echo=FALSE}
+suppressPackageStartupMessages(library(SummarizedExperiment))
+suppressPackageStartupMessages(data(airway, package="airway"))
+```
+
+```{r}
+library(SummarizedExperiment)
+data(airway, package="airway")
+se <- airway
+se
+```
+
+To retrieve the experiment data from a `SummarizedExperiment` object one can
+use the `assays()` accessor. An object can have multiple assay datasets
+each of which can be accessed using the `$` operator.
+The `airway` dataset contains only one assay (`counts`). Here each row
+represents a gene transcript and each column one of the samples.
+
+```{r assays, eval = FALSE}
+assays(se)$counts
+```
+
+```{r assays_table, echo = FALSE}
+knitr::kable(assays(se)$counts[1:10,])
+```
+
+## 'Row' (regions-of-interest) data
+The `rowRanges()` accessor is used to view the range information for a
+`RangedSummarizedExperiment`. (Note if this were the parent
+`SummarizedExperiment` class we'd use `rowData()`). The data are stored in a
+`GRangesList` object, where each list element corresponds to one gene
+transcript and the ranges in each `GRanges` correspond to the exons in the
+transcript.
+
+```{r rowRanges}
+rowRanges(se)
+```
+
+## 'Column' (sample) data
+
+Sample meta-data describing the samples can be accessed using `colData()`, and
+is a `DataFrame` that can store any number of descriptive columns for each
+sample row.
+
+```{r colData}
+colData(se)
+```
+
+This sample metadata can be accessed using the `$` accessor which makes it
+easy to subset the entire object by a given phenotype.
+
+```{r columnSubset}
+# subset for only those samples treated with dexamethasone
+se[, se$dex == "trt"]
+```
+
+## Experiment-wide metadata
+
+Meta-data describing the experimental methods and publication references can be
+accessed using `metadata()`.
+
+```{r metadata}
+metadata(se)
+```
+
+Note that `metadata()` is just a simple list, so it is appropriate for _any_
+experiment wide metadata the user wishes to save, such as storing model
+formulas.
+
+```{r metadata-formula}
+metadata(se)$formula <- counts ~ dex + albut
+
+metadata(se)
+```
+
+# Constructing a `SummarizedExperiment`
+
+Often, `SummarizedExperiment` or `RangedSummarizedExperiment` objects are
+returned by functions written by other packages. However it is possible to
+create them by hand with a call to the `SummarizedExperiment()` constructor.
+
+Constructing a `RangedSummarizedExperiment` with a `GRanges` as the
+_rowRanges_ argument:
+
+```{r constructRSE}
+nrows <- 200
+ncols <- 6
+counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
+rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),
+ IRanges(floor(runif(200, 1e5, 1e6)), width=100),
+ strand=sample(c("+", "-"), 200, TRUE),
+ feature_id=sprintf("ID%03d", 1:200))
+colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
+ row.names=LETTERS[1:6])
+
+SummarizedExperiment(assays=list(counts=counts),
+ rowRanges=rowRanges, colData=colData)
+```
+
+A `SummarizedExperiment` can be constructed with or without supplying
+a `DataFrame` for the _rowData_ argument:
+
+```{r constructSE}
+SummarizedExperiment(assays=list(counts=counts), colData=colData)
+```
+
+# Common operations on `SummarizedExperiment`
+
+## Subsetting
+
+- `[` Performs two dimensional subsetting, just like subsetting a matrix
+ or data frame.
+```{r 2d}
+# subset the first five transcripts and first three samples
+se[1:5, 1:3]
+```
+- `$` operates on `colData()` columns, for easy sample extraction.
+```{r colDataExtraction}
+se[, se$cell == "N61311"]
+```
+
+## Getters and setters
+
+- `rowRanges()` / (`rowData()`), `colData()`, `metadata()`
+```{r getSet}
+counts <- matrix(1:15, 5, 3, dimnames=list(LETTERS[1:5], LETTERS[1:3]))
+
+dates <- SummarizedExperiment(assays=list(counts=counts),
+ rowData=DataFrame(month=month.name[1:5], day=1:5))
+
+# Subset all January assays
+dates[rowData(dates)$month == "January", ]
+```
+
+- `assay()` versus `assays()`
+There are two accessor functions for extracting the assay data from a
+`SummarizedExperiment` object. `assays()` operates on the entire list of assay
+data as a whole, while `assay()` operates on only one assay at a time.
+`assay(x, i)` is simply a convenience function which is equivalent to
+`assays(x)[[i]]`.
+
+```{r assay_assays}
+assays(se)
+
+assays(se)[[1]][1:5, 1:5]
+
+# assay defaults to the first assay if no i is given
+assay(se)[1:5, 1:5]
+
+assay(se, 1)[1:5, 1:5]
+```
+
+## Range-based operations
+
+- `subsetByOverlaps()`
+`SummarizedExperiment` objects support all of the `findOverlaps()` methods and
+associated functions. This includes `subsetByOverlaps()`, which makes it easy
+to subset a `SummarizedExperiment` object by an interval.
+
+```{r overlap}
+# Subset for only rows which are in the interval 100,000 to 110,000 of
+# chromosome 1
+roi <- GRanges(seqnames="1", ranges=100000:1100000)
+subsetByOverlaps(se, roi)
+```
+
+# Advanced: Extending `RangedSummarizedExperiment`
+
+For representing and manipulating data in their own package, Bioconductor
+developers are encouraged to re-use existing classes defined in other
+packages like the `RangedSummarizedExperiment` or `GRanges` containers
+defined in the `SummarizedExperiment` or `GenomicRanges` infrastructure
+packages, respectively. Many Bioconductor packages are designed around
+these basic containers, that is, they define functions that take and/or
+return a `RangedSummarizedExperiment` or `GRanges` object.
+For example the `csaw` package defines various functions that operate
+on `RangedSummarizedExperiment` objects, which are used to represent the
+number of ChIP-seq reads from each BAM file overlapping pre-specified
+regions.
+
+However, sometimes re-using the `RangedSummarizedExperiment` class as-is
+does not satisfy the needs of the package and the developer makes the choice
+to extend the class in order to accomodate the special needs of the package
+and/or the specificities of the data that it deals with. For example the
+`DESeq2` package defines the `DESeqDataSet` class which extends
+`RangedSummarizedExperiment` to add the `design` and `dispersionFunction`
+slots to it.
+
+The following subsections describe in a nutshell how the developer would
+typically proceed for extending `RangedSummarizedExperiment` in his/her
+own package. Some familiarity with the S4 class system is required. Readers
+not familiar with the S4 class system are encouraged to consult the vignette
+`A quick overview of the S4 class system` located in the `S4Vectors` package
+for the basics of implementing and extending S4 classes.
+
+The approach described below allows the developer to extend
+`RangedSummarizedExperiment` it in a way that remains agnostic of its
+internals. Keeping this separation between the responsibilities of the owners
+of the parent and child classes facilitate maintenance in the long run.
+In particular, the implementation of the child class won't be affected by
+changes in the internals of the parent class.
+
+## Depend on, and import, the `SummarizedExperiment` package
+
+Add `SummarizedExperiment` to the Depends field of the DESCRIPTION file
+of the package and the `import(SummarizedExperiment)` directive to its
+NAMESPACE file.
+
+## Define and export the `RangedSummarizedExperiment` subclass
+
+Define the subclass with something like:
+```{r rseSubclass}
+setClass("MyRSESubclass",
+ contains="RangedSummarizedExperiment",
+ representation=representation(
+ slot1="integer",
+ slot2="function"
+ ## ... maybe more ...
+ )
+)
+```
+
+Export it by adding the `exportClasses(MyRSESubclass)` directive to the
+NAMESPACE file.
+
+## Construct `MyRSESubclass` instances
+
+When calling `new()` for constructing a `MyRSESubclass` instance, specify
+only the MyRSESubclass-specific slots:
+`new("MyRSESubclass", rse, slot1=value1, slot2=value2)`,
+where `rse` is a `RangedSummarizedExperiment` object.
+
+Providing a `MyRSESubclass` constructor function (named as the class itself)
+is recommended.
+
+## Define a validity method
+
+The validity method for `MyRSESubclass` only needs to take care of what's
+new in `MyRSESubclass` with respect to `RangedSummarizedExperiment`, that is,
+of the aspects of `MyRSESubclass` objects that are not already covered by the
+validity method for `RangedSummarizedExperiment` objects. This is because
+calling `validObject()` on a `MyRSESubclass` object automatically validates
+it as a `RangedSummarizedExperiment` object first and then calls the validity
+method for `MyRSESubclass` objects. In other words, validation works
+incrementally starting from the root of the class hierarchy and going in the
+parent-to-child direction.
+
+## Use the `RangedSummarizedExperiment` accessors on `MyRSESubclass` objects
+
+Like any user of `RangedSummarizedExperiment` objects, the developer of
+`MyRSESubclass` should always use the `RangedSummarizedExperiment` accessors
+to access the `RangedSummarizedExperiment`-specific parts of his/her
+`MyRSESubclass` objects.
+
+## Use `callNextMethod`
+
+In case some of the methods defined for `RangedSummarizedExperiment` objects
+need to be overwritten, the new methods should call `callNextMethod`
+internally.
diff --git a/inst/doc/SummarizedExperiment.html b/inst/doc/SummarizedExperiment.html
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+<div id="header">
+<h1 class="title"><em>SummarizedExperiment</em> for Coordinating Experimental Assays, Samples, and Regions of Interest</h1>
+<h4 class="author"><em>Martin Morgan, Valerie Obenchain, Jim Hester, Hervé Pagès</em></h4>
+<h4 class="date"><em>Revised: 22 June, 2016</em></h4>
+</div>
+
+<h1>Contents</h1>
+<div id="TOC">
+<ul>
+<li><a href="#introduction"><span class="toc-section-number">1</span> Introduction</a></li>
+<li><a href="#anatomy-of-a-summarizedexperiment"><span class="toc-section-number">2</span> Anatomy of a <code>SummarizedExperiment</code></a><ul>
+<li><a href="#assays"><span class="toc-section-number">2.1</span> Assays</a></li>
+<li><a href="#row-regions-of-interest-data"><span class="toc-section-number">2.2</span> ‘Row’ (regions-of-interest) data</a></li>
+<li><a href="#column-sample-data"><span class="toc-section-number">2.3</span> ‘Column’ (sample) data</a></li>
+<li><a href="#experiment-wide-metadata"><span class="toc-section-number">2.4</span> Experiment-wide metadata</a></li>
+</ul></li>
+<li><a href="#constructing-a-summarizedexperiment"><span class="toc-section-number">3</span> Constructing a <code>SummarizedExperiment</code></a></li>
+<li><a href="#common-operations-on-summarizedexperiment"><span class="toc-section-number">4</span> Common operations on <code>SummarizedExperiment</code></a><ul>
+<li><a href="#subsetting"><span class="toc-section-number">4.1</span> Subsetting</a></li>
+<li><a href="#getters-and-setters"><span class="toc-section-number">4.2</span> Getters and setters</a></li>
+<li><a href="#range-based-operations"><span class="toc-section-number">4.3</span> Range-based operations</a></li>
+</ul></li>
+<li><a href="#advanced-extending-rangedsummarizedexperiment"><span class="toc-section-number">5</span> Advanced: Extending <code>RangedSummarizedExperiment</code></a><ul>
+<li><a href="#depend-on-and-import-the-summarizedexperiment-package"><span class="toc-section-number">5.1</span> Depend on, and import, the <code>SummarizedExperiment</code> package</a></li>
+<li><a href="#define-and-export-the-rangedsummarizedexperiment-subclass"><span class="toc-section-number">5.2</span> Define and export the <code>RangedSummarizedExperiment</code> subclass</a></li>
+<li><a href="#construct-myrsesubclass-instances"><span class="toc-section-number">5.3</span> Construct <code>MyRSESubclass</code> instances</a></li>
+<li><a href="#define-a-validity-method"><span class="toc-section-number">5.4</span> Define a validity method</a></li>
+<li><a href="#use-the-rangedsummarizedexperiment-accessors-on-myrsesubclass-objects"><span class="toc-section-number">5.5</span> Use the <code>RangedSummarizedExperiment</code> accessors on <code>MyRSESubclass</code> objects</a></li>
+<li><a href="#use-callnextmethod"><span class="toc-section-number">5.6</span> Use <code>callNextMethod</code></a></li>
+</ul></li>
+</ul>
+</div>
+
+<script type="text/javascript">
+document.addEventListener("DOMContentLoaded", function() {
+ document.querySelector("h1").className = "title";
+});
+</script>
+<script type="text/javascript">
+document.addEventListener("DOMContentLoaded", function() {
+ var links = document.links;
+ for (var i = 0, linksLength = links.length; i < linksLength; i++)
+ if (links[i].hostname != window.location.hostname)
+ links[i].target = '_blank';
+});
+</script>
+<div id="introduction" class="section level1">
+<h1><span class="header-section-number">1</span> Introduction</h1>
+<p>The <code>SummarizedExperiment</code> class is used to store rectangular matrices of experimental results, which are commonly produced by sequencing and microarray experiments. Each object stores observations of one or more samples, along with additional meta-data describing both the observations (features) and samples (phenotypes).</p>
+<p>A key aspect of the <code>SummarizedExperiment</code> class is the coordination of the meta-data and assays when subsetting. For example, if you want to exclude a given sample you can do for both the meta-data and assay in one operation, which ensures the meta-data and observed data will remain in sync. Improperly accounting for meta and observational data has resulted in a number of incorrect results and retractions so this is a very desirable property.</p>
+<p><code>SummarizedExperiment</code> is in many ways similar to the historical <code>ExpressionSet</code>, the main distinction being that <code>SummarizedExperiment</code> is more flexible in it’s row information, allowing both <code>GRanges</code> based as well as those described by arbitrary <code>DataFrame</code>s. This makes it ideally suited to a variety of experiments, particularly sequencing based experiments such as RNA-Seq and ChIp-Seq.</p>
+</div>
+<div id="anatomy-of-a-summarizedexperiment" class="section level1">
+<h1><span class="header-section-number">2</span> Anatomy of a <code>SummarizedExperiment</code></h1>
+<p>The <em>SummarizedExperiment</em> package contains two classes: <code>SummarizedExperiment</code> and <code>RangedSummarizedExperiment</code>.</p>
+<p><code>SummarizedExperiment</code> is a matrix-like container where rows represent features of interest (e.g. genes, transcripts, exons, etc.) and columns represent samples. The objects contain one or more assays, each represented by a matrix-like object of numeric or other mode. The rows of a <code>SummarizedExperiment</code> object represent features of interest. Information about these features is stored in a <code>DataFrame</code> object, accessible using the function <code>rowData [...]
+<p><code>RangedSummarizedExperiment</code> is the child of the <code>SummarizedExperiment</code> class which means that all the methods on <code>SummarizedExperiment</code> also work on a <code>RangedSummarizedExperiment</code>.</p>
+<p>The fundamental difference between the two classes is that the rows of a <code>RangedSummarizedExperiment</code> object represent genomic ranges of interest instead of a <code>DataFrame</code> of features. The <code>RangedSummarizedExperiment</code> ranges are described by a <code>GRanges</code> or a <code>GRangesList</code> object, accessible using the <code>rowRanges()</code> function.</p>
+<p>The following graphic displays the class geometry and highlights the vertical (column) and horizontal (row) relationships.</p>
+<p><img src="data:image/svg+xml;base64,PD94bWwgdmVyc2lvbj0iMS4wIiBzdGFuZGFsb25lPSJ5ZXMiPz4KCjxzdmcgdmVyc2lvbj0iMS4xIiB2aWV3Qm94PSIwLjAgMC4wIDcwNS41NTM4MDU3NzQyNzgyIDYwMi4xMTU0ODU1NjQzMDQ0IiBmaWxsPSJub25lIiBzdHJva2U9Im5vbmUiIHN0cm9rZS1saW5lY2FwPSJzcXVhcmUiIHN0cm9rZS1taXRlcmxpbWl0PSIxMCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIiB4bWxuczp4bGluaz0iaHR0cDovL3d3dy53My5vcmcvMTk5OS94bGluayI+PGNsaXBQYXRoIGlkPSJwLjAiPjxwYXRoIGQ9Im0wIDBsNzA1LjU1MzgzIDBsMCA2MDIuMTE1NWwtNzA1LjU1MzgzIDBsMCAtNjAyL [...]
+<div id="assays" class="section level2">
+<h2><span class="header-section-number">2.1</span> Assays</h2>
+<p>The <code>airway</code> package contains an example dataset from an RNA-Seq experiment of read counts per gene for airway smooth muscles. These data are stored in a <code>RangedSummarizedExperiment</code> object which contains 8 different experimental and assays 64,102 gene transcripts.</p>
+<pre class="r"><code>library(SummarizedExperiment)
+data(airway, package="airway")
+se <- airway
+se</code></pre>
+<pre><code>## class: RangedSummarizedExperiment
+## dim: 64102 8
+## metadata(1): ''
+## assays(1): counts
+## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
+## rowData names(0):
+## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
+## colData names(9): SampleName cell ... Sample BioSample</code></pre>
+<p>To retrieve the experiment data from a <code>SummarizedExperiment</code> object one can use the <code>assays()</code> accessor. An object can have multiple assay datasets each of which can be accessed using the <code>$</code> operator. The <code>airway</code> dataset contains only one assay (<code>counts</code>). Here each row represents a gene transcript and each column one of the samples.</p>
+<pre class="r"><code>assays(se)$counts</code></pre>
+<table>
+<thead>
+<tr class="header">
+<th></th>
+<th align="right">SRR1039508</th>
+<th align="right">SRR1039509</th>
+<th align="right">SRR1039512</th>
+<th align="right">SRR1039513</th>
+<th align="right">SRR1039516</th>
+<th align="right">SRR1039517</th>
+<th align="right">SRR1039520</th>
+<th align="right">SRR1039521</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td>ENSG00000000003</td>
+<td align="right">679</td>
+<td align="right">448</td>
+<td align="right">873</td>
+<td align="right">408</td>
+<td align="right">1138</td>
+<td align="right">1047</td>
+<td align="right">770</td>
+<td align="right">572</td>
+</tr>
+<tr class="even">
+<td>ENSG00000000005</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+</tr>
+<tr class="odd">
+<td>ENSG00000000419</td>
+<td align="right">467</td>
+<td align="right">515</td>
+<td align="right">621</td>
+<td align="right">365</td>
+<td align="right">587</td>
+<td align="right">799</td>
+<td align="right">417</td>
+<td align="right">508</td>
+</tr>
+<tr class="even">
+<td>ENSG00000000457</td>
+<td align="right">260</td>
+<td align="right">211</td>
+<td align="right">263</td>
+<td align="right">164</td>
+<td align="right">245</td>
+<td align="right">331</td>
+<td align="right">233</td>
+<td align="right">229</td>
+</tr>
+<tr class="odd">
+<td>ENSG00000000460</td>
+<td align="right">60</td>
+<td align="right">55</td>
+<td align="right">40</td>
+<td align="right">35</td>
+<td align="right">78</td>
+<td align="right">63</td>
+<td align="right">76</td>
+<td align="right">60</td>
+</tr>
+<tr class="even">
+<td>ENSG00000000938</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">2</td>
+<td align="right">0</td>
+<td align="right">1</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+</tr>
+<tr class="odd">
+<td>ENSG00000000971</td>
+<td align="right">3251</td>
+<td align="right">3679</td>
+<td align="right">6177</td>
+<td align="right">4252</td>
+<td align="right">6721</td>
+<td align="right">11027</td>
+<td align="right">5176</td>
+<td align="right">7995</td>
+</tr>
+<tr class="even">
+<td>ENSG00000001036</td>
+<td align="right">1433</td>
+<td align="right">1062</td>
+<td align="right">1733</td>
+<td align="right">881</td>
+<td align="right">1424</td>
+<td align="right">1439</td>
+<td align="right">1359</td>
+<td align="right">1109</td>
+</tr>
+<tr class="odd">
+<td>ENSG00000001084</td>
+<td align="right">519</td>
+<td align="right">380</td>
+<td align="right">595</td>
+<td align="right">493</td>
+<td align="right">820</td>
+<td align="right">714</td>
+<td align="right">696</td>
+<td align="right">704</td>
+</tr>
+<tr class="even">
+<td>ENSG00000001167</td>
+<td align="right">394</td>
+<td align="right">236</td>
+<td align="right">464</td>
+<td align="right">175</td>
+<td align="right">658</td>
+<td align="right">584</td>
+<td align="right">360</td>
+<td align="right">269</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div id="row-regions-of-interest-data" class="section level2">
+<h2><span class="header-section-number">2.2</span> ‘Row’ (regions-of-interest) data</h2>
+<p>The <code>rowRanges()</code> accessor is used to view the range information for a <code>RangedSummarizedExperiment</code>. (Note if this were the parent <code>SummarizedExperiment</code> class we’d use <code>rowData()</code>). The data are stored in a <code>GRangesList</code> object, where each list element corresponds to one gene transcript and the ranges in each <code>GRanges</code> correspond to the exons in the transcript.</p>
+<pre class="r"><code>rowRanges(se)</code></pre>
+<pre><code>## GRangesList object of length 64102:
+## $ENSG00000000003
+## GRanges object with 17 ranges and 2 metadata columns:
+## seqnames ranges strand | exon_id exon_name
+## <Rle> <IRanges> <Rle> | <integer> <character>
+## [1] X [99883667, 99884983] - | 667145 ENSE00001459322
+## [2] X [99885756, 99885863] - | 667146 ENSE00000868868
+## [3] X [99887482, 99887565] - | 667147 ENSE00000401072
+## [4] X [99887538, 99887565] - | 667148 ENSE00001849132
+## [5] X [99888402, 99888536] - | 667149 ENSE00003554016
+## ... ... ... ... . ... ...
+## [13] X [99890555, 99890743] - | 667156 ENSE00003512331
+## [14] X [99891188, 99891686] - | 667158 ENSE00001886883
+## [15] X [99891605, 99891803] - | 667159 ENSE00001855382
+## [16] X [99891790, 99892101] - | 667160 ENSE00001863395
+## [17] X [99894942, 99894988] - | 667161 ENSE00001828996
+##
+## ...
+## <64101 more elements>
+## -------
+## seqinfo: 722 sequences (1 circular) from an unspecified genome</code></pre>
+</div>
+<div id="column-sample-data" class="section level2">
+<h2><span class="header-section-number">2.3</span> ‘Column’ (sample) data</h2>
+<p>Sample meta-data describing the samples can be accessed using <code>colData()</code>, and is a <code>DataFrame</code> that can store any number of descriptive columns for each sample row.</p>
+<pre class="r"><code>colData(se)</code></pre>
+<pre><code>## DataFrame with 8 rows and 9 columns
+## SampleName cell dex albut Run avgLength
+## <factor> <factor> <factor> <factor> <factor> <integer>
+## SRR1039508 GSM1275862 N61311 untrt untrt SRR1039508 126
+## SRR1039509 GSM1275863 N61311 trt untrt SRR1039509 126
+## SRR1039512 GSM1275866 N052611 untrt untrt SRR1039512 126
+## SRR1039513 GSM1275867 N052611 trt untrt SRR1039513 87
+## SRR1039516 GSM1275870 N080611 untrt untrt SRR1039516 120
+## SRR1039517 GSM1275871 N080611 trt untrt SRR1039517 126
+## SRR1039520 GSM1275874 N061011 untrt untrt SRR1039520 101
+## SRR1039521 GSM1275875 N061011 trt untrt SRR1039521 98
+## Experiment Sample BioSample
+## <factor> <factor> <factor>
+## SRR1039508 SRX384345 SRS508568 SAMN02422669
+## SRR1039509 SRX384346 SRS508567 SAMN02422675
+## SRR1039512 SRX384349 SRS508571 SAMN02422678
+## SRR1039513 SRX384350 SRS508572 SAMN02422670
+## SRR1039516 SRX384353 SRS508575 SAMN02422682
+## SRR1039517 SRX384354 SRS508576 SAMN02422673
+## SRR1039520 SRX384357 SRS508579 SAMN02422683
+## SRR1039521 SRX384358 SRS508580 SAMN02422677</code></pre>
+<p>This sample metadata can be accessed using the <code>$</code> accessor which makes it easy to subset the entire object by a given phenotype.</p>
+<pre class="r"><code># subset for only those samples treated with dexamethasone
+se[, se$dex == "trt"]</code></pre>
+<pre><code>## class: RangedSummarizedExperiment
+## dim: 64102 4
+## metadata(1): ''
+## assays(1): counts
+## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
+## rowData names(0):
+## colnames(4): SRR1039509 SRR1039513 SRR1039517 SRR1039521
+## colData names(9): SampleName cell ... Sample BioSample</code></pre>
+</div>
+<div id="experiment-wide-metadata" class="section level2">
+<h2><span class="header-section-number">2.4</span> Experiment-wide metadata</h2>
+<p>Meta-data describing the experimental methods and publication references can be accessed using <code>metadata()</code>.</p>
+<pre class="r"><code>metadata(se)</code></pre>
+<pre><code>## [[1]]
+## Experiment data
+## Experimenter name: Himes BE
+## Laboratory: NA
+## Contact information:
+## Title: RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells.
+## URL: http://www.ncbi.nlm.nih.gov/pubmed/24926665
+## PMIDs: 24926665
+##
+## Abstract: A 226 word abstract is available. Use 'abstract' method.</code></pre>
+<p>Note that <code>metadata()</code> is just a simple list, so it is appropriate for <em>any</em> experiment wide metadata the user wishes to save, such as storing model formulas.</p>
+<pre class="r"><code>metadata(se)$formula <- counts ~ dex + albut
+
+metadata(se)</code></pre>
+<pre><code>## [[1]]
+## Experiment data
+## Experimenter name: Himes BE
+## Laboratory: NA
+## Contact information:
+## Title: RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells.
+## URL: http://www.ncbi.nlm.nih.gov/pubmed/24926665
+## PMIDs: 24926665
+##
+## Abstract: A 226 word abstract is available. Use 'abstract' method.
+##
+## $formula
+## counts ~ dex + albut</code></pre>
+</div>
+</div>
+<div id="constructing-a-summarizedexperiment" class="section level1">
+<h1><span class="header-section-number">3</span> Constructing a <code>SummarizedExperiment</code></h1>
+<p>Often, <code>SummarizedExperiment</code> or <code>RangedSummarizedExperiment</code> objects are returned by functions written by other packages. However it is possible to create them by hand with a call to the <code>SummarizedExperiment()</code> constructor.</p>
+<p>Constructing a <code>RangedSummarizedExperiment</code> with a <code>GRanges</code> as the <em>rowRanges</em> argument:</p>
+<pre class="r"><code>nrows <- 200
+ncols <- 6
+counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
+rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),
+ IRanges(floor(runif(200, 1e5, 1e6)), width=100),
+ strand=sample(c("+", "-"), 200, TRUE),
+ feature_id=sprintf("ID%03d", 1:200))
+colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
+ row.names=LETTERS[1:6])
+
+SummarizedExperiment(assays=list(counts=counts),
+ rowRanges=rowRanges, colData=colData)</code></pre>
+<pre><code>## class: RangedSummarizedExperiment
+## dim: 200 6
+## metadata(0):
+## assays(1): counts
+## rownames: NULL
+## rowData names(1): feature_id
+## colnames(6): A B ... E F
+## colData names(1): Treatment</code></pre>
+<p>A <code>SummarizedExperiment</code> can be constructed with or without supplying a <code>DataFrame</code> for the <em>rowData</em> argument:</p>
+<pre class="r"><code>SummarizedExperiment(assays=list(counts=counts), colData=colData)</code></pre>
+<pre><code>## class: SummarizedExperiment
+## dim: 200 6
+## metadata(0):
+## assays(1): counts
+## rownames: NULL
+## rowData names(0):
+## colnames(6): A B ... E F
+## colData names(1): Treatment</code></pre>
+</div>
+<div id="common-operations-on-summarizedexperiment" class="section level1">
+<h1><span class="header-section-number">4</span> Common operations on <code>SummarizedExperiment</code></h1>
+<div id="subsetting" class="section level2">
+<h2><span class="header-section-number">4.1</span> Subsetting</h2>
+<ul>
+<li><code>[</code> Performs two dimensional subsetting, just like subsetting a matrix or data frame.</li>
+</ul>
+<pre class="r"><code># subset the first five transcripts and first three samples
+se[1:5, 1:3]</code></pre>
+<pre><code>## class: RangedSummarizedExperiment
+## dim: 5 3
+## metadata(2): '' formula
+## assays(1): counts
+## rownames(5): ENSG00000000003 ENSG00000000005 ENSG00000000419
+## ENSG00000000457 ENSG00000000460
+## rowData names(0):
+## colnames(3): SRR1039508 SRR1039509 SRR1039512
+## colData names(9): SampleName cell ... Sample BioSample</code></pre>
+<ul>
+<li><code>$</code> operates on <code>colData()</code> columns, for easy sample extraction.</li>
+</ul>
+<pre class="r"><code>se[, se$cell == "N61311"]</code></pre>
+<pre><code>## class: RangedSummarizedExperiment
+## dim: 64102 2
+## metadata(2): '' formula
+## assays(1): counts
+## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
+## rowData names(0):
+## colnames(2): SRR1039508 SRR1039509
+## colData names(9): SampleName cell ... Sample BioSample</code></pre>
+</div>
+<div id="getters-and-setters" class="section level2">
+<h2><span class="header-section-number">4.2</span> Getters and setters</h2>
+<ul>
+<li><code>rowRanges()</code> / (<code>rowData()</code>), <code>colData()</code>, <code>metadata()</code></li>
+</ul>
+<pre class="r"><code>counts <- matrix(1:15, 5, 3, dimnames=list(LETTERS[1:5], LETTERS[1:3]))
+
+dates <- SummarizedExperiment(assays=list(counts=counts),
+ rowData=DataFrame(month=month.name[1:5], day=1:5))
+
+# Subset all January assays
+dates[rowData(dates)$month == "January", ]</code></pre>
+<pre><code>## class: SummarizedExperiment
+## dim: 1 3
+## metadata(0):
+## assays(1): counts
+## rownames(1): A
+## rowData names(2): month day
+## colnames(3): A B C
+## colData names(0):</code></pre>
+<ul>
+<li><code>assay()</code> versus <code>assays()</code> There are two accessor functions for extracting the assay data from a <code>SummarizedExperiment</code> object. <code>assays()</code> operates on the entire list of assay data as a whole, while <code>assay()</code> operates on only one assay at a time. <code>assay(x, i)</code> is simply a convenience function which is equivalent to <code>assays(x)[[i]]</code>.</li>
+</ul>
+<pre class="r"><code>assays(se)</code></pre>
+<pre><code>## List of length 1
+## names(1): counts</code></pre>
+<pre class="r"><code>assays(se)[[1]][1:5, 1:5]</code></pre>
+<pre><code>## SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
+## ENSG00000000003 679 448 873 408 1138
+## ENSG00000000005 0 0 0 0 0
+## ENSG00000000419 467 515 621 365 587
+## ENSG00000000457 260 211 263 164 245
+## ENSG00000000460 60 55 40 35 78</code></pre>
+<pre class="r"><code># assay defaults to the first assay if no i is given
+assay(se)[1:5, 1:5]</code></pre>
+<pre><code>## SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
+## ENSG00000000003 679 448 873 408 1138
+## ENSG00000000005 0 0 0 0 0
+## ENSG00000000419 467 515 621 365 587
+## ENSG00000000457 260 211 263 164 245
+## ENSG00000000460 60 55 40 35 78</code></pre>
+<pre class="r"><code>assay(se, 1)[1:5, 1:5]</code></pre>
+<pre><code>## SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
+## ENSG00000000003 679 448 873 408 1138
+## ENSG00000000005 0 0 0 0 0
+## ENSG00000000419 467 515 621 365 587
+## ENSG00000000457 260 211 263 164 245
+## ENSG00000000460 60 55 40 35 78</code></pre>
+</div>
+<div id="range-based-operations" class="section level2">
+<h2><span class="header-section-number">4.3</span> Range-based operations</h2>
+<ul>
+<li><code>subsetByOverlaps()</code> <code>SummarizedExperiment</code> objects support all of the <code>findOverlaps()</code> methods and associated functions. This includes <code>subsetByOverlaps()</code>, which makes it easy to subset a <code>SummarizedExperiment</code> object by an interval.</li>
+</ul>
+<pre class="r"><code># Subset for only rows which are in the interval 100,000 to 110,000 of
+# chromosome 1
+roi <- GRanges(seqnames="1", ranges=100000:1100000)
+subsetByOverlaps(se, roi)</code></pre>
+<pre><code>## class: RangedSummarizedExperiment
+## dim: 74 8
+## metadata(2): '' formula
+## assays(1): counts
+## rownames(74): ENSG00000131591 ENSG00000177757 ... ENSG00000272512
+## ENSG00000273443
+## rowData names(0):
+## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
+## colData names(9): SampleName cell ... Sample BioSample</code></pre>
+</div>
+</div>
+<div id="advanced-extending-rangedsummarizedexperiment" class="section level1">
+<h1><span class="header-section-number">5</span> Advanced: Extending <code>RangedSummarizedExperiment</code></h1>
+<p>For representing and manipulating data in their own package, Bioconductor developers are encouraged to re-use existing classes defined in other packages like the <code>RangedSummarizedExperiment</code> or <code>GRanges</code> containers defined in the <code>SummarizedExperiment</code> or <code>GenomicRanges</code> infrastructure packages, respectively. Many Bioconductor packages are designed around these basic containers, that is, they define functions that take and/or return a <code> [...]
+<p>However, sometimes re-using the <code>RangedSummarizedExperiment</code> class as-is does not satisfy the needs of the package and the developer makes the choice to extend the class in order to accomodate the special needs of the package and/or the specificities of the data that it deals with. For example the <code>DESeq2</code> package defines the <code>DESeqDataSet</code> class which extends <code>RangedSummarizedExperiment</code> to add the <code>design</code> and <code>dispersionFu [...]
+<p>The following subsections describe in a nutshell how the developer would typically proceed for extending <code>RangedSummarizedExperiment</code> in his/her own package. Some familiarity with the S4 class system is required. Readers not familiar with the S4 class system are encouraged to consult the vignette <code>A quick overview of the S4 class system</code> located in the <code>S4Vectors</code> package for the basics of implementing and extending S4 classes.</p>
+<p>The approach described below allows the developer to extend <code>RangedSummarizedExperiment</code> it in a way that remains agnostic of its internals. Keeping this separation between the responsibilities of the owners of the parent and child classes facilitate maintenance in the long run. In particular, the implementation of the child class won’t be affected by changes in the internals of the parent class.</p>
+<div id="depend-on-and-import-the-summarizedexperiment-package" class="section level2">
+<h2><span class="header-section-number">5.1</span> Depend on, and import, the <code>SummarizedExperiment</code> package</h2>
+<p>Add <code>SummarizedExperiment</code> to the Depends field of the DESCRIPTION file of the package and the <code>import(SummarizedExperiment)</code> directive to its NAMESPACE file.</p>
+</div>
+<div id="define-and-export-the-rangedsummarizedexperiment-subclass" class="section level2">
+<h2><span class="header-section-number">5.2</span> Define and export the <code>RangedSummarizedExperiment</code> subclass</h2>
+<p>Define the subclass with something like:</p>
+<pre class="r"><code>setClass("MyRSESubclass",
+ contains="RangedSummarizedExperiment",
+ representation=representation(
+ slot1="integer",
+ slot2="function"
+ ## ... maybe more ...
+ )
+)</code></pre>
+<p>Export it by adding the <code>exportClasses(MyRSESubclass)</code> directive to the NAMESPACE file.</p>
+</div>
+<div id="construct-myrsesubclass-instances" class="section level2">
+<h2><span class="header-section-number">5.3</span> Construct <code>MyRSESubclass</code> instances</h2>
+<p>When calling <code>new()</code> for constructing a <code>MyRSESubclass</code> instance, specify only the MyRSESubclass-specific slots: <code>new("MyRSESubclass", rse, slot1=value1, slot2=value2)</code>, where <code>rse</code> is a <code>RangedSummarizedExperiment</code> object.</p>
+<p>Providing a <code>MyRSESubclass</code> constructor function (named as the class itself) is recommended.</p>
+</div>
+<div id="define-a-validity-method" class="section level2">
+<h2><span class="header-section-number">5.4</span> Define a validity method</h2>
+<p>The validity method for <code>MyRSESubclass</code> only needs to take care of what’s new in <code>MyRSESubclass</code> with respect to <code>RangedSummarizedExperiment</code>, that is, of the aspects of <code>MyRSESubclass</code> objects that are not already covered by the validity method for <code>RangedSummarizedExperiment</code> objects. This is because calling <code>validObject()</code> on a <code>MyRSESubclass</code> object automatically validates it as a <code>RangedSummarizedEx [...]
+</div>
+<div id="use-the-rangedsummarizedexperiment-accessors-on-myrsesubclass-objects" class="section level2">
+<h2><span class="header-section-number">5.5</span> Use the <code>RangedSummarizedExperiment</code> accessors on <code>MyRSESubclass</code> objects</h2>
+<p>Like any user of <code>RangedSummarizedExperiment</code> objects, the developer of <code>MyRSESubclass</code> should always use the <code>RangedSummarizedExperiment</code> accessors to access the <code>RangedSummarizedExperiment</code>-specific parts of his/her <code>MyRSESubclass</code> objects.</p>
+</div>
+<div id="use-callnextmethod" class="section level2">
+<h2><span class="header-section-number">5.6</span> Use <code>callNextMethod</code></h2>
+<p>In case some of the methods defined for <code>RangedSummarizedExperiment</code> objects need to be overwritten, the new methods should call <code>callNextMethod</code> internally.</p>
+</div>
+</div>
+
+
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+ (function () {
+ var script = document.createElement("script");
+ script.type = "text/javascript";
+ script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+ document.getElementsByTagName("head")[0].appendChild(script);
+ })();
+</script>
+
+</body>
+</html>
diff --git a/inst/extdata/kallisto/abundance.h5 b/inst/extdata/kallisto/abundance.h5
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index 0000000..b7ee6be
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diff --git a/inst/extdata/kallisto/abundance.tsv b/inst/extdata/kallisto/abundance.tsv
new file mode 100644
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--- /dev/null
+++ b/inst/extdata/kallisto/abundance.tsv
@@ -0,0 +1,2859 @@
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diff --git a/inst/extdata/kallisto/abundance.txt b/inst/extdata/kallisto/abundance.txt
new file mode 100644
index 0000000..91a23bd
--- /dev/null
+++ b/inst/extdata/kallisto/abundance.txt
@@ -0,0 +1,16 @@
+target_id length eff_length est_counts tpm
+NM_001168316 2283 2105.9 164.133 12856.9
+NM_174914 2385 2207.9 1495.6 111741
+NR_031764 1853 1675.9 104.27 10263.4
+NM_004503 1681 1503.9 332.001 36416.5
+NM_006897 1541 1363.9 664 80308.9
+NM_014212 2037 1859.9 55 4878.11
+NM_014620 2300 2122.9 592.584 46046.7
+NM_017409 1959 1781.9 47 4351.04
+NM_017410 2396 2218.9 42 3122.41
+NM_018953 1612 1434.9 227.995 26210.8
+NM_022658 2288 2110.9 4881 381434
+NM_153633 1666 1488.9 359.898 39874.2
+NM_153693 2072 1894.9 72.5147 6312.74
+NM_173860 849 671.903 962 236182
+NR_003084 1640 1462.9 0.00787013 0.887453
diff --git a/inst/extdata/kallisto/run_info.json b/inst/extdata/kallisto/run_info.json
new file mode 100644
index 0000000..d5eb080
--- /dev/null
+++ b/inst/extdata/kallisto/run_info.json
@@ -0,0 +1,8 @@
+{
+ "n_targets": 2858,
+ "n_bootstraps": 100,
+ "kallisto_version": "0.42.2",
+ "index_version": 10,
+ "start_time": "Wed Jun 10 15:03:26 2015",
+ "call": "../kallisto/build/src/kallisto quant -i hg19chr14transcripts.idx -o output -b 100 --single -l 150 /dev/fd/63"
+}
diff --git a/inst/scripts/Find_and_update_objects/README b/inst/scripts/Find_and_update_objects/README
new file mode 100644
index 0000000..e51f3de
--- /dev/null
+++ b/inst/scripts/Find_and_update_objects/README
@@ -0,0 +1,68 @@
+Find and update objects
+=======================
+
+This document describes the procedure for finding and updating serialized
+objects in the rda files located in a directory hierarchy. This procedure
+consists of 4 STEPS, as described below.
+
+Note that this procedure was developped and used to update all "old"
+serialized SummarizedExperiment objects located in the data-experiment svn
+repository. It should be easy to adapt to update other types of objects in
+other locations.
+
+Also note that the purpose of STEPS 1 & 2 & 3 below is to collect the list
+of rda objects to update. Since this can take a long time, the 2 lists
+obtained for the "old" serialized SummarizedExperiment objects located
+in https://hedgehog.fhcrc.org/bioc-data/trunk/experiment/pkgs and
+https://hedgehog.fhcrc.org/bioc-data/trunk/experiment/data_store were saved
+to the pkgs_RDA_OBJECTS_TO_UPDATE and data_store_RDA_OBJECTS_TO_UPDATE
+files, respectively, and these 2 files placed in this folder. So in case
+these objects need to be updated again, these 2 files can be re-used to run
+STEP 4 directly without the need to re-run STEPS 1 & 2 & 3.
+
+STEP 1: Prepare list of rda files to scan.
+
+ cd <dir/you/want/to/search>
+ find . -type d -name '.svn' -prune -o -type f -print | \
+ grep -Ei '\.(rda|RData)$' >RDA_FILES
+
+STEP 2: Scan the rda files.
+
+ cd <dir/you/want/to/search>
+
+ R="$HOME/bin/R-3.2"
+ R_SCRIPT="scriptfile <- system.file('scripts', 'Find_and_update_objects', "
+ R_SCRIPT="$R_SCRIPT 'scan_rda_files.R', "
+ R_SCRIPT="$R_SCRIPT package='SummarizedExperiment', mustWork=TRUE)"
+ R_SCRIPT="$R_SCRIPT; source(scriptfile)"
+ echo "$R_SCRIPT" | $R --vanilla >scan_rda_files.log 2>&1 &
+
+STEP 3: Collect rda objects to update.
+
+ cd <dir/you/want/to/search>
+
+ R="$HOME/bin/R-3.2"
+ R_SCRIPT="scriptfile <- system.file('scripts', 'Find_and_update_objects', "
+ R_SCRIPT="$R_SCRIPT 'collect_rda_objects_to_update.R', "
+ R_SCRIPT="$R_SCRIPT package='SummarizedExperiment', mustWork=TRUE)"
+ R_SCRIPT="$R_SCRIPT; source(scriptfile)"
+ echo "$R_SCRIPT" | $R --vanilla >collect_rda_objects_to_update.log 2>&1 &
+
+STEP 4: Update rda objects.
+
+ cd <dir/you/want/to/search>
+
+ #To update the "old" serialized SummarizedExperiment objects located in
+ # https://hedgehog.fhcrc.org/bioc-data/trunk/experiment/pkgs
+ #replace the above command with
+ # svn co https://hedgehog.fhcrc.org/bioc-data/trunk/experiment/pkgs
+ # cp path/to/pkgs_RDA_OBJECTS_TO_UPDATE pkgs/RDA_OBJECTS_TO_UPDATE
+ # cd pkgs
+
+ R="$HOME/bin/R-3.2"
+ R_SCRIPT="scriptfile <- system.file('scripts', 'Find_and_update_objects', "
+ R_SCRIPT="$R_SCRIPT 'update_rda_objects.R', "
+ R_SCRIPT="$R_SCRIPT package='SummarizedExperiment', mustWork=TRUE)"
+ R_SCRIPT="$R_SCRIPT; source(scriptfile)"
+ echo "$R_SCRIPT" | $R --vanilla >update_rda_objects.log 2>&1 &
+
diff --git a/inst/scripts/Find_and_update_objects/collect_rda_objects_to_update.R b/inst/scripts/Find_and_update_objects/collect_rda_objects_to_update.R
new file mode 100644
index 0000000..0d07e43
--- /dev/null
+++ b/inst/scripts/Find_and_update_objects/collect_rda_objects_to_update.R
@@ -0,0 +1,133 @@
+### =========================================================================
+### collect_rda_objects_to_update.R
+### -------------------------------------------------------------------------
+###
+### This script performs STEP 3 of the "Find and update objects" procedure
+### described in the README file located in the same folder.
+###
+### Before you run this script, make sure you performed STEPS 1 & 2.
+### See README file for more information.
+###
+### Then to run STEP 3 in "batch" mode:
+###
+### cd <dir/you/want/to/search> # RDA_OBJECTS file should be here
+### R CMD BATCH collect_rda_objects_to_update.R \
+### >collect_rda_objects_to_update.log 2>&1 &
+###
+### The output of STEP 3 is a file created in the current directory and named
+### RDA_OBJECTS_TO_UPDATE. It is a subset of input file RDA_OBJECTS.
+###
+
+INFILE <- "RDA_OBJECTS"
+OUTFILE <- "RDA_OBJECTS_TO_UPDATE"
+
+library(BiocInstaller)
+library(SummarizedExperiment)
+
+if (FALSE) {
+### Unfortunately, loading all the required packages in the main process will
+### sometimes hit the maximal number of DLLs that can be loaded ("maximal
+### number of DLLs reached..." infamous error).
+.check_classes <- function(classes, package)
+{
+ suppressWarnings(suppressPackageStartupMessages(
+ library(package, character.only=TRUE, quietly=TRUE)
+ ))
+ sapply(classes, function(class) {
+ extends(class, "SummarizedExperiment") ||
+ extends(class, "RangedSummarizedExperiment")
+ })
+}
+}
+
+### We check the classes in a subprocess to work around the "maximal number
+### of DLLs reached..." infamous error.
+.check_classes <- function(classes, package)
+{
+ classes_in1string <- paste0("\"", classes, "\"")
+ classes_in1string <- paste0("c(",
+ paste(classes_in1string, collapse=", "),
+ ")")
+ outfile <- file.path(tempdir(), paste0(package, "_class_summary"))
+ input <- c("suppressWarnings(suppressPackageStartupMessages(",
+ sprintf(" library(%s)", "SummarizedExperiment"),
+ "))",
+ "suppressWarnings(suppressPackageStartupMessages(",
+ sprintf(" library(%s)", package),
+ "))",
+ sprintf("classes <- %s", classes_in1string),
+ "ok <- sapply(classes, function(class) {",
+ " extends(class, \"SummarizedExperiment\") ||",
+ " extends(class, \"RangedSummarizedExperiment\")",
+ "})",
+ "class_summary <- data.frame(class=classes, ok=unname(ok))",
+ sprintf("write.table(class_summary, file=\"%s\", sep=\"\t\")", outfile)
+ )
+ command <- file.path(R.home("bin"), "R")
+ args <- c("--vanilla", "--slave")
+ system2(command, args=args, input=input)
+ class_summary <- read.table(outfile, stringsAsFactors=FALSE)
+ file.remove(outfile)
+ stopifnot(identical(class_summary[ , "class"], classes)) # sanity check
+ setNames(class_summary[ , "ok"], classes)
+}
+
+collectRdaObjectsToUpdate <- function(rda_objects, outfile="")
+{
+ rda_objects2 <- unique(rda_objects[ , c("objclass", "objclass_pkg")])
+ objclass2 <- rda_objects2[ , "objclass"]
+ objclass_pkg2 <- rda_objects2[ , "objclass_pkg"]
+ idx <- which(duplicated(objclass2))
+ if (length(idx) != 0L) {
+ msg <- c("the following classes are defined in more than 1 package: ",
+ paste0(unique(objclass2[idx]), collapse=", "))
+ warning(msg)
+ }
+ pkg2class <- split(objclass2, objclass_pkg2)
+ pkg2class[c(".", ".GlobalEnv")] <- NULL
+ pkgs <- names(pkg2class)
+
+ ## Install missing packages.
+ installed_pkgs <- rownames(installed.packages())
+ missing_pkgs <- setdiff(pkgs, installed_pkgs)
+ if (length(missing_pkgs) != 0L) {
+ biocLite(missing_pkgs)
+ installed_pkgs <- rownames(installed.packages())
+ missing_pkgs <- setdiff(pkgs, installed_pkgs)
+ if (length(missing_pkgs) != 0L) {
+ ## Some packages could not be installed.
+ pkgs <- intersect(pkgs, installed_pkgs)
+ pkg2class <- pkg2class[pkgs]
+ }
+ }
+
+ ## Check classes, one package at a time.
+ class2ok <- unlist(
+ lapply(seq_along(pkgs),
+ function(i) {
+ pkg <- pkgs[[i]]
+ cat("[", i , "/", length(pkgs), "] Check classes defined ",
+ "in package ", pkg, " ... ", sep="")
+ ans <- .check_classes(pkg2class[[pkg]], pkg)
+ cat("OK\n")
+ ans
+ }
+ )
+ )
+
+ ## Write output to file.
+ objclass <- rda_objects[ , "objclass"]
+ ok <- class2ok[objclass]
+ ok[is.na(ok)] <- FALSE
+ rda_objects_to_update <- rda_objects[ok, , drop=FALSE]
+ rda_objects_to_update <- do.call(
+ paste,
+ c(as.list(rda_objects_to_update), list(sep="\t"))
+ )
+ writeLines(rda_objects_to_update, con=outfile)
+}
+
+rda_objects <- read.table(INFILE, stringsAsFactors=FALSE)
+colnames(rda_objects) <- c("rda_file", "objname", "objclass", "objclass_pkg")
+collectRdaObjectsToUpdate(rda_objects, outfile=OUTFILE)
+
diff --git a/inst/scripts/Find_and_update_objects/data_store_RDA_OBJECTS_TO_UPDATE b/inst/scripts/Find_and_update_objects/data_store_RDA_OBJECTS_TO_UPDATE
new file mode 100644
index 0000000..d9e6e4f
--- /dev/null
+++ b/inst/scripts/Find_and_update_objects/data_store_RDA_OBJECTS_TO_UPDATE
@@ -0,0 +1,30 @@
+# This file contains the list of rda objects to update located in
+# https://hedgehog.fhcrc.org/bioc-data/trunk/experiment/data_store
+# It was obtained by running STEPS 1 & 2 & 3 of the "Find and update objects"
+# procedure on May 26, 2015. See README file for more information.
+# To update the objects listed in this file:
+# 1. Get a working copy of the above URL.
+# 2. Copy this file to the working copy and rename it RDA_OBJECTS_TO_UPDATE.
+# 3. Run STEP 4 (see README file for more information).
+./geuvPack/data/geuFPKM.rda geuFPKM SummarizedExperiment GenomicRanges
+./bsseqData/data/BS.cancer.ex.fit.rda BS.cancer.ex.fit BSseq bsseq
+./bsseqData/data/BS.cancer.ex.rda BS.cancer.ex BSseq bsseq
+./fission/data/fission.RData fission SummarizedExperiment GenomicRanges
+./DREAM4/data/dream4_010_04.RData dream4_010_04 SummarizedExperiment GenomicRanges
+./DREAM4/data/dream4_100_01.RData dream4_100_01 SummarizedExperiment GenomicRanges
+./DREAM4/data/dream4_010_01.RData dream4_010_01 SummarizedExperiment GenomicRanges
+./DREAM4/data/dream4_010_05.RData dream4_010_05 SummarizedExperiment GenomicRanges
+./DREAM4/data/dream4_010_02.RData dream4_010_02 SummarizedExperiment GenomicRanges
+./DREAM4/data/dream4_100_04.RData dream4_100_04 SummarizedExperiment GenomicRanges
+./DREAM4/data/dream4_010_03.RData dream4_010_03 SummarizedExperiment GenomicRanges
+./DREAM4/data/dream4_100_05.RData dream4_100_05 SummarizedExperiment GenomicRanges
+./DREAM4/data/dream4_100_02.RData dream4_100_02 SummarizedExperiment GenomicRanges
+./DREAM4/data/dream4_100_03.RData dream4_100_03 SummarizedExperiment GenomicRanges
+./COSMIC.67/data/cosmic_67.rda cosmic_67 CollapsedVCF VariantAnnotation
+./parathyroidSE/data/parathyroidGenesSE.RData parathyroidGenesSE SummarizedExperiment GenomicRanges
+./parathyroidSE/data/parathyroidExonsSE.RData parathyroidExonsSE SummarizedExperiment GenomicRanges
+./RRBSdata/data/rrbs.RData rrbs BSraw BiSeq
+./airway/data/airway.RData airway SummarizedExperiment GenomicRanges
+./dsQTL/data/DSQ_2.rda DSQ_2 SummarizedExperiment GenomicRanges
+./dsQTL/data/DSQ_17.rda DSQ_17 SummarizedExperiment GenomicRanges
+./dsQTL/data/DHStop5_hg19.rda DHStop5_hg19 SummarizedExperiment GenomicRanges
diff --git a/inst/scripts/Find_and_update_objects/pkgs_RDA_OBJECTS_TO_UPDATE b/inst/scripts/Find_and_update_objects/pkgs_RDA_OBJECTS_TO_UPDATE
new file mode 100644
index 0000000..d10769a
--- /dev/null
+++ b/inst/scripts/Find_and_update_objects/pkgs_RDA_OBJECTS_TO_UPDATE
@@ -0,0 +1,9 @@
+# This file contains the list of rda objects to update located in
+# https://hedgehog.fhcrc.org/bioc-data/trunk/experiment/pkgs
+# It was obtained by running STEPS 1 & 2 & 3 of the "Find and update objects"
+# procedure on May 26, 2015. See README file for more information.
+# To update the objects listed in this file:
+# 1. Get a working copy of the above URL.
+# 2. Copy this file to the working copy and rename it RDA_OBJECTS_TO_UPDATE.
+# 3. Run STEP 4 (see README file for more information).
+./pasilla/data/pasillaDEXSeqDataSet.RData dxd DEXSeqDataSet DEXSeq
diff --git a/inst/scripts/Find_and_update_objects/scan_rda_files.R b/inst/scripts/Find_and_update_objects/scan_rda_files.R
new file mode 100644
index 0000000..5bcd019
--- /dev/null
+++ b/inst/scripts/Find_and_update_objects/scan_rda_files.R
@@ -0,0 +1,68 @@
+### =========================================================================
+### scan_rda_files.R
+### -------------------------------------------------------------------------
+###
+### This script performs STEP 2 of the "Find and update objects" procedure
+### described in the README file located in the same folder.
+###
+### Before you run this script, make sure you performed STEP 1, that is, you
+### need to generate input file RDA_FILES. This can be achieved with
+### something like:
+###
+### cd <dir/you/want/to/search>
+### find . -type d -name '.svn' -prune -o -type f -print | \
+### grep -Ei '\.(rda|RData)$' >RDA_FILES
+###
+### See README file for more information.
+###
+### Then to run STEP 2 in "batch" mode:
+###
+### cd <dir/you/want/to/search> # RDA_FILES file should be here
+### R CMD BATCH scan_rda_files.R >scan_rda_files.log 2>&1 &
+###
+### This can take a couple of hours to complete...
+###
+### The output of STEP 2 is a file created in the current directory and named
+### RDA_OBJECTS. It has 1 line per serialized object and the 4 following
+### fields (separated by tabs):
+### 1. Path to rda file (as found in input file RDA_FILES).
+### 2. Name of object in rda file.
+### 3. Class of object in rda file.
+### 4. Package where class of object is defined.
+###
+
+INFILE <- "RDA_FILES"
+OUTFILE <- "RDA_OBJECTS"
+
+scanRdaFiles <- function(rda_files, outfile="")
+{
+ cat("", file=outfile) # create (or overwrite) empty output file
+ for (i in seq_along(rda_files)) {
+ rda_file <- rda_files[[i]]
+
+ cat("[", i , "/", length(rda_files), "] Loading ", rda_file, " ... ",
+ sep="")
+ envir <- new.env(parent=emptyenv())
+ load(rda_file, envir=envir)
+ cat("OK\n")
+
+ for (objname in names(envir)) {
+ obj <- get(objname, envir=envir)
+ objclass <- class(obj)
+ objclass_pkg <- attr(objclass, "package")
+
+ ## Fix 'objclass' and 'objclass_pkg' (they both need to be
+ ## character vectors of length 1 before we pass them to paste()).
+ objclass <- objclass[[1L]]
+ if (is.null(objclass_pkg))
+ objclass_pkg <- "."
+ outline <- paste(rda_file, objname, objclass, objclass_pkg,
+ sep="\t")
+ cat(outline, "\n", sep="", file=OUTFILE, append=TRUE)
+ }
+ }
+}
+
+rda_files <- read.table(INFILE, stringsAsFactors=FALSE)[[1L]]
+scanRdaFiles(rda_files, outfile=OUTFILE)
+
diff --git a/inst/scripts/Find_and_update_objects/update_rda_objects.R b/inst/scripts/Find_and_update_objects/update_rda_objects.R
new file mode 100644
index 0000000..fd9aa14
--- /dev/null
+++ b/inst/scripts/Find_and_update_objects/update_rda_objects.R
@@ -0,0 +1,70 @@
+### =========================================================================
+### update_rda_objects.R
+### -------------------------------------------------------------------------
+###
+### This script performs STEP 4 of the "Find and update objects" procedure
+### described in the README file located in the same folder.
+###
+### Before you run this script, make sure you performed STEPS 1 & 2 & 3.
+### See README file for more information.
+###
+### Then to run STEP 4 in "batch" mode:
+###
+### cd <dir/you/want/to/search> # RDA_OBJECTS_TO_UPDATE file should be here
+### R CMD BATCH update_rda_objects.R >update_rda_objects.log 2>&1 &
+###
+
+INFILE <- "RDA_OBJECTS_TO_UPDATE"
+
+library(SummarizedExperiment)
+
+.update_objects <- function(envir)
+{
+ updated <- FALSE
+ for (objname in names(envir)) {
+ obj <- get(objname, envir=envir, inherits=FALSE)
+ objclass <- class(obj)
+ objclass_pkg <- attr(objclass, "package")
+ if (!is.null(objclass_pkg)) {
+ suppressWarnings(suppressPackageStartupMessages(
+ library(objclass_pkg, character.only=TRUE, quietly=TRUE)
+ ))
+ }
+ if (!SummarizedExperiment:::.has_SummarizedExperiment_internal_structure(obj))
+ next()
+
+ cat(" Updating ", objname, " ... ", sep="")
+ obj <- updateObject(obj)
+ validObject(obj, complete=TRUE)
+ assign(objname, obj, envir=envir, inherits=FALSE)
+ cat("OK\n")
+ updated <- TRUE
+ }
+ updated
+}
+
+updateRdaObjects <- function(rda_objects)
+{
+ rda_files <- unique(rda_objects[ , "rda_file"])
+ for (i in seq_along(rda_files)) {
+ rda_file <- rda_files[[i]]
+
+ cat("[", i , "/", length(rda_files), "] Loading ", rda_file, " ... ",
+ sep="")
+ envir <- new.env(parent=emptyenv())
+ load(rda_file, envir=envir)
+ cat("OK\n")
+
+ if (.update_objects(envir)) {
+ cat(" Saving updated objects to ", rda_file, " ... ", sep="")
+ save(list=names(envir), file=rda_file, envir=envir, compress="xz")
+ cat("OK\n")
+ }
+ }
+}
+
+rda_objects_to_update <- read.table(INFILE, stringsAsFactors=FALSE)
+colnames(rda_objects_to_update) <- c("rda_file", "objname",
+ "objclass", "objclass_pkg")
+updateRdaObjects(rda_objects_to_update)
+
diff --git a/inst/unitTests/test_Assays-class.R b/inst/unitTests/test_Assays-class.R
new file mode 100644
index 0000000..1376417
--- /dev/null
+++ b/inst/unitTests/test_Assays-class.R
@@ -0,0 +1,88 @@
+test_bind_Assays <- function() {
+ ## unnamed -- map by position
+ l1 <- list(matrix(1, 3, 4), matrix(2, 3, 4))
+ l2 <- list(matrix(3, 3, 4), matrix(4, 3, 4))
+ a1 <- Assays(l1)
+ a2 <- Assays(l2)
+
+ target <- Map(rbind, l1, l2)
+ current <- rbind(a1, a2)
+ checkTrue(is(current, "ShallowSimpleListAssays"))
+ checkIdentical(as(target, "SimpleList"), as(current, "SimpleList"))
+
+ target <- Map(cbind, l1, l2)
+ current <- cbind(a1, a2)
+ checkTrue(is(current, "ShallowSimpleListAssays"))
+ checkIdentical(as(target, "SimpleList"), as(current, "SimpleList"))
+
+ ## named -- map by name
+ l1 <- list(x=matrix(1, 3, 4), y=matrix(2, 3, 4))
+ l2 <- list(y=matrix(4, 3, 4), x=matrix(3, 3, 4))
+ a1 <- Assays(l1)
+ a2 <- Assays(l2)
+
+ target <- Map(rbind, l1, l2[match(names(l2), names(l1))])
+ current <- rbind(a1, a2)
+ checkTrue(is(current, "ShallowSimpleListAssays"))
+ checkIdentical(as(target, "SimpleList"), as(current, "SimpleList"))
+
+ target <- Map(cbind, l1, l2[match(names(l2), names(l1))])
+ current <- cbind(a1, a2)
+ checkTrue(is(current, "ShallowSimpleListAssays"))
+ checkIdentical(as(target, "SimpleList"), as(current, "SimpleList"))
+}
+
+test_bind_higher_order_Assays <- function() {
+ ## unnamed -- map by position
+ l1 <- list(array(1, dim = c(3, 4, 5, 6)),
+ array(2, dim = c(3, 4, 5, 6)))
+ l2 <- list(array(4, dim = c(3, 4, 5, 6)),
+ array(3, dim = c(3, 4, 5, 6)))
+ a1 <- Assays(l1)
+ a2 <- Assays(l2)
+
+ target <- Map(arbind, l1, l2)
+ current <- rbind(a1, a2)
+ checkTrue(is(current, "ShallowSimpleListAssays"))
+ checkIdentical(as(target, "SimpleList"), as(current, "SimpleList"))
+
+ target <- Map(acbind, l1, l2)
+ current <- cbind(a1, a2)
+ checkTrue(is(current, "ShallowSimpleListAssays"))
+ checkIdentical(as(target, "SimpleList"), as(current, "SimpleList"))
+
+ ## named -- map by name
+ l1 <- list(x = array(1, dim = c(3, 4, 5, 6)),
+ y = array(2, dim = c(3, 4, 5, 6)))
+ l2 <- list(y = array(4, dim = c(3, 4, 5, 6)),
+ x = array(3, dim = c(3, 4, 5, 6)))
+ a1 <- Assays(l1)
+ a2 <- Assays(l2)
+
+ target <- Map(arbind, l1, l2[match(names(l2), names(l1))])
+ current <- rbind(a1, a2)
+ checkTrue(is(current, "ShallowSimpleListAssays"))
+ checkIdentical(as(target, "SimpleList"), as(current, "SimpleList"))
+
+ target <- Map(acbind, l1, l2[match(names(l2), names(l1))])
+ current <- cbind(a1, a2)
+ checkTrue(is(current, "ShallowSimpleListAssays"))
+ checkIdentical(as(target, "SimpleList"), as(current, "SimpleList"))
+}
+
+test_bind_error_on_incompatible_dimension_Assays <- function() {
+ l1 <- list(x = array(1, dim = c(3, 4, 5, 6)),
+ y = array(2, dim = c(3, 4, 5, 6)))
+ l2 <- list(y = matrix(4, 3, 4), x = matrix(3, 3, 4))
+ a1 <- Assays(l1)
+ a2 <- Assays(l2)
+
+ ## arbind
+ checkException(rbind(a1, a2), silent = TRUE)
+ checkException(arbind(l1[[1]], l2[[1]]), silent = TRUE)
+
+ ## acbind
+ checkException(cbind(a1, a2), silent = TRUE)
+ checkException(acbind(l1[[1]], l2[[1]]), silent = TRUE)
+}
+
diff --git a/inst/unitTests/test_RangedSummarizedExperiment-class.R b/inst/unitTests/test_RangedSummarizedExperiment-class.R
new file mode 100644
index 0000000..88a1ced
--- /dev/null
+++ b/inst/unitTests/test_RangedSummarizedExperiment-class.R
@@ -0,0 +1,375 @@
+library(digest)
+
+.singleDispatch <-
+ c("duplicated", "end", "end<-", "granges", "ranges",
+ "seqinfo", "seqinfo<-", "seqnames", "start", "start<-",
+ "strand", "width", "width<-")
+
+.twoDispatch <- c("pcompare", "Compare")
+
+.otherFuns <- c("is.unsorted", "order", "rank", "sort")
+
+M1 <- matrix(1, 5, 3, dimnames=list(NULL, NULL))
+M2 <- matrix(1, 3, 3, dimnames=list(NULL, NULL))
+mList <- list(M1, M2)
+assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2))
+rowRangesList <-
+ list(gr=GRanges("chr1", IRanges(1:5, 10)),
+ grl=split(GRanges("chr1", IRanges(1:5, 10)), c(1,1,2,2,3)))
+names(rowRangesList[["grl"]]) <- NULL
+colData <- DataFrame(x=letters[1:3])
+
+## a list of one SE with GRanges and one with GRangesList
+rseList <-
+ list(SummarizedExperiment(
+ assays=assaysList[["gr"]],
+ rowRanges=rowRangesList[["gr"]],
+ colData=colData),
+ SummarizedExperiment(
+ assays=assaysList[["grl"]],
+ rowRanges=rowRangesList[["grl"]],
+ colData=colData))
+
+
+test_RangedSummarizedExperiment_construction <- function()
+{
+ ## empty-ish
+ m1 <- matrix(0, 0, 0)
+ checkTrue(validObject(new("RangedSummarizedExperiment")))
+
+ ## substance
+ for (i in seq_along(rseList)) {
+ rse <- rseList[[i]]
+ checkTrue(validObject(rse))
+ checkIdentical(SimpleList(m=mList[[i]]), assays(rse))
+ checkIdentical(rowRangesList[[i]], rowRanges(rse))
+ checkIdentical(DataFrame(x=letters[1:3]), colData(rse))
+ }
+
+ ## array in assays slot
+ ss <- rseList[[1]]
+ assays(ss) <- SimpleList(array(1:5, c(5,3,2)))
+ checkTrue(validObject(ss))
+ checkTrue(all(dim(assays(ss[1:3,1:2])[[1]]) == c(3, 2, 2)))
+}
+
+test_RangedSummarizedExperiment_getters <- function()
+{
+ for (i in seq_along(rseList)) {
+ rse <- rseList[[i]]
+ rowRanges <- rowRangesList[[i]]
+
+ ## dim, dimnames
+ checkIdentical(c(length(rowRanges), nrow(colData)), dim(rse))
+ checkIdentical(list(NULL, NULL), dimnames(rse))
+
+ ## row / col / metadata
+ checkIdentical(rowRanges, rowRanges(rse))
+ checkIdentical(colData, colData(rse))
+ checkIdentical(list(), metadata(rse))
+ }
+}
+
+test_RangedSummarizedExperiment_setters <- function()
+{
+ for (i in seq_along(rseList)) {
+ rse <- rseList[[i]]
+ rowRanges <- rowRangesList[[i]]
+
+ ## row / col / metadata<-
+ ss1 <- rse
+ revData <- rev(rowRanges)
+ rowRanges(ss1) <- revData
+ checkIdentical(revData, rowRanges(ss1))
+ checkException(rowRanges(ss1) <- rowRanges(rse)[1:2,,drop=FALSE],
+ "incorrect row dimensions", TRUE)
+ revData <- colData[rev(seq_len(nrow(colData))),,drop=FALSE]
+ colData(ss1) <- revData
+ checkIdentical(revData, colData(ss1))
+ checkException(colData(ss1) <- colData(rse)[1:2,,drop=FALSE],
+ "incorrect col dimensions", TRUE)
+ lst <- list("foo", "bar")
+ metadata(ss1) <- lst
+ checkIdentical(lst, metadata(ss1))
+
+ ## assay / assays
+ ss1 <- rse
+ assay(ss1) <- assay(ss1)+1
+ checkIdentical(assay(rse)+1, assay(ss1))
+ ss1 <- rse
+ assay(ss1, 1) <- assay(ss1, 1) + 1
+ checkIdentical(assay(rse, "m") + 1, assay(ss1, "m"))
+ ss1 <- rse
+ assay(ss1, "m") <- assay(ss1, "m") + 1
+ checkIdentical(assay(rse, "m")+1, assay(ss1, "m"))
+
+ ## dimnames<-
+ ss1 <- rse
+ dimnames <- list(letters[seq_len(nrow(ss1))],
+ LETTERS[seq_len(ncol(ss1))])
+ rownames(ss1) <- dimnames[[1]]
+ colnames(ss1) <- dimnames[[2]]
+ checkIdentical(dimnames, dimnames(ss1))
+ rowRanges1 <- rowRanges
+ names(rowRanges1) <- dimnames[[1]]
+ checkIdentical(rowRanges1, rowRanges(ss1))
+ colData1 <- colData
+ row.names(colData1) <- dimnames[[2]]
+ checkIdentical(colData1, colData(ss1))
+ ss1 <- rse
+ dimnames(ss1) <- dimnames
+ checkIdentical(dimnames, dimnames(ss1))
+ dimnames(ss1) <- NULL
+ checkIdentical(list(NULL, NULL), dimnames(ss1))
+ }
+}
+
+test_RangedSummarizedExperiment_subset <- function()
+{
+ for (i in seq_along(rseList)) {
+ rse <- rseList[[i]]
+ rowRanges <- rowRangesList[[i]]
+
+ ## numeric
+ ss1 <- rse[2:3,]
+ checkIdentical(c(2L, ncol(rse)), dim(ss1))
+ checkIdentical(rowRanges(ss1), rowRanges(rse)[2:3,])
+ checkIdentical(colData(ss1), colData(rse))
+ ss1 <- rse[,2:3]
+ checkIdentical(c(nrow(rse), 2L), dim(ss1))
+ checkIdentical(rowRanges(ss1), rowRanges(rse))
+ checkIdentical(colData(ss1), colData(rse)[2:3,,drop=FALSE])
+ ss1 <- rse[2:3, 2:3]
+ checkIdentical(c(2L, 2L), dim(ss1))
+ checkIdentical(rowRanges(ss1), rowRanges(rse)[2:3,,drop=FALSE])
+ checkIdentical(colData(ss1), colData(rse)[2:3,,drop=FALSE])
+
+ ## character
+ ss1 <- rse
+ dimnames(ss1) <- list(LETTERS[seq_len(nrow(ss1))],
+ letters[seq_len(ncol(ss1))])
+ ridx <- c("B", "C")
+ checkIdentical(rowRanges(ss1[ridx,]), rowRanges(ss1)[ridx,])
+ checkIdentical(rowRanges(ss1["C",]), rowRanges(ss1)["C",,drop=FALSE])
+ checkException(ss1[LETTERS,], "i-index out of bounds", TRUE)
+ cidx <- c("b", "c")
+ checkIdentical(colData(ss1[,cidx]), colData(ss1)[cidx,,drop=FALSE])
+ checkIdentical(colData(ss1[,"a"]), colData(ss1)["a",,drop=FALSE])
+ checkException(ss1[,letters], "j-index out of bounds", TRUE)
+
+ ## logical
+ ss1 <- rse
+ dimnames(ss1) <- list(LETTERS[seq_len(nrow(ss1))],
+ letters[seq_len(ncol(ss1))])
+ checkEquals(ss1, ss1[TRUE,])
+ checkIdentical(c(0L, ncol(ss1)), dim(ss1[FALSE,]))
+ checkEquals(ss1, ss1[,TRUE])
+ checkIdentical(c(nrow(ss1), 0L), dim(ss1[,FALSE]))
+ idx <- c(TRUE, FALSE) # recycling
+ ss2 <- ss1[idx,]
+ checkIdentical(rowRanges(ss1)[idx,,drop=FALSE], rowRanges(ss2))
+ ss2 <- ss1[,idx]
+ checkIdentical(colData(ss1)[idx,,drop=FALSE], colData(ss2))
+
+ ## Rle
+ ss1 <- rse
+ rle <- rep(c(TRUE, FALSE), each=3, length.out=nrow(ss1))
+ checkIdentical(rowRanges(ss1[rle]), rowRanges(ss1[Rle(rle)]))
+ checkIdentical(assays(ss1[rle]), assays(ss1[Rle(rle)]))
+ }
+
+ ## 0 columns
+ se <- SummarizedExperiment(rowRanges=GRanges("chr1", IRanges(1:10, width=1)))
+ checkIdentical(dim(se[1:5, ]), c(5L, 0L))
+ ## 0 rows
+ se <- SummarizedExperiment(colData=DataFrame(samples=1:10))
+ checkIdentical(dim(se[ ,1:5]), c(0L, 5L))
+}
+
+test_RangedSummarizedExperiment_subsetassign <- function()
+{
+ for (i in seq_along(rseList)) {
+ rse <- rseList[[i]]
+ dimnames(rse) <- list(LETTERS[seq_len(nrow(rse))],
+ letters[seq_len(ncol(rse))])
+ ## rows
+ ss1 <- rse
+ ss1[1:2,] <- ss1[2:1,]
+ checkIdentical(rowRanges(rse)[2:1,], rowRanges(ss1)[1:2,])
+ checkIdentical(rowRanges(rse[-(1:2),]), rowRanges(ss1)[-(1:2),])
+ checkIdentical(colData(rse), colData(ss1))
+ checkIdentical(c(metadata(rse), metadata(rse)), metadata(ss1))
+ ## Rle
+ ss1rle <- ss1Rle <- rse
+ rle <- rep(c(TRUE, FALSE), each=3, length.out=nrow(ss1))
+ ss1rle[rle,] <- ss1rle[rle,]
+ ss1Rle[Rle(rle),] <- ss1Rle[Rle(rle),]
+ checkIdentical(rowRanges(ss1rle), rowRanges(ss1Rle))
+ checkIdentical(assays(ss1rle), assays(ss1Rle))
+ ## cols
+ ss1 <- rse
+ ss1[,1:2] <- ss1[,2:1,drop=FALSE]
+ checkIdentical(colData(rse)[2:1,,drop=FALSE],
+ colData(ss1)[1:2,,drop=FALSE])
+ checkIdentical(colData(rse)[-(1:2),,drop=FALSE],
+ colData(ss1)[-(1:2),,drop=FALSE])
+ checkIdentical(rowRanges(rse), rowRanges(ss1))
+ checkIdentical(c(metadata(rse), metadata(rse)), metadata(ss1))
+ }
+
+ ## full replacement
+ ss1 <- ss2 <- rseList[[1]]
+ rowRanges(ss2) <- rev(rowRanges(ss2))
+ ss1[,] <- ss2
+ checkIdentical(ss1, ss2)
+}
+
+quiet <- suppressWarnings
+test_RangedSummarizedExperiment_cbind <- function()
+## requires matching ranges
+{
+ ## empty
+ se <- SummarizedExperiment()
+ empty <- cbind(se, se)
+ checkTrue(all.equal(se, empty))
+
+ ## different ranges
+ se1 <- rseList[[1]]
+ se2 <- se1[2:4]
+ rownames(se2) <- month.name[seq_len(nrow(se2))]
+ checkException(quiet(cbind(se1, se2)), silent=TRUE)
+
+ ## same ranges
+ se1 <- rseList[[1]]
+ se2 <- se1[,1:2]
+ colnames(se2) <- month.name[seq_len(ncol(se2))]
+ res <- cbind(se1, se2)
+ checkTrue(nrow(res) == 5)
+ checkTrue(ncol(res) == 5)
+ ## rowRanges
+ rowData(se1) <- DataFrame("one"=1:5)
+ rowData(se2) <- DataFrame("two"=6:10)
+ res <- quiet(cbind(se1, se2))
+ checkIdentical(names(mcols(rowRanges(res))), c("one", "two"))
+ rowData(se2) <- DataFrame("one"=6:10, "two"=6:10)
+ checkException(cbind(se1, se2), silent=TRUE)
+ ## colData
+ checkTrue(nrow(colData(res)) == 5)
+ ## assays
+ se1 <- rseList[[1]]
+ se2 <- se1[,1:2]
+ assays(se1) <- SimpleList("m"=matrix(rep("m", 15), nrow=5),
+ "a"=array(rep("a", 30), c(5,3,2)))
+ assays(se2) <- SimpleList("m"=matrix(LETTERS[1:10], nrow=5),
+ "a"=array(LETTERS[1:20], c(5,2,2)))
+ res <- cbind(se1, se2) ## same variables
+ checkTrue(nrow(res) == 5)
+ checkTrue(ncol(res) == 5)
+ checkTrue(all.equal(dim(assays(res)$m), c(5L, 5L)))
+ checkTrue(all.equal(dim(assays(res)$a), c(5L, 5L, 2L)))
+ names(assays(se1)) <- c("mm", "aa")
+ checkException(cbind(se1, se2), silent=TRUE) ## different variables
+}
+
+test_RangedSummarizedExperiment_rbind <- function()
+## requires matching samples
+{
+ ## empty
+ se <- SummarizedExperiment()
+ empty <- rbind(se, se)
+ checkTrue(all.equal(se, empty))
+
+ ## different samples
+ se1 <- rseList[[1]]
+ se2 <- se1[,1]
+ checkException(quiet(rbind(se1, se2)), silent=TRUE)
+
+ ## same samples
+ se1 <- rseList[[1]]
+ se2 <- se1
+ rownames(se2) <- LETTERS[seq_len(nrow(se2))]
+ res <- rbind(se1, se2)
+ checkTrue(nrow(res) == 10)
+ checkTrue(ncol(res) == 3)
+ ## rowRanges
+ rowData(se1) <- DataFrame("one"=1:5)
+ rowData(se2) <- DataFrame("two"=6:10)
+ checkException(rbind(se1, se2), silent=TRUE)
+ ## colDat
+ se1 <- rseList[[1]]
+ se2 <- se1
+ colData(se2) <- DataFrame("one"=1:3, "two"=4:6)
+ res <- quiet(rbind(se1, se2))
+ checkTrue(ncol(colData(res)) == 3)
+ ## assays
+ se1 <- rseList[[1]]
+ se2 <- se1
+ assays(se1) <- SimpleList("m"=matrix(rep("m", 15), nrow=5),
+ "a"=array(rep("a", 30), c(5,3,2)))
+ assays(se2) <- SimpleList("m"=matrix(LETTERS[1:15], nrow=5),
+ "a"=array(LETTERS[1:30], c(5,3,2)))
+ res <- rbind(se1, se2) ## same variables
+ checkTrue(nrow(res) == 10)
+ checkTrue(ncol(res) == 3)
+ checkTrue(all.equal(dim(assays(res)$m), c(10L, 3L)))
+ checkTrue(all.equal(dim(assays(res)$a), c(10L, 3L, 2L)))
+ names(assays(se1)) <- c("mm", "aa")
+ checkException(rbind(se1, se2), silent=TRUE) ## different variables
+}
+
+test_RangedSummarizedExperiment_GRanges_API <- function()
+{
+ ## are we targetting the correct API? signature for
+ ## RangedSummarizedExperiment method should match signature for
+ ## GenomicRanges or similar, as in each test below
+
+ for (.fun in .singleDispatch) {
+ generic <- getGeneric(.fun)
+ method <- getMethod(.fun, "RangedSummarizedExperiment")
+ checkIdentical("x", generic at signature)
+ checkIdentical(formals(generic at .Data), formals(method at .Data))
+ }
+
+ ## FIXME: pcompare, Compare
+
+ .sig <- "RangedSummarizedExperiment"
+ for (.fun in .otherFuns) {
+ generic <- getGeneric(.fun)
+ method <- getMethod(.fun, "RangedSummarizedExperiment")
+ checkIdentical(formals(generic at .Data), formals(method at .Data))
+ }
+}
+
+test_RangedSummarizedExperiment_GRanges_values <- function()
+{
+ x <- rseList[[1]]
+ isAssign <- grep("<-$", .singleDispatch, value=TRUE)
+ .funs <- setdiff(.singleDispatch, isAssign)
+ ## 'exp' created after manual inspection of results
+ exp <- setNames(c("02dde", "80339", "49a3f", "86757", "77198",
+ "ec53a", "35e2c", "625d9", "3c90a"), .funs)
+ obs <- sapply(.funs, function(.fun) {
+ substr(digest(getGeneric(.fun)(x)), 1, 5)
+ })
+ checkIdentical(exp, obs)
+
+ .funs <- isAssign
+ .gets <- sub("<-$", "", isAssign)
+ for (i in seq_along(isAssign)) {
+ ## self-assignment isomorphism
+ value <- getGeneric(.gets[[i]])(x)
+ x1 <- do.call(isAssign[[i]], list(x, value=value))
+ checkIdentical(x, x1)
+ }
+}
+
+test_RangedSummarizedExperiment_split <- function()
+{
+ gr <- GRanges(Rle(c("A", "B"), c(2, 3)), IRanges(1:5, 10))
+ se <- SummarizedExperiment(M1, rowRanges=gr, colData=colData)
+ ## FIXME: unname should not be necessary
+ obs <- split(se, seqnames(se))
+ exp <- SimpleList(A=se[1:2], B=se[3:5])
+ checkEquals(obs, exp)
+}
+
diff --git a/inst/unitTests/test_SummarizedExperiment-class.R b/inst/unitTests/test_SummarizedExperiment-class.R
new file mode 100644
index 0000000..3d590d8
--- /dev/null
+++ b/inst/unitTests/test_SummarizedExperiment-class.R
@@ -0,0 +1,339 @@
+M1 <- matrix(1, 5, 3, dimnames=list(NULL, NULL))
+M2 <- matrix(1, 3, 3, dimnames=list(NULL, NULL))
+mList <- list(M1, M2)
+assaysList <- list(M1=SimpleList(m=M1), M2=SimpleList(m=M2))
+rowData1 <- DataFrame(id1=LETTERS[1:5])
+rowData2 <- S4Vectors:::make_zero_col_DataFrame(3L)
+rowDataList <- list(rowData1, rowData2)
+colData0 <- DataFrame(x=letters[1:3])
+
+se0List <-
+ list(SummarizedExperiment(
+ assays=assaysList[["M1"]],
+ rowData=rowData1,
+ colData=colData0),
+ SummarizedExperiment(
+ assays=assaysList[["M2"]],
+ colData=colData0))
+
+
+test_SummarizedExperiment_construction <- function()
+{
+ ## empty-ish
+ m1 <- matrix(0, 0, 0)
+ checkTrue(validObject(new("SummarizedExperiment")))
+ checkTrue(validObject(SummarizedExperiment()), "empty constructor")
+ checkTrue(validObject(SummarizedExperiment(SimpleList())))
+ checkTrue(validObject(SummarizedExperiment(assays=SimpleList(m1))),
+ "0x0 constructor")
+ checkException(SummarizedExperiment(assays=SimpleList(m1, matrix())),
+ "assays dim mismatch", TRUE)
+
+ ## substance
+ for (i in seq_along(se0List)) {
+ se0 <- se0List[[i]]
+ checkTrue(validObject(se0))
+ checkIdentical(SimpleList(m=mList[[i]]), assays(se0))
+ checkIdentical(rowDataList[[i]], rowData(se0))
+ checkIdentical(colData0, colData(se0))
+ }
+
+ ## array in assays slot
+ ss <- se0List[[1]]
+ assays(ss) <- SimpleList(array(1:5, c(5,3,2)))
+ checkTrue(validObject(ss))
+ checkTrue(all(dim(assays(ss[1:3,1:2])[[1]]) == c(3, 2, 2)))
+
+ ## matrix-of-list in assay slot
+ m <- matrix(list(), 2, 3, dimnames=list(LETTERS[1:2], letters[1:3]))
+ checkTrue(validObject(se <- SummarizedExperiment(m)))
+ checkIdentical(m, assay(se))
+ checkIdentical(m[,1:2], assay(se[,1:2]))
+
+ ## DataFrame in assay slot
+ df <- DataFrame(a=1:3, b=1:3, row.names=LETTERS[1:3])
+ checkTrue(validObject(SummarizedExperiment(list(df))))
+}
+
+test_SummarizedExperiment_construction_colnames <- function()
+{
+ colnames <- LETTERS[1:3]
+
+ checkException(SummarizedExperiment(
+ assays=matrix(0, 2, 3, dimnames=list(NULL, colnames)),
+ colData=DataFrame(row.names=letters[1:3])),
+ "assay colnames() differ from colData rownames()", TRUE)
+
+ checkTrue(validObject(SummarizedExperiment(matrix(0, 2, 3))),
+ "NULL dimnames on assays-only construction")
+ se <- SummarizedExperiment(matrix(0, 2, 3))
+ checkTrue(is.null(colnames(se)))
+
+ checkTrue(validObject(SummarizedExperiment(
+ matrix(0, 2, 3), colData=DataFrame(x=1:3)[,FALSE])),
+ "NULL dimnames on assays and colData")
+ se <- SummarizedExperiment(matrix(0, 2, 3),
+ colData=DataFrame(x=1:3)[,FALSE])
+ checkTrue(is.null(colnames(se)))
+
+ ## dimnames from colData rownames
+ se <- SummarizedExperiment(matrix(0, 2, 3),
+ colData=DataFrame(row.names=colnames))
+ checkIdentical(colnames(se), colnames)
+ checkTrue(is.null(colnames(assay(se, withDimnames=FALSE))),
+ "don't replace NULL colnames")
+
+ ## when colData rownames == NULL, take dimnames from assay colnames
+ colnames <- LETTERS[1:3]
+ se <- SummarizedExperiment(matrix(0, 2, 3, dimnames=list(NULL, colnames)),
+ colData=DataFrame(x=colnames)[,FALSE])
+ checkIdentical(colnames(se), colnames)
+ checkIdentical(colnames(assay(se, withDimnames=FALSE)), colnames,
+ "don't remove non-NULL colnames")
+
+ ## matching colData rownames and assay colnames
+ se <- SummarizedExperiment(matrix(0, 2, 3, dimnames=list(NULL, colnames)),
+ colData=DataFrame(row.names=colnames))
+ checkIdentical(colnames(se), colnames)
+ checkIdentical(colnames(assay(se, withDimnames=FALSE)), colnames)
+}
+
+test_SummarizedExperiment_getters <- function()
+{
+ for (i in seq_along(se0List)) {
+ se0 <- se0List[[i]]
+
+ ## dim, dimnames
+ checkIdentical(c(nrow(mList[[i]]), nrow(colData0)), dim(se0))
+ checkIdentical(list(NULL, NULL), dimnames(se0))
+
+ ## col / metadata
+ checkIdentical(rowDataList[[i]], rowData(se0))
+ checkIdentical(colData0, colData(se0))
+ checkIdentical(list(), metadata(se0))
+ }
+
+ ## assays
+ m0 <- matrix(0L, 0, 0, dimnames=list(NULL, NULL))
+ m1 <- matrix(0, 0, 0, dimnames=list(NULL, NULL))
+ a <- SimpleList(a=m0, b=m1)
+ checkIdentical(a, assays(SummarizedExperiment(assays=a)))
+ ## assay
+ checkException(
+ assay(SummarizedExperiment()), "0-length assay", TRUE)
+ checkIdentical(m0,
+ assay(SummarizedExperiment(assays=a)), "default assay")
+ checkIdentical(m1,
+ assay(SummarizedExperiment(assays=a), 2),
+ "assay, numeric index")
+ checkException(
+ assay(SummarizedExperiment(assays=a), 3),
+ "invalid assay index", TRUE)
+ checkIdentical(m1,
+ assay(SummarizedExperiment(assays=a), "b"),
+ "assay, character index")
+ checkException(
+ assay(SummarizedExperiment(assays=a), "c"),
+ "invalid assay name", TRUE)
+}
+
+test_SummarizedExperiment_setters <- function()
+{
+ for (i in seq_along(se0List)) {
+ se0 <- se0List[[i]]
+
+ ## row / col / metadata<-
+ se1 <- se0
+ rowData <- rowDataList[[i]]
+
+ rowData <- rowData[rev(seq_len(nrow(rowData))),,drop=FALSE]
+ rowData(se1) <- rowData
+ checkIdentical(rowData, rowData(se1))
+
+ colData <- colData0[rev(seq_len(nrow(colData0))),,drop=FALSE]
+ colData(se1) <- colData
+ checkIdentical(colData, colData(se1))
+
+ ## The rowData (alias for mcols) setter recycles the supplied
+ ## DataFrame. This is consistent with what the mcols/elementMetadata
+ ## setter does on Vector objects in general.
+ rowData(se1) <- rowData(se0)[1:2,,drop=FALSE]
+ idx <- rep(1:2, length.out=length(se1))
+ target_se1_rowData <- rowData(se0)[idx,,drop=FALSE]
+ checkIdentical(target_se1_rowData, rowData(se1))
+
+ ## The colData setter does NOT recycle the supplied DataFrame.
+ checkException(colData(se1) <- colData(se0)[1:2,,drop=FALSE],
+ "incorrect col dimensions", TRUE)
+
+ lst <- list("foo", "bar")
+ metadata(se1) <- lst
+ checkIdentical(lst, metadata(se1))
+
+ ## assay / assays
+ se1 <- se0
+ assay(se1) <- assay(se1)+1
+ checkIdentical(assay(se0)+1, assay(se1))
+ se1 <- se0
+ assay(se1, 1) <- assay(se1, 1) + 1
+ checkIdentical(assay(se0, "m") + 1, assay(se1, "m"))
+ se1 <- se0
+ assay(se1, "m") <- assay(se1, "m") + 1
+ checkIdentical(assay(se0, "m")+1, assay(se1, "m"))
+
+ ## dimnames<-
+ se1 <- se0
+ dimnames <- list(letters[seq_len(nrow(se1))],
+ LETTERS[seq_len(ncol(se1))])
+ rownames(se1) <- dimnames[[1]]
+ colnames(se1) <- dimnames[[2]]
+ checkIdentical(dimnames, dimnames(se1))
+ colData1 <- colData0
+ row.names(colData1) <- dimnames[[2]]
+ checkIdentical(colData1, colData(se1))
+ se1 <- se0
+ dimnames(se1) <- dimnames
+ checkIdentical(dimnames, dimnames(se1))
+ dimnames(se1) <- NULL
+ checkIdentical(list(NULL, NULL), dimnames(se1))
+ }
+}
+
+test_SummarizedExperiment_subset <- function()
+{
+ for (i in seq_along(se0List)) {
+ se0 <- se0List[[i]]
+
+ ## numeric
+ se1 <- se0[2:3,]
+ checkIdentical(c(2L, ncol(se0)), dim(se1))
+ checkIdentical(rowData(se1), rowData(se0)[2:3,,drop=FALSE])
+ checkIdentical(colData(se1), colData(se0))
+ se1 <- se0[,2:3]
+ checkIdentical(c(nrow(se0), 2L), dim(se1))
+ checkIdentical(rowData(se1), rowData(se0))
+ checkIdentical(colData(se1), colData(se0)[2:3,,drop=FALSE])
+ se1 <- se0[2:3, 2:3]
+ checkIdentical(c(2L, 2L), dim(se1))
+ checkIdentical(colData(se1), colData(se0)[2:3,,drop=FALSE])
+
+ ## character
+ se1 <- se0
+ dimnames(se1) <- list(LETTERS[seq_len(nrow(se1))],
+ letters[seq_len(ncol(se1))])
+ ridx <- c("B", "C")
+ checkException(se1[LETTERS,], "i-index out of bounds", TRUE)
+ cidx <- c("b", "c")
+ checkIdentical(colData(se1[,cidx]), colData(se1)[cidx,,drop=FALSE])
+ checkIdentical(colData(se1[,"a"]), colData(se1)["a",,drop=FALSE])
+ checkException(se1[,letters], "j-index out of bounds", TRUE)
+
+ ## logical
+ se1 <- se0
+ dimnames(se1) <- list(LETTERS[seq_len(nrow(se1))],
+ letters[seq_len(ncol(se1))])
+ checkEquals(se1, se1[TRUE,])
+ checkIdentical(c(0L, ncol(se1)), dim(se1[FALSE,]))
+ checkEquals(se1, se1[,TRUE])
+ checkIdentical(c(nrow(se1), 0L), dim(se1[,FALSE]))
+ idx <- c(TRUE, FALSE) # recycling
+ se2 <- se1[idx,]
+ se2 <- se1[,idx]
+ checkIdentical(colData(se1)[idx,,drop=FALSE], colData(se2))
+
+ ## Rle
+ se1 <- se0
+ rle <- rep(c(TRUE, FALSE), each=3, length.out=nrow(se1))
+ checkIdentical(assays(se1[rle]), assays(se1[Rle(rle)]))
+ }
+
+ ## 0 columns
+ se <- SummarizedExperiment(matrix(integer(0), nrow=5))
+ checkIdentical(dim(se[1:5, ]), c(5L, 0L))
+ ## 0 rows
+ se <- SummarizedExperiment(colData=DataFrame(samples=1:10))
+ checkIdentical(dim(se[ ,1:5]), c(0L, 5L))
+}
+
+test_SummarizedExperiment_subsetassign <- function()
+{
+ for (i in seq_along(se0List)) {
+ se0 <- se0List[[i]]
+ dimnames(se0) <- list(LETTERS[seq_len(nrow(se0))],
+ letters[seq_len(ncol(se0))])
+ ## rows
+ se1 <- se0
+ se1[1:2,] <- se1[2:1,]
+ checkIdentical(colData(se0), colData(se1))
+ checkIdentical(c(metadata(se0), metadata(se0)), metadata(se1))
+ ## Rle
+ se1rle <- se1Rle <- se0
+ rle <- rep(c(TRUE, FALSE), each=3, length.out=nrow(se1))
+ se1rle[rle,] <- se1rle[rle,]
+ se1Rle[Rle(rle),] <- se1Rle[Rle(rle),]
+ checkIdentical(assays(se1rle), assays(se1Rle))
+ ## cols
+ se1 <- se0
+ se1[,1:2] <- se1[,2:1,drop=FALSE]
+ checkIdentical(colData(se0)[2:1,,drop=FALSE],
+ colData(se1)[1:2,,drop=FALSE])
+ checkIdentical(colData(se0)[-(1:2),,drop=FALSE],
+ colData(se1)[-(1:2),,drop=FALSE])
+ checkIdentical(c(metadata(se0), metadata(se0)), metadata(se1))
+ }
+
+ ## full replacement
+ se1 <- se2 <- se0List[[1]]
+ se1[,] <- se2
+ checkIdentical(se1, se2)
+}
+
+test_SummarizedExperiment_assays_4d <- function()
+{
+ ## construction/validation
+ A <- array(0, c(3, 2, 5, 4), list(c("a1", "a2", "a3"),
+ c("b1", "b2"),
+ NULL,
+ c("d1", "d2", "d3", "d4")))
+ B <- array(0, c(3, 2, 6), list(c("a1", "a2", "a3"),
+ c("b1", "oops"),
+ NULL))
+ assays0 <- SimpleList(A=A, B=B)
+ checkException(SummarizedExperiment(assays0))
+
+ dimnames(B)[1:2] <- dimnames(A)[1:2]
+ C <- array(0, c(3, 2, 4), list(NULL,
+ c("b1", "b2"),
+ c("z1", "z2", "z3", "z4")))
+
+ assays0 <- SimpleList(A=A, B=B, C=C)
+ se <- SummarizedExperiment(assays0)
+ checkTrue(validObject(se, complete=TRUE))
+
+ ## dimnames
+ checkIdentical(dimnames(A)[1:2], dimnames(se))
+ checkIdentical(dimnames(B)[1:2], dimnames(se))
+ for (i in seq_along(assays(se))) {
+ checkIdentical(assays0[[i]], assay(se, i, withDimnames=FALSE))
+ checkIdentical(dimnames(se), dimnames(assay(se, i))[1:2])
+ }
+
+ ## [
+ se2 <- se[3:2, ]
+ checkIdentical(A[3:2, , , , drop=FALSE], assay(se2, 1, withDimnames=FALSE))
+ checkIdentical(B[3:2, , , drop=FALSE], assay(se2, 2, withDimnames=FALSE))
+ checkIdentical(C[3:2, , , drop=FALSE], assay(se2, 3, withDimnames=FALSE))
+
+ ## [<-
+ A1 <- A; A1[1, , , ] <- A[1, , , , drop=FALSE] + 1
+ assays(se[1, ])[[1]] <- 1 + assays(se[1, ])[[1]]
+ checkIdentical(assays(se)[[1]], A1)
+
+ ## [, [<- don't support more than 4 dimensions
+ a <- array(0, c(3, 3, 3, 3, 3),
+ list(LETTERS[1:3], letters[1:3], NULL, NULL, NULL))
+ assays <- SimpleList(a=a)
+ se <- SummarizedExperiment(assays)
+ checkException(se[1,], silent=TRUE)
+}
+
diff --git a/inst/unitTests/test_coverage-methods.R b/inst/unitTests/test_coverage-methods.R
new file mode 100644
index 0000000..6300188
--- /dev/null
+++ b/inst/unitTests/test_coverage-methods.R
@@ -0,0 +1,56 @@
+###
+
+M1 <- matrix(1, 5, 3, dimnames=list(NULL, NULL))
+M2 <- matrix(1, 3, 3, dimnames=list(NULL, NULL))
+assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2))
+rowRangesList <-
+ list(gr=GRanges("chr1", IRanges(1:5, 10)),
+ grl=split(GRanges("chr1", IRanges(1:5, 10)), c(1,1,2,2,3)))
+names(rowRangesList[["grl"]]) <- NULL
+colData <- DataFrame(x=letters[1:3])
+
+## a list of one SE with GRanges and one with GRangesList
+rseList <-
+ list(SummarizedExperiment(
+ assays=assaysList[["gr"]],
+ rowRanges=rowRangesList[["gr"]],
+ colData=colData),
+ SummarizedExperiment(
+ assays=assaysList[["grl"]],
+ rowRanges=rowRangesList[["grl"]],
+ colData=colData))
+
+
+test_interfaces <- function()
+{
+ generic_functions <- "coverage"
+ for (fun in generic_functions) {
+ generic <- getGeneric(fun)
+ method <- getMethod(fun, "RangedSummarizedExperiment")
+ checkIdentical("x", generic at signature)
+ checkIdentical(formals(generic at .Data), formals(method at .Data))
+ }
+}
+
+test_coverage_RangedSummarizedExperiment <- function()
+{
+ for (i in 1:2) {
+ x <- rseList[[i]]
+
+ target <- coverage(rowRanges(x))
+ current <- coverage(x)
+ checkIdentical(target, current)
+
+ weight <- runif(length(x))
+ ## Issues a warning (in BioC 3.3) when rowRanges(x) is a GRangesList
+ ## object, which reveals a problem with how the "coverage" method for
+ ## GRangesList objects handles the 'weight' argument. The warning is
+ ## expected and healthy, don't try to suppress it here. It will go
+ ## away when we fix the "coverage" method for GRangesList objects
+ ## (defined in the GenomicRanges package).
+ target <- coverage(rowRanges(x), weight=weight)
+ current <- coverage(x, weight=weight)
+ checkIdentical(target, current)
+ }
+}
+
diff --git a/inst/unitTests/test_findOverlaps-methods.R b/inst/unitTests/test_findOverlaps-methods.R
new file mode 100644
index 0000000..edcd3ce
--- /dev/null
+++ b/inst/unitTests/test_findOverlaps-methods.R
@@ -0,0 +1,92 @@
+###
+
+M1 <- matrix(1, 5, 3, dimnames=list(NULL, NULL))
+M2 <- matrix(1, 3, 3, dimnames=list(NULL, NULL))
+assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2))
+rowRangesList <-
+ list(gr=GRanges("chr1", IRanges(1:5, 10)),
+ grl=split(GRanges("chr1", IRanges(1:5, 10)), c(1,1,2,2,3)))
+names(rowRangesList[["grl"]]) <- NULL
+colData <- DataFrame(x=letters[1:3])
+
+## a list of one SE with GRanges and one with GRangesList
+rseList <-
+ list(SummarizedExperiment(
+ assays=assaysList[["gr"]],
+ rowRanges=rowRangesList[["gr"]],
+ colData=colData),
+ SummarizedExperiment(
+ assays=assaysList[["grl"]],
+ rowRanges=rowRangesList[["grl"]],
+ colData=colData))
+
+
+test_interfaces <- function()
+{
+ fun <- "findOverlaps"
+ signatures <- list(
+ c("RangedSummarizedExperiment", "Vector"),
+ c("Vector", "RangedSummarizedExperiment"),
+ c("RangedSummarizedExperiment", "RangedSummarizedExperiment")
+ )
+ generic <- getGeneric(fun)
+ for (sig in signatures) {
+ method <- getMethod(fun, sig)
+ checkIdentical(c("query", "subject"), generic at signature)
+ checkIdentical(formals(generic at .Data), formals(method at .Data))
+ }
+}
+
+test_findOverlaps_methods <- function()
+{
+ identical_SummarizedExperiment <- function(x, y) {
+ x at assays <- as(assays(x), "SimpleListAssays")
+ y at assays <- as(assays(y), "SimpleListAssays")
+ identical(x, y)
+ }
+ for (i in 1:2) {
+ x <- rseList[[i]]
+ for (j in 1:2) {
+ y <- rseList[[j]]
+
+ ## findOverlaps
+ target <- findOverlaps(rowRanges(x), rowRanges(y))
+ current <- findOverlaps(x, rowRanges(y))
+ checkIdentical(target, current)
+ current <- findOverlaps(rowRanges(x), y)
+ checkIdentical(target, current)
+ current <- findOverlaps(x, y)
+ checkIdentical(target, current)
+
+ ## countOverlaps
+ target <- countOverlaps(rowRanges(x), rowRanges(y))
+ current <- countOverlaps(x, rowRanges(y))
+ checkIdentical(target, current)
+ current <- countOverlaps(rowRanges(x), y)
+ checkIdentical(target, current)
+ current <- countOverlaps(x, y)
+ checkIdentical(target, current)
+
+ ## overlapsAny
+ target <- overlapsAny(rowRanges(x), rowRanges(y))
+ current <- overlapsAny(x, rowRanges(y))
+ checkIdentical(target, current)
+ current <- overlapsAny(rowRanges(x), y)
+ checkIdentical(target, current)
+ current <- overlapsAny(x, y)
+ checkIdentical(target, current)
+
+ ## subsetByOverlaps
+ target <- subsetByOverlaps(x, rowRanges(y))
+ current <- subsetByOverlaps(x, rowRanges(y))
+ checkTrue(identical_SummarizedExperiment(target, current))
+ current <- subsetByOverlaps(x, y)
+ checkTrue(identical_SummarizedExperiment(target, current))
+
+ target <- subsetByOverlaps(rowRanges(x), rowRanges(y))
+ current <- subsetByOverlaps(rowRanges(x), y)
+ checkIdentical(target, current)
+ }
+ }
+}
+
diff --git a/inst/unitTests/test_inter-range-methods.R b/inst/unitTests/test_inter-range-methods.R
new file mode 100644
index 0000000..cee69db
--- /dev/null
+++ b/inst/unitTests/test_inter-range-methods.R
@@ -0,0 +1,52 @@
+###
+
+M1 <- matrix(1, 5, 3, dimnames=list(NULL, NULL))
+M2 <- matrix(1, 3, 3, dimnames=list(NULL, NULL))
+assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2))
+rowRangesList <-
+ list(gr=GRanges("chr1", IRanges(1:5, 10), Rle(c("+", "-"), 3:2)),
+ grl=split(GRanges("chr1", IRanges(1:5, 10)), c(1,1,2,2,3)))
+names(rowRangesList[["grl"]]) <- NULL
+colData <- DataFrame(x=letters[1:3])
+
+## a list of one SE with GRanges and one with GRangesList
+rseList <-
+ list(SummarizedExperiment(
+ assays=assaysList[["gr"]],
+ rowRanges=rowRangesList[["gr"]],
+ colData=colData),
+ SummarizedExperiment(
+ assays=assaysList[["grl"]],
+ rowRanges=rowRangesList[["grl"]],
+ colData=colData))
+
+
+test_interfaces <- function()
+{
+ generic_functions <- c("isDisjoint", "disjointBins")
+ for (fun in generic_functions) {
+ generic <- getGeneric(fun)
+ method <- getMethod(fun, "RangedSummarizedExperiment")
+ checkIdentical("x", generic at signature)
+ checkIdentical(formals(generic at .Data), formals(method at .Data))
+ }
+}
+
+test_inter_range_methods <- function()
+{
+ #for (i in 1:2) {
+ for (i in 1L) {
+ x <- rseList[[i]]
+
+ ## isDisjoint
+ target <- isDisjoint(rowRanges(x))
+ current <- isDisjoint(x)
+ checkIdentical(target, current)
+
+ ## disjointBins
+ target <- disjointBins(rowRanges(x))
+ current <- disjointBins(x)
+ checkIdentical(target, current)
+ }
+}
+
diff --git a/inst/unitTests/test_intra-range-methods.R b/inst/unitTests/test_intra-range-methods.R
new file mode 100644
index 0000000..ff6087f
--- /dev/null
+++ b/inst/unitTests/test_intra-range-methods.R
@@ -0,0 +1,94 @@
+###
+
+M1 <- matrix(1, 5, 3, dimnames=list(NULL, NULL))
+M2 <- matrix(1, 3, 3, dimnames=list(NULL, NULL))
+assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2))
+rowRangesList <-
+ list(gr=GRanges("chr1", IRanges(1:5, 10), Rle(c("+", "-"), 3:2)),
+ grl=split(GRanges("chr1", IRanges(1:5, 10)), c(1,1,2,2,3)))
+names(rowRangesList[["grl"]]) <- NULL
+colData <- DataFrame(x=letters[1:3])
+
+## a list of one SE with GRanges and one with GRangesList
+rseList <-
+ list(SummarizedExperiment(
+ assays=assaysList[["gr"]],
+ rowRanges=rowRangesList[["gr"]],
+ colData=colData),
+ SummarizedExperiment(
+ assays=assaysList[["grl"]],
+ rowRanges=rowRangesList[["grl"]],
+ colData=colData))
+
+
+test_interfaces <- function()
+{
+ generic_functions <- c("shift", "narrow", "resize",
+ "flank", "promoters",
+ "restrict", "trim")
+ for (fun in generic_functions) {
+ generic <- getGeneric(fun)
+ method <- getMethod(fun, "RangedSummarizedExperiment")
+ checkIdentical("x", generic at signature)
+ checkIdentical(formals(generic at .Data), formals(method at .Data))
+ }
+}
+
+test_intra_range_methods <- function()
+{
+ identical_SummarizedExperiment <- function(x, y) {
+ x at assays <- as(assays(x), "SimpleListAssays")
+ y at assays <- as(assays(y), "SimpleListAssays")
+ identical(x, y)
+ }
+ #for (i in 1:2) {
+ for (i in 1L) {
+ ## shift
+ target <- rseList[[i]]
+ rowRanges(target) <- shift(rowRanges(target), 50)
+ current <- shift(rseList[[i]], 50)
+ checkTrue(identical_SummarizedExperiment(target, current))
+
+ ## narrow
+ target <- rseList[[i]]
+ rowRanges(target) <- narrow(rowRanges(target), 2, -2)
+ current <- narrow(rseList[[i]], 2, -2)
+ checkTrue(identical_SummarizedExperiment(target, current))
+
+ ## resize
+ target <- rseList[[i]]
+ rowRanges(target) <- resize(rowRanges(target), 8)
+ current <- resize(rseList[[i]], 8)
+ checkTrue(identical_SummarizedExperiment(target, current))
+
+ ## flank
+ target <- rseList[[i]]
+ rowRanges(target) <- flank(rowRanges(target), 5, both=TRUE)
+ current <- flank(rseList[[i]], 5, both=TRUE)
+ checkTrue(identical_SummarizedExperiment(target, current))
+
+ ## promoters
+ target <- rseList[[i]]
+ rowRanges(target) <- promoters(rowRanges(target),
+ upstream=20, downstream=5)
+ current <- promoters(rseList[[i]], upstream=20, downstream=5)
+ checkTrue(identical_SummarizedExperiment(target, current))
+
+ ## restrict
+ target <- rseList[[i]]
+ rowRanges(target) <- restrict(rowRanges(target), start=2, end=3,
+ keep.all.ranges=TRUE)
+ current <- restrict(rseList[[i]], start=2, end=3,
+ keep.all.ranges=TRUE)
+ checkTrue(identical_SummarizedExperiment(target, current))
+
+ ## trim
+ suppressWarnings(seqlengths(rseList[[i]]) <- 8)
+ target <- rseList[[i]]
+ rowRanges(target) <- trim(rowRanges(target))
+ current <- trim(rseList[[i]])
+ checkTrue(identical_SummarizedExperiment(target, current))
+ seqlengths(rseList[[i]]) <- NA
+ }
+}
+
diff --git a/inst/unitTests/test_makeSummarizedExperimentFromDataFrame.R b/inst/unitTests/test_makeSummarizedExperimentFromDataFrame.R
new file mode 100644
index 0000000..a16aca2
--- /dev/null
+++ b/inst/unitTests/test_makeSummarizedExperimentFromDataFrame.R
@@ -0,0 +1,56 @@
+##
+
+rowNames <- paste0("GENE", letters[5:1])
+
+range_info <- list(chr="chr2", start = 11:15, end = 12:16,
+ strand = c("+", "-", "+", "*", "."))
+expr_info <- list(expr0 = 3:7, expr1 = 8:12, expr2 = 12:16)
+
+df <- as.data.frame(c(range_info, expr_info), row.names = rowNames)
+DF <- DataFrame(c(range_info, expr_info), row.names = rowNames)
+
+test_makeSummarizedExperimentFromDataFrame <- function()
+{
+ validObject(makeSummarizedExperimentFromDataFrame(df))
+ validObject(makeSummarizedExperimentFromDataFrame(DF))
+
+ rangesA <- GRanges(as.data.frame(range_info, row.names = rowNames))
+ rangesB <- rowRanges(makeSummarizedExperimentFromDataFrame(df))
+ # Check rowRanges to be identical
+ checkIdentical(rangesA, rangesB)
+ # Check assay matrix and expr_info matrix are identical
+ checkIdentical(assay(makeSummarizedExperimentFromDataFrame(df)),
+ as.matrix(as.data.frame(expr_info, row.names = rowNames)))
+ checkIdentical(assay(makeSummarizedExperimentFromDataFrame(DF)),
+ as.matrix(as.data.frame(expr_info, row.names = rowNames)))
+
+ checkEquals(makeSummarizedExperimentFromDataFrame(df),
+ makeSummarizedExperimentFromDataFrame(DF))
+
+ checkException(
+ makeSummarizedExperimentFromDataFrame(
+ cbind(df, expr3 = letters[seq_len(nrow(df))])))
+
+ checkException(
+ makeSummarizedExperimentFromDataFrame(
+ cbind(DF, DataFrame(expr3 = letters[seq_len(nrow(df))]))))
+
+ checkIdentical(nrow(df),
+ length(rowRanges(
+ makeSummarizedExperimentFromDataFrame(df))))
+
+ checkIdentical(nrow(DF),
+ length(rowRanges(
+ makeSummarizedExperimentFromDataFrame(DF))))
+
+ checkIdentical(colnames(makeSummarizedExperimentFromDataFrame(df)),
+ names(expr_info))
+ checkIdentical(rownames(makeSummarizedExperimentFromDataFrame(df)),
+ rowNames)
+
+ checkIdentical(colnames(makeSummarizedExperimentFromDataFrame(DF)),
+ names(expr_info))
+ checkIdentical(rownames(makeSummarizedExperimentFromDataFrame(DF)),
+ rowNames)
+}
+
diff --git a/inst/unitTests/test_makeSummarizedExperimentFromExpressionSet.R b/inst/unitTests/test_makeSummarizedExperimentFromExpressionSet.R
new file mode 100644
index 0000000..711b4d7
--- /dev/null
+++ b/inst/unitTests/test_makeSummarizedExperimentFromExpressionSet.R
@@ -0,0 +1,187 @@
+M1 <- matrix(1, 5, 3, dimnames=list(NULL, NULL))
+M2 <- matrix(1, 3, 3, dimnames=list(NULL, NULL))
+assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2))
+rowRangesList <-
+ list(gr=GRanges("chr1", IRanges(1:5, 10)),
+ grl=split(GRanges("chr1", IRanges(1:5, 10)), c(1,1,2,2,3)))
+names(rowRangesList[["grl"]]) <- NULL
+colData <- DataFrame(x=letters[1:3])
+
+## a list of one SE with GRanges and one with GRangesList
+rseList <-
+ list(SummarizedExperiment(
+ assays=assaysList[["gr"]],
+ rowRanges=rowRangesList[["gr"]],
+ colData=colData),
+ SummarizedExperiment(
+ assays=assaysList[["grl"]],
+ rowRanges=rowRangesList[["grl"]],
+ colData=colData))
+
+
+test_SummarizedExperiment_GenomicRanges_coercion <- function()
+{
+ eset1 <- ExpressionSet()
+
+ checkTrue(validObject(eset1))
+
+ se1 <- as(eset1, "RangedSummarizedExperiment")
+
+ checkTrue(validObject(se1))
+
+ data("sample.ExpressionSet", package = "Biobase")
+
+ eset2 <- sample.ExpressionSet
+ checkTrue(validObject(eset2))
+
+ se2 <- as(eset2, "RangedSummarizedExperiment")
+
+ checkTrue(validObject(se2))
+
+ checkIdentical(experimentData(eset2),
+ metadata(se2)$experimentData)
+
+ checkIdentical(annotation(eset2),
+ metadata(se2)$annotation)
+
+ checkIdentical(protocolData(eset2),
+ metadata(se2)$protocolData)
+
+ eset2Assays <- SimpleList(as.list(assayData(eset2)))
+ se2Assays <- assays(se2)
+ checkIdentical(eset2Assays$exprs, se2Assays$exprs)
+ checkIdentical(eset2Assays$se.exprs, se2Assays$se.exprs)
+
+ checkIdentical(featureNames(eset2), rownames(se2))
+
+ checkIdentical(sampleNames(eset2), colnames(se2))
+}
+
+test_GenomicRanges_SummarizedExperiment_coercion <- function()
+{
+ ## empty SE
+ simpleSE <- SummarizedExperiment()
+
+ eset1 <- as(simpleSE, "ExpressionSet")
+
+ checkTrue(validObject(eset1))
+
+ ## Back and forth empty ES
+ simpleES <- ExpressionSet()
+
+ simpleES2 <- as(as(simpleES, "RangedSummarizedExperiment"),
+ "ExpressionSet")
+
+ checkTrue(validObject(simpleES2))
+
+ checkEquals(as.list(assayData(simpleES)),
+ as.list(assayData(simpleES2)))
+
+ ## Simple SE
+ simpleSE <- rseList[[1]]
+ assayNames(simpleSE) <- "exprs" # No warning 'No assay named exprs..."
+ eset2 <- as(simpleSE, "ExpressionSet")
+ checkTrue(validObject(eset2))
+
+ ## The ExpressionSet features should have the data from the
+ ## SummarizedExperiment rows if they are from GRanges.
+ checkIdentical(pData(featureData(eset2)),
+ as.data.frame(rowRanges(rseList[[1]])))
+
+ # the rowRanges are retained if the object has them to begin with.
+ se2_2 <- as(eset2, "RangedSummarizedExperiment")
+ rr_se2_2 <- unname(rowRanges(se2_2))
+ rr_eset2 <- rowRanges(rseList[[1]])
+ checkEquals(rr_se2_2, rr_eset2)
+
+ simpleSE <- rseList[[2]]
+ assayNames(simpleSE) <- "exprs" # No warning 'No assay named exprs..."
+ eset3 <- as(simpleSE, "ExpressionSet")
+ checkTrue(validObject(eset3))
+
+ ## The ExpressionSet features should not have the data from the
+ ## SummarizedExperiment rows if they are from GRangesList, but they
+ ## should be empty and the same length as the number of ranges.
+ checkEquals(unname(NROW(featureData(eset3))),
+ unname(length(rowRanges(rseList[[2]]))))
+
+ data("sample.ExpressionSet", package = "Biobase")
+ eset4 <- sample.ExpressionSet
+
+ eset5 <- as(as(eset4, "RangedSummarizedExperiment"), "ExpressionSet")
+
+ checkTrue(validObject(eset5))
+
+ ## this is necessary because the order in environments is undefined.
+ compareLists <- function(x, y) {
+ nmsX <- names(x)
+ nmsY <- names(y)
+
+ reorderY <- match(nmsY, nmsX)
+
+ checkIdentical(x, y[reorderY])
+ }
+
+ compareLists(as.list(assayData(eset4)),
+ as.list(assayData(eset5)))
+
+ checkIdentical(experimentData(eset4),
+ experimentData(eset5))
+
+ checkIdentical(annotation(eset4),
+ annotation(eset5))
+
+ checkIdentical(protocolData(eset4),
+ protocolData(eset5))
+
+ checkIdentical(featureNames(eset4),
+ featureNames(eset5))
+
+ checkIdentical(sampleNames(eset4),
+ sampleNames(eset5))
+}
+
+test_GenomicRanges_SummarizedExperiment_coercion_mappingFunctions <- function()
+{
+ ## naiveRangeMapper
+ ## valid object from empty object
+ checkTrue(validObject(makeSummarizedExperimentFromExpressionSet(ExpressionSet())))
+
+ ## valid object from sample ExpressionSet
+ data("sample.ExpressionSet", package = "Biobase")
+ eset1 <- sample.ExpressionSet
+ checkTrue(validObject(makeSummarizedExperimentFromExpressionSet(eset1)))
+
+ ## makeSummarizedExperimentFromExpressionSet should be the same as `as`
+ ## with default args
+ checkEquals(makeSummarizedExperimentFromExpressionSet(eset1),
+ as(eset1, "RangedSummarizedExperiment"))
+
+ ## probeRangeMapper
+ ## valid object from empty object
+ checkTrue(validObject(
+ makeSummarizedExperimentFromExpressionSet(ExpressionSet(),
+ probeRangeMapper)))
+
+ ## valid object from sample ExpressionSet
+ se1 <- makeSummarizedExperimentFromExpressionSet(eset1, probeRangeMapper)
+ checkTrue(validObject(se1))
+
+ ## Granges returned have rownames that were from the featureNames
+ checkTrue(all(rownames(rowRanges(se1)) %in% featureNames(eset1)))
+
+ ## geneRangeMapper
+ ## valid object from empty object
+ checkTrue(validObject(
+ makeSummarizedExperimentFromExpressionSet(ExpressionSet(),
+ geneRangeMapper(NULL))))
+
+ ## valid object from sample ExpressionSet
+ se2 <- makeSummarizedExperimentFromExpressionSet(eset1,
+ geneRangeMapper("TxDb.Hsapiens.UCSC.hg19.knownGene"))
+ checkTrue(validObject(se2))
+
+ ## Granges returned have rownames that were from the featureNames
+ checkTrue(all(rownames(rowRanges(se2)) %in% featureNames(eset1)))
+}
+
diff --git a/inst/unitTests/test_nearest-methods.R b/inst/unitTests/test_nearest-methods.R
new file mode 100644
index 0000000..38aa74e
--- /dev/null
+++ b/inst/unitTests/test_nearest-methods.R
@@ -0,0 +1,70 @@
+###
+
+M1 <- matrix(1, 5, 3, dimnames=list(NULL, NULL))
+M2 <- matrix(1, 3, 3, dimnames=list(NULL, NULL))
+assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2))
+rowRangesList <-
+ list(gr=GRanges("chr1", IRanges(1:5, 10)),
+ grl=split(GRanges("chr1", IRanges(1:5, 10)), c(1,1,2,2,3)))
+names(rowRangesList[["grl"]]) <- NULL
+colData <- DataFrame(x=letters[1:3])
+
+## a list of one SE with GRanges and one with GRangesList
+rseList <-
+ list(SummarizedExperiment(
+ assays=assaysList[["gr"]],
+ rowRanges=rowRangesList[["gr"]],
+ colData=colData),
+ SummarizedExperiment(
+ assays=assaysList[["grl"]],
+ rowRanges=rowRangesList[["grl"]],
+ colData=colData))
+
+
+.GENERIC_SIGNATURES <- list(
+ precede=c("x", "subject"),
+ follow=c("x", "subject"),
+ nearest=c("x", "subject"),
+ distance=c("x", "y"),
+ distanceToNearest=c("x", "subject")
+)
+
+test_interfaces <- function()
+{
+ method_signatures <- list(
+ c("RangedSummarizedExperiment", "ANY"),
+ c("ANY", "RangedSummarizedExperiment"),
+ c("RangedSummarizedExperiment", "RangedSummarizedExperiment")
+ )
+ for (fun in names(.GENERIC_SIGNATURES)) {
+ generic <- getGeneric(fun)
+ checkIdentical(.GENERIC_SIGNATURES[[fun]], generic at signature)
+ for (sig in method_signatures) {
+ method <- getMethod(fun, sig)
+ checkIdentical(formals(generic at .Data), formals(method at .Data))
+ }
+ }
+}
+
+test_nearest_methods <- function()
+{
+ #for (i in 1:2) {
+ for (i in 1L) {
+ x <- rseList[[i]]
+ #for (j in 1:2) {
+ for (j in 1L) {
+ y <- rseList[[j]]
+ for (fun in names(.GENERIC_SIGNATURES)) {
+ fun <- get(fun)
+ target <- fun(rowRanges(x), rowRanges(y))
+ current <- fun(x, rowRanges(y))
+ checkIdentical(target, current)
+ current <- fun(rowRanges(x), y)
+ checkIdentical(target, current)
+ current <- fun(x, y)
+ checkIdentical(target, current)
+ }
+ }
+ }
+}
+
diff --git a/man/Assays-class.Rd b/man/Assays-class.Rd
new file mode 100644
index 0000000..063c706
--- /dev/null
+++ b/man/Assays-class.Rd
@@ -0,0 +1,172 @@
+\name{Assays-class}
+\docType{class}
+
+\alias{class:Assays}
+\alias{Assays-class}
+\alias{Assays}
+\alias{length,Assays-method}
+\alias{names,Assays-method}
+\alias{names<-,Assays-method}
+\alias{[[,Assays,ANY,ANY-method}
+\alias{[[<-,Assays,ANY,ANY-method}
+\alias{dim,Assays-method}
+\alias{[,Assays,ANY-method}
+\alias{[,Assays,ANY,ANY,ANY-method}
+\alias{[<-,Assays,ANY,ANY,ANY-method}
+\alias{rbind,Assays-method}
+\alias{cbind,Assays-method}
+\alias{arbind,Matrix-method}
+\alias{acbind,Matrix-method}
+
+\alias{class:SimpleListAssays}
+\alias{SimpleListAssays-class}
+\alias{SimpleListAssays}
+
+\alias{class:ShallowData}
+\alias{ShallowData-class}
+\alias{ShallowData}
+
+\alias{class:ShallowSimpleListAssays}
+\alias{ShallowSimpleListAssays-class}
+\alias{ShallowSimpleListAssays}
+\alias{coerce,SimpleList,ShallowSimpleListAssays-method}
+\alias{coerce,ShallowSimpleListAssays,SimpleList-method}
+
+\alias{class:AssaysInEnv}
+\alias{AssaysInEnv-class}
+\alias{AssaysInEnv}
+\alias{length,AssaysInEnv-method}
+\alias{names,AssaysInEnv-method}
+\alias{names<-,AssaysInEnv-method}
+\alias{[[,AssaysInEnv,ANY,ANY-method}
+\alias{[[<-,AssaysInEnv,ANY,ANY-method}
+\alias{coerce,SimpleList,AssaysInEnv-method}
+\alias{coerce,AssaysInEnv,SimpleList-method}
+
+\title{Assays objects}
+
+\description{
+ The Assays virtual class and its methods provide a formal abstraction
+ of the assays slot of \link{SummarizedExperiment} objects.
+
+ SimpleListAssays and ShallowSimpleListAssays are concrete subclasses of
+ Assays with the latter being currently the default implementation of Assays
+ objects. Other implementations (e.g. disk-based) could easily be added.
+
+ Note that these classes are not meant to be used directly by the end-user
+ and the material in this man page is aimed at package developers.
+}
+
+\details{
+ Assays objects have a list-like semantics with elements having matrix- or
+ array-like semantics (e.g., \code{dim}, \code{dimnames}).
+
+ The Assays API consists of:
+ \itemize{
+ \item (a) The \code{Assays()} constructor function.
+ \item (b) Lossless back and forth coercion from/to \link{SimpleList}.
+ The coercion method from \link{SimpleList} doesn't need (and
+ should not) validate the returned object.
+ \item (c) \code{length}, \code{names}, \code{`names<-`},
+ \code{[[}, \code{`[[<-`},
+ \code{dim}, \code{[}, \code{`[<-`}, \code{rbind}, \code{cbind}.
+ }
+ An Assays concrete subclass needs to implement (b) (required) plus,
+ optionally any of the methods in (c).
+
+ IMPORTANT: Methods that return a modified Assays object (a.k.a.
+ endomorphisms), that is, \code{[} as well as replacement methods
+ \code{names<-}, \code{[[<-}, and \code{[<-}, must respect the
+ \emph{copy-on-change contract}.
+ With objects that don't make use of references internally, the developer
+ doesn't need to take any special action for that because it's automatically
+ taken care of by R itself. However, for objects that do make use of
+ references internally (e.g. environments, external pointers, pointer to a
+ file on disk, etc...), the developer needs to be careful to implement
+ endomorphisms with copy-on-change semantics.
+ This can easily be achieved (and is what the default methods for Assays
+ objects do) by performaing a full (deep) copy of the object before modifying
+ it instead of trying to modify it in-place. Note that the full (deep) copy
+ is not always necessary in order to achieve copy-on-change semantics: it's
+ enough (and often preferrable for performance reasons) to copy only the
+ parts of the objects that need to be modified.
+
+ Assays has currently 3 implementations which are formalized by concrete
+ subclasses SimpleListAssays, ShallowSimpleListAssays, and AssaysInEnv.
+ ShallowSimpleListAssays is the default. AssaysInEnv is a \emph{broken}
+ alternative to ShallowSimpleListAssays that does NOT respect the
+ \emph{copy-on-change contract}. It is only provided for illustration
+ purposes (see source file Assays-class.R for the details).
+
+ A little more detail about ShallowSimpleListAssays: a small reference
+ class hierarchy (not exported from the \pkg{GenomicRanges} name space)
+ defines a reference class ShallowData with a single field \code{data}
+ of type \code{ANY}, and a derived class ShallowSimpleListAssays
+ that specializes the type of \code{data} as \link{SimpleList}, and
+ \code{contains=c("ShallowData", "Assays")}. The assays slot of a
+ \link{SummarizedExperiment} object contains an instance of
+ ShallowSimpleListAssays.
+}
+
+\author{Martin Morgan, \url{mtmorgan at fhcrc.org}}
+
+\seealso{
+ \itemize{
+ \item \link{SummarizedExperiment} objects.
+
+ \item \link[S4Vectors]{SimpleList} objects in the \pkg{S4Vectors} package.
+ }
+}
+
+\examples{
+## ---------------------------------------------------------------------
+## DIRECT MANIPULATION OF Assays OBJECTS
+## ---------------------------------------------------------------------
+m1 <- matrix(runif(24), ncol=3)
+m2 <- matrix(runif(24), ncol=3)
+a <- Assays(SimpleList(m1, m2))
+a
+
+as(a, "SimpleList")
+
+length(a)
+a[[2]]
+dim(a)
+
+b <- a[-4, 2]
+b
+length(b)
+b[[2]]
+dim(b)
+
+names(a)
+names(a) <- c("a1", "a2")
+names(a)
+a[["a2"]]
+
+rbind(a, a)
+cbind(a, a)
+
+## ---------------------------------------------------------------------
+## COPY-ON-CHANGE CONTRACT
+## ---------------------------------------------------------------------
+
+## ShallowSimpleListAssays objects have copy-on-change semantics but not
+## AssaysInEnv objects. For example:
+ssla <- as(SimpleList(m1, m2), "ShallowSimpleListAssays")
+aie <- as(SimpleList(m1, m2), "AssaysInEnv")
+
+## No names on 'ssla' and 'aie':
+names(ssla)
+names(aie)
+
+ssla2 <- ssla
+aie2 <- aie
+names(ssla2) <- names(aie2) <- c("A1", "A2")
+
+names(ssla) # still NULL (as expected)
+
+names(aie) # changed! (because the names<-,AssaysInEnv method is not
+ # implemented in a way that respects the copy-on-change
+ # contract)
+}
diff --git a/man/RangedSummarizedExperiment-class.Rd b/man/RangedSummarizedExperiment-class.Rd
new file mode 100644
index 0000000..061fd68
--- /dev/null
+++ b/man/RangedSummarizedExperiment-class.Rd
@@ -0,0 +1,335 @@
+\name{RangedSummarizedExperiment-class}
+\docType{class}
+
+% Class
+\alias{class:RangedSummarizedExperiment}
+\alias{RangedSummarizedExperiment-class}
+\alias{RangedSummarizedExperiment}
+
+% Constructor
+\alias{SummarizedExperiment}
+\alias{SummarizedExperiment,SimpleList-method}
+\alias{SummarizedExperiment,ANY-method}
+\alias{SummarizedExperiment,list-method}
+\alias{SummarizedExperiment,missing-method}
+
+% Coercion
+\alias{coerce,RangedSummarizedExperiment,SummarizedExperiment-method}
+\alias{coerce,SummarizedExperiment,RangedSummarizedExperiment-method}
+
+% Accessors
+\alias{rowRanges}
+\alias{rowRanges,RangedSummarizedExperiment-method}
+\alias{rowRanges<-}
+\alias{rowRanges<-,SummarizedExperiment,GenomicRanges-method}
+\alias{rowRanges<-,SummarizedExperiment,GRangesList-method}
+\alias{names,RangedSummarizedExperiment-method}
+\alias{names<-,RangedSummarizedExperiment-method}
+\alias{dimnames,RangedSummarizedExperiment-method}
+\alias{dimnames<-,RangedSummarizedExperiment,list-method}
+
+% GenomicRanges compatibility methods
+\alias{Compare,ANY,RangedSummarizedExperiment-method}
+\alias{Compare,RangedSummarizedExperiment,ANY-method}
+\alias{Compare,RangedSummarizedExperiment,RangedSummarizedExperiment-method}
+\alias{pcompare,ANY,RangedSummarizedExperiment-method}
+\alias{pcompare,RangedSummarizedExperiment,ANY-method}
+\alias{pcompare,RangedSummarizedExperiment,RangedSummarizedExperiment-method}
+\alias{duplicated,RangedSummarizedExperiment-method}
+\alias{elementMetadata,RangedSummarizedExperiment-method}
+\alias{elementMetadata<-,RangedSummarizedExperiment-method}
+\alias{end,RangedSummarizedExperiment-method}
+\alias{end<-,RangedSummarizedExperiment-method}
+\alias{granges,RangedSummarizedExperiment-method}
+\alias{is.unsorted,RangedSummarizedExperiment-method}
+\alias{mcols,RangedSummarizedExperiment-method}
+\alias{mcols<-,RangedSummarizedExperiment-method}
+\alias{order,RangedSummarizedExperiment-method}
+\alias{ranges,RangedSummarizedExperiment-method}
+\alias{ranges<-,RangedSummarizedExperiment-method}
+\alias{rank,RangedSummarizedExperiment-method}
+\alias{seqinfo,RangedSummarizedExperiment-method}
+\alias{seqinfo<-,RangedSummarizedExperiment-method}
+\alias{seqnames,RangedSummarizedExperiment-method}
+\alias{sort,RangedSummarizedExperiment-method}
+\alias{split,RangedSummarizedExperiment-method}
+\alias{split,RangedSummarizedExperiment,ANY-method}
+\alias{start,RangedSummarizedExperiment-method}
+\alias{start<-,RangedSummarizedExperiment-method}
+\alias{strand,RangedSummarizedExperiment-method}
+\alias{strand<-,RangedSummarizedExperiment,ANY-method}
+\alias{subset,RangedSummarizedExperiment-method}
+\alias{width,RangedSummarizedExperiment-method}
+\alias{width<-,RangedSummarizedExperiment-method}
+
+\alias{updateObject,SummarizedExperiment-method}
+
+\title{RangedSummarizedExperiment objects}
+
+\description{
+
+ The RangedSummarizedExperiment class is a matrix-like container where rows
+ represent ranges of interest (as a \link{GRanges} or \link{GRangesList}
+ object) and columns represent samples (with sample data summarized as a
+ \link{DataFrame}). A RangedSummarizedExperiment contains one or more
+ assays, each represented by a matrix-like object of numeric or other mode.
+
+ RangedSummarizedExperiment is a subclass of \link{SummarizedExperiment} and,
+ as such, all the methods documented in \code{?\link{SummarizedExperiment}}
+ also work on a RangedSummarizedExperiment object. The methods documented
+ below are additional methods that are specific to RangedSummarizedExperiment
+ objects.
+}
+
+\usage{
+
+## Constructor
+
+SummarizedExperiment(assays, ...)
+\S4method{SummarizedExperiment}{SimpleList}(assays, rowData=NULL, rowRanges=GRangesList(),
+ colData=DataFrame(), metadata=list())
+\S4method{SummarizedExperiment}{ANY}(assays, ...)
+\S4method{SummarizedExperiment}{list}(assays, ...)
+\S4method{SummarizedExperiment}{missing}(assays, ...)
+
+## Accessors
+
+rowRanges(x, ...)
+rowRanges(x, ...) <- value
+
+## Subsetting
+
+\S4method{subset}{RangedSummarizedExperiment}(x, subset, select, ...)
+
+## rowRanges access
+## see 'GRanges compatibility', below
+}
+
+\arguments{
+
+ \item{assays}{A \code{list} or \code{SimpleList} of matrix-like elements,
+ or a matrix-like object. All elements of the list must have the same
+ dimensions, and dimension names (if present) must be consistent
+ across elements and with the row names of \code{rowRanges} and
+ \code{colData}.}
+
+ \item{rowData}{A \link[S4Vectors]{DataFrame} object describing
+ the rows. Row names, if present, become the row names of the
+ SummarizedExperiment object. The number of rows of the
+ \link[S4Vectors]{DataFrame} must equal the number of rows of the
+ matrices in \code{assays}.}
+
+ \item{rowRanges}{A \link[GenomicRanges]{GRanges} or
+ \link[GenomicRanges]{GRangesList} object describing the ranges of
+ interest. Names, if present, become the row names of the
+ SummarizedExperiment object. The length of the
+ \link[GenomicRanges]{GRanges} or \link[GenomicRanges]{GRangesList}
+ must equal the number of rows of the matrices in \code{assays}.
+ If \code{rowRanges} is missing, a \link{SummarizedExperiment}
+ instance is returned.}
+
+ \item{colData}{An optional \link{DataFrame} describing the
+ samples. Row names, if present, become the column names of the
+ RangedSummarizedExperiment.}
+
+ \item{metadata}{An optional \code{list} of arbitrary content
+ describing the overall experiment.}
+
+ \item{...}{For \code{SummarizedExperiment}, S4 methods \code{list}
+ and \code{matrix}, arguments identical to those of the
+ \code{SimpleList} method.
+
+ For \code{rowRanges}, ignored.
+
+ }
+
+ \item{x}{A RangedSummarizedExperiment object. The \code{rowRanges} setter
+ will also accept a \link{SummarizedExperiment} object and will first
+ coerce it to RangedSummarizedExperiment before it sets \code{value} on
+ it.}
+
+ \item{value}{A \link[GenomicRanges]{GRanges} or
+ \link[GenomicRanges]{GRangesList} object.}
+
+ \item{subset}{An expression which, when evaluated in the
+ context of \code{rowRanges(x)}, is a logical vector indicating
+ elements or rows to keep: missing values are taken as false.}
+
+ \item{select}{An expression which, when evaluated in the
+ context of \code{colData(x)}, is a logical vector indicating
+ elements or rows to keep: missing values are taken as false.}
+
+}
+
+\details{
+
+ The rows of a RangedSummarizedExperiment object represent ranges
+ (in genomic coordinates) of interest. The ranges of interest are
+ described by a \link{GRanges} or a \link{GRangesList} object, accessible
+ using the \code{rowRanges} function, described below. The \link{GRanges}
+ and \link{GRangesList} classes contains sequence (e.g., chromosome) name,
+ genomic coordinates, and strand information. Each range can be
+ annotated with additional data; this data might be used to describe
+ the range or to summarize results (e.g., statistics of differential
+ abundance) relevant to the range. Rows may or may not have row names;
+ they often will not.
+
+}
+
+\section{Constructor}{
+
+ RangedSummarizedExperiment instances are constructed using the
+ \code{SummarizedExperiment} function with arguments outlined above.
+
+}
+
+\section{Accessors}{
+
+ In the following code snippets, \code{x} is a RangedSummarizedExperiment
+ object.
+
+ \describe{
+
+ \item{\code{rowRanges(x)}, \code{rowRanges(x) <- value}:}{Get or set the
+ row data. \code{value} is a \code{GenomicRanges} object. Row
+ names of \code{value} must be NULL or consistent with the existing
+ row names of \code{x}.}
+
+ }
+}
+
+\section{GRanges compatibility (rowRanges access)}{
+
+ Many \link{GRanges} and \link{GRangesList} operations are supported on
+ RangedSummarizedExperiment objects, using \code{rowRanges}.
+
+ Supported operations include: \code{\link{pcompare}},
+ \code{\link{duplicated}}, \code{\link{end}}, \code{\link{end<-}},
+ \code{\link{granges}}, \code{\link{is.unsorted}}, \code{\link{match}},
+ \code{\link{mcols}}, \code{\link{mcols<-}}, \code{\link{order}},
+ \code{\link{ranges}}, \code{\link{ranges<-}}, \code{\link{rank}},
+ \code{\link{seqinfo}}, \code{\link{seqinfo<-}}, \code{\link{seqnames}},
+ \code{\link{sort}}, \code{\link{start}}, \code{\link{start<-}},
+ \code{\link{strand}}, \code{\link{strand<-}},
+ \code{\link{width}}, \code{\link{width<-}}.
+
+ See also \code{?\link[SummarizedExperiment]{shift}},
+ \code{?\link[SummarizedExperiment]{isDisjoint}},
+ \code{?\link[SummarizedExperiment]{coverage}},
+ \code{?\link[SummarizedExperiment]{findOverlaps}}, and
+ \code{?\link[SummarizedExperiment]{nearest}} for more
+ \emph{GRanges compatibility methods}.
+
+ Not all \link{GRanges} operations are supported, because
+ they do not make sense for RangedSummarizedExperiment objects
+ (e.g., length, name, as.data.frame, c, splitAsList), involve
+ non-trivial combination or splitting of rows (e.g., disjoin, gaps,
+ reduce, unique), or have not yet been implemented (Ops, map, window,
+ window<-).
+
+}
+
+\section{Subsetting}{
+
+ In the code snippets below, \code{x} is a RangedSummarizedExperiment
+ object.
+
+ \describe{
+
+ \item{\code{subset(x, subset, select)}:}{Create a subset of \code{x}
+ using an expression \code{subset} referring to columns of
+ \code{rowRanges(x)} (including \sQuote{seqnames}, \sQuote{start},
+ \sQuote{end}, \sQuote{width}, \sQuote{strand}, and
+ \code{names(rowData(x))}) and / or \code{select} referring to
+ column names of \code{colData(x)}.}
+
+ }
+
+}
+
+\section{Extension}{
+
+ RangedSummarizedExperiment is implemented as an S4 class, and can be
+ extended in the usual way, using \code{contains="RangedSummarizedExperiment"}
+ in the new class definition.
+
+}
+
+\author{Martin Morgan, \url{mtmorgan at fhcrc.org}}
+
+\seealso{
+ \itemize{
+ \item \link{SummarizedExperiment} objects.
+
+ \item \link[SummarizedExperiment]{shift},
+ \link[SummarizedExperiment]{isDisjoint},
+ \link[SummarizedExperiment]{coverage},
+ \link[SummarizedExperiment]{findOverlaps}, and
+ \link[SummarizedExperiment]{nearest} for more
+ \emph{GRanges compatibility methods}.
+
+ \item \link[GenomicRanges]{GRanges} objects in the \pkg{GenomicRanges}
+ package.
+ }
+}
+
+\examples{
+nrows <- 200; ncols <- 6
+counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
+rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),
+ IRanges(floor(runif(200, 1e5, 1e6)), width=100),
+ strand=sample(c("+", "-"), 200, TRUE),
+ feature_id=sprintf("ID\%03d", 1:200))
+colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
+ row.names=LETTERS[1:6])
+rse <- SummarizedExperiment(assays=SimpleList(counts=counts),
+ rowRanges=rowRanges, colData=colData)
+rse
+dim(rse)
+dimnames(rse)
+assayNames(rse)
+head(assay(rse))
+assays(rse) <- endoapply(assays(rse), asinh)
+head(assay(rse))
+
+rowRanges(rse)
+rowData(rse) # same as 'mcols(rowRanges(rse))'
+colData(rse)
+
+rse[, rse$Treatment == "ChIP"]
+
+## cbind() combines objects with the same ranges but different samples:
+rse1 <- rse
+rse2 <- rse1[,1:3]
+colnames(rse2) <- letters[1:ncol(rse2)]
+cmb1 <- cbind(rse1, rse2)
+dim(cmb1)
+dimnames(cmb1)
+
+## rbind() combines objects with the same samples but different ranges:
+rse1 <- rse
+rse2 <- rse1[1:50,]
+rownames(rse2) <- letters[1:nrow(rse2)]
+cmb2 <- rbind(rse1, rse2)
+dim(cmb2)
+dimnames(cmb2)
+
+## Coercion to/from SummarizedExperiment:
+se0 <- as(rse, "SummarizedExperiment")
+se0
+
+as(se0, "RangedSummarizedExperiment")
+
+## Setting rowRanges on a SummarizedExperiment object turns it into a
+## RangedSummarizedExperiment object:
+se <- se0
+rowRanges(se) <- rowRanges
+se # RangedSummarizedExperiment
+
+## Sanity checks:
+stopifnot(identical(assays(se0), assays(rse)))
+stopifnot(identical(dim(se0), dim(rse)))
+stopifnot(identical(dimnames(se0), dimnames(rse)))
+stopifnot(identical(rowData(se0), rowData(rse)))
+stopifnot(identical(colData(se0), colData(rse)))
+}
diff --git a/man/SummarizedExperiment-class.Rd b/man/SummarizedExperiment-class.Rd
new file mode 100644
index 0000000..0d49063
--- /dev/null
+++ b/man/SummarizedExperiment-class.Rd
@@ -0,0 +1,454 @@
+\name{SummarizedExperiment-class}
+\docType{class}
+
+% Class
+\alias{class:SummarizedExperiment}
+\alias{SummarizedExperiment-class}
+
+% Accessors
+\alias{length,SummarizedExperiment-method}
+\alias{names,SummarizedExperiment-method}
+\alias{names<-,SummarizedExperiment-method}
+\alias{rowData}
+\alias{rowData,SummarizedExperiment-method}
+\alias{rowData<-}
+\alias{rowData<-,SummarizedExperiment-method}
+\alias{colData}
+\alias{colData,SummarizedExperiment-method}
+\alias{colData<-}
+\alias{colData<-,SummarizedExperiment,DataFrame-method}
+\alias{assays}
+\alias{assays,SummarizedExperiment-method}
+\alias{assays<-}
+\alias{assays<-,SummarizedExperiment,SimpleList-method}
+\alias{assays<-,SummarizedExperiment,list-method}
+\alias{assay}
+\alias{assay,SummarizedExperiment,missing-method}
+\alias{assay,SummarizedExperiment,numeric-method}
+\alias{assay,SummarizedExperiment,character-method}
+\alias{assay<-}
+\alias{assay<-,SummarizedExperiment,missing-method}
+\alias{assay<-,SummarizedExperiment,numeric-method}
+\alias{assay<-,SummarizedExperiment,character-method}
+\alias{assayNames}
+\alias{assayNames,SummarizedExperiment-method}
+\alias{assayNames<-}
+\alias{assayNames<-,SummarizedExperiment,character-method}
+\alias{dim,SummarizedExperiment-method}
+\alias{dimnames,SummarizedExperiment-method}
+\alias{dimnames<-,SummarizedExperiment,list-method}
+\alias{dimnames<-,SummarizedExperiment,NULL-method}
+
+% Subsetting
+\alias{[,SummarizedExperiment-method}
+\alias{[,SummarizedExperiment,ANY-method}
+\alias{[,SummarizedExperiment,ANY,ANY,ANY-method}
+\alias{[<-,SummarizedExperiment,ANY,ANY,SummarizedExperiment-method}
+\alias{extractROWS,SummarizedExperiment,ANY-method}
+\alias{replaceROWS,SummarizedExperiment-method}
+
+% Quick colData access
+\alias{[[,SummarizedExperiment,ANY,missing-method}
+\alias{[[<-,SummarizedExperiment,ANY,missing-method}
+\alias{$,SummarizedExperiment-method}
+\alias{$<-,SummarizedExperiment-method}
+
+% Display
+\alias{show,SummarizedExperiment-method}
+
+% Combine
+\alias{rbind,SummarizedExperiment-method}
+\alias{cbind,SummarizedExperiment-method}
+
+% On-disk realization
+\alias{realize,SummarizedExperiment-method}
+
+\title{SummarizedExperiment objects}
+
+\description{
+
+ The SummarizedExperiment class is a matrix-like container where rows
+ represent features of interest (e.g. genes, transcripts, exons, etc...)
+ and columns represent samples (with sample data summarized as a
+ \link{DataFrame}). A SummarizedExperiment object contains one or more
+ assays, each represented by a matrix-like object of numeric or other mode.
+
+ Note that SummarizedExperiment is the parent of the
+ \link{RangedSummarizedExperiment} class which means that all the methods
+ documented below also work on a \link{RangedSummarizedExperiment} object.
+}
+
+\usage{
+
+## Constructor
+
+# See ?RangedSummarizedExperiment for the constructor function.
+
+## Accessors
+
+assayNames(x, ...)
+assayNames(x, ...) <- value
+assays(x, ..., withDimnames=TRUE)
+assays(x, ..., withDimnames=TRUE) <- value
+assay(x, i, ...)
+assay(x, i, ...) <- value
+rowData(x, ...)
+rowData(x, ...) <- value
+colData(x, ...)
+colData(x, ...) <- value
+#dim(x)
+#dimnames(x)
+#dimnames(x) <- value
+
+## Quick colData access
+
+\S4method{$}{SummarizedExperiment}(x, name)
+\S4method{$}{SummarizedExperiment}(x, name) <- value
+\S4method{[[}{SummarizedExperiment,ANY,missing}(x, i, j, ...)
+\S4method{[[}{SummarizedExperiment,ANY,missing}(x, i, j, ...) <- value
+
+## Subsetting
+
+\S4method{[}{SummarizedExperiment}(x, i, j, ..., drop=TRUE)
+\S4method{[}{SummarizedExperiment,ANY,ANY,SummarizedExperiment}(x, i, j) <- value
+
+## Combining
+
+\S4method{cbind}{SummarizedExperiment}(..., deparse.level=1)
+\S4method{rbind}{SummarizedExperiment}(..., deparse.level=1)
+
+## On-disk realization
+\S4method{realize}{SummarizedExperiment}(x)
+}
+
+\arguments{
+
+ \item{x}{A SummarizedExperiment object.}
+
+ \item{...}{
+ For \code{assay}, \code{...} may contain \code{withDimnames}, which is
+ forwarded to \code{assays}.
+
+ For \code{rowData}, arguments passed thru \code{...} are forwarded to
+ \code{\link[S4Vectors]{mcols}}.
+
+ For \code{cbind}, \code{rbind}, \code{...} contains SummarizedExperiment
+ objects to be combined.
+
+ For other accessors, ignored.
+ }
+
+ \item{i, j}{
+ For \code{assay}, \code{assay<-}, \code{i} is an integer or
+ numeric scalar; see \sQuote{Details} for additional constraints.
+
+ For \code{[,SummarizedExperiment},
+ \code{[,SummarizedExperiment<-}, \code{i}, \code{j} are subscripts
+ that can act to subset the rows and columns of \code{x}, that is the
+ \code{matrix} elements of \code{assays}.
+
+ For \code{[[,SummarizedExperiment},
+ \code{[[<-,SummarizedExperiment}, \code{i} is a scalar index (e.g.,
+ \code{character(1)} or \code{integer(1)}) into a column of
+ \code{colData}.
+ }
+
+ \item{name}{A symbol representing the name of a column of
+ \code{colData}.}
+
+ \item{withDimnames}{A \code{logical(1)}, indicating whether dimnames
+ should be applied to extracted assay elements. Setting
+ \code{withDimnames=FALSE} increases the speed and memory efficiency
+ with which assays are extracted. \code{withDimnames=TRUE} in the
+ getter \code{assays<-} allows efficient complex assignments (e.g.,
+ updating names of assays, \code{names(assays(x, withDimnames=FALSE))
+ = ...} is more efficient than \code{names(assays(x)) = ...}); it
+ does not influence actual assignment of dimnames to assays.}
+
+ \item{drop}{A \code{logical(1)}, ignored by these methods.}
+
+ \item{value}{An object of a class specified in the S4 method
+ signature or as outlined in \sQuote{Details}.}
+
+ \item{deparse.level}{See \code{?base::\link[base]{cbind}} for a description of
+ this argument.}
+
+}
+
+\details{
+
+ The SummarizedExperiment class is meant for numeric and other
+ data types derived from a sequencing experiment. The structure is
+ rectangular like a \code{matrix}, but with additional annotations on
+ the rows and columns, and with the possibility to manage several
+ assays simultaneously.
+
+ The rows of a SummarizedExperiment object represent features
+ of interest. Information about these features is stored in a
+ \link{DataFrame} object, accessible using the function
+ \code{rowData}. The \link{DataFrame} must have as many rows
+ as there are rows in the SummarizedExperiment object, with each row
+ of the \link{DataFrame} providing information on the feature in the
+ corresponding row of the SummarizedExperiment object. Columns of the
+ \link{DataFrame} represent different attributes of the features
+ of interest, e.g., gene or transcript IDs, etc.
+
+ Each column of a SummarizedExperiment object represents a sample.
+ Information about the samples are stored in a \link{DataFrame},
+ accessible using the function \code{colData}, described below.
+ The \link{DataFrame} must have as many rows as there are
+ columns in the SummarizedExperiment object, with each row of the
+ \link{DataFrame} providing information on the sample in the
+ corresponding column of the SummarizedExperiment object.
+ Columns of the \link{DataFrame} represent different sample
+ attributes, e.g., tissue of origin, etc. Columns of the
+ \link{DataFrame} can themselves be annotated (via the
+ \code{\link[S4Vectors]{mcols}} function). Column names typically
+ provide a short identifier unique to each sample.
+
+ A SummarizedExperiment object can also contain information about
+ the overall experiment, for instance the lab in which it was conducted,
+ the publications with which it is associated, etc. This information is
+ stored as a \code{list} object, accessible using the \code{metadata}
+ function. The form of the data associated with the experiment is left to
+ the discretion of the user.
+
+ The SummarizedExperiment container is appropriate for matrix-like
+ data. The data are accessed using the \code{assays} function,
+ described below. This returns a \link{SimpleList} object. Each
+ element of the list must itself be a matrix (of any mode) and must
+ have dimensions that are the same as the dimensions of the
+ SummarizedExperiment in which they are stored. Row and column
+ names of each matrix must either be \code{NULL} or match those of the
+ SummarizedExperiment during construction. It is convenient for
+ the elements of \link{SimpleList} of assays to be named.
+
+}
+
+\section{Constructor}{
+
+ SummarizedExperiment instances are constructed using the
+ \code{SummarizedExperiment} function documented in
+ \code{?\link{RangedSummarizedExperiment}}.
+
+}
+
+\section{Accessors}{
+
+ In the following code snippets, \code{x} is a SummarizedExperiment
+ object.
+
+ \describe{
+
+ \item{\code{assays(x)}, \code{assays(x) <- value}:}{Get or set the
+ assays. \code{value} is a \code{list} or \code{SimpleList}, each
+ element of which is a matrix with the same dimensions as
+ \code{x}.}
+
+ \item{\code{assay(x, i)}, \code{assay(x, i) <- value}:}{A convenient
+ alternative (to \code{assays(x)[[i]]}, \code{assays(x)[[i]] <-
+ value}) to get or set the \code{i}th (default first) assay
+ element. \code{value} must be a matrix of the same dimension as
+ \code{x}, and with dimension names \code{NULL} or consistent with
+ those of \code{x}.}
+
+ \item{\code{assayNames(x)}, \code{assayNames(x) <- value}:}{Get or
+ set the names of \code{assay()} elements.}
+
+ \item{\code{rowData(x)}, \code{rowData(x) <- value}:}{Get or set the
+ row data. \code{value} is a \link{DataFrame} object. Row
+ names of \code{value} must be NULL or consistent with the existing
+ row names of \code{x}.}
+
+ \item{\code{colData(x)}, \code{colData(x) <- value}:}{Get or set the
+ column data. \code{value} is a \link{DataFrame} object. Row
+ names of \code{value} must be NULL or consistent with the existing
+ column names of \code{x}.}
+
+ \item{\code{metadata(x)}, \code{metadata(x) <- value}:}{Get or set
+ the experiment data. \code{value} is a \code{list} with arbitrary
+ content.}
+
+ \item{\code{dim(x)}:}{Get the dimensions (features of interest x samples)
+ of the SummarizedExperiment.}
+
+ \item{\code{dimnames(x)}, \code{dimnames(x) <- value}:}{Get or set
+ the dimension names. \code{value} is usually a list of length 2,
+ containing elements that are either \code{NULL} or vectors of
+ appropriate length for the corresponding dimension. \code{value}
+ can be \code{NULL}, which removes dimension names. This method
+ implies that \code{rownames}, \code{rownames<-}, \code{colnames},
+ and \code{colnames<-} are all available.}
+
+ }
+}
+
+\section{Subsetting}{
+
+ In the code snippets below, \code{x} is a SummarizedExperiment object.
+
+ \describe{
+
+ \item{\code{x[i,j]}, \code{x[i,j] <- value}:}{Create or replace a
+ subset of \code{x}. \code{i}, \code{j} can be \code{numeric},
+ \code{logical}, \code{character}, or \code{missing}. \code{value}
+ must be a SummarizedExperiment object with dimensions,
+ dimension names, and assay elements consistent with the subset
+ \code{x[i,j]} being replaced.}
+
+ }
+
+ Additional subsetting accessors provide convenient access to
+ \code{colData} columns
+
+ \describe{
+
+ \item{\code{x$name}, \code{x$name <- value}}{Access or replace
+ column \code{name} in \code{x}.}
+
+ \item{\code{x[[i, ...]]}, \code{x[[i, ...]] <- value}}{Access or
+ replace column \code{i} in \code{x}.}
+
+ }
+
+}
+
+\section{Combining}{
+
+ In the code snippets below, \code{...} are SummarizedExperiment objects
+ to be combined.
+
+ \describe{
+
+ \item{\code{cbind(...)}:}{
+ \code{cbind} combines objects with the same features of interest
+ but different samples (columns in \code{assays}).
+ The colnames in \code{colData(SummarizedExperiment)} must match or
+ an error is thrown.
+ Duplicate columns of \code{rowData(SummarizedExperiment)} must
+ contain the same data.
+
+ Data in \code{assays} are combined by name matching; if all assay
+ names are NULL matching is by position. A mixture of names and NULL
+ throws an error.
+
+ \code{metadata} from all objects are combined into a \code{list}
+ with no name checking.
+ }
+
+ \item{\code{rbind(...)}:}{
+ \code{rbind} combines objects with the same samples
+ but different features of interest (rows in \code{assays}).
+ The colnames in \code{rowData(SummarizedExperiment)} must match or
+ an error is thrown.
+ Duplicate columns of \code{colData(SummarizedExperiment)} must
+ contain the same data.
+
+ Data in \code{assays} are combined by name matching; if all assay
+ names are NULL matching is by position. A mixture of names and NULL
+ throws an error.
+
+ \code{metadata} from all objects are combined into a \code{list}
+ with no name checking.
+ }
+
+ }
+
+}
+
+\section{Implementation and Extension}{
+
+ This section contains advanced material meant for package developers.
+
+ SummarizedExperiment is implemented as an S4 class, and can be extended in
+ the usual way, using \code{contains="SummarizedExperiment"} in the new
+ class definition.
+
+ In addition, the representation of the \code{assays} slot of
+ SummarizedExperiment is as a virtual class Assays. This
+ allows derived classes (\code{contains="Assays"}) to easily implement
+ alternative requirements for the assays, e.g., backed by file-based
+ storage like NetCDF or the \code{ff} package, while re-using the existing
+ SummarizedExperiment class without modification.
+ See \link{Assays} for more information.
+
+ The current \code{assays} slot is implemented as a reference class
+ that has copy-on-change semantics. This means that modifying non-assay
+ slots does not copy the (large) assay data, and at the same time the
+ user is not surprised by reference-based semantics. Updates to
+ non-assay slots are very fast; updating the assays slot itself can be
+ 5x or more faster than with an S4 instance in the slot. One useful
+ technique when working with \code{assay} or \code{assays} function is
+ use of the \code{withDimnames=FALSE} argument, which benefits speed
+ and memory use by not copying dimnames from the row- and colData
+ elements to each assay.
+}
+
+\author{Martin Morgan, \url{mtmorgan at fhcrc.org}}
+
+\seealso{
+ \itemize{
+ \item \link{RangedSummarizedExperiment} objects.
+
+ \item \link[S4Vectors]{DataFrame}, \link[S4Vectors]{SimpleList}, and
+ \link[S4Vectors]{Annotated} objects in the \pkg{S4Vectors} package.
+
+ \item The \code{\link[S4Vectors]{metadata}} and
+ \code{\link[S4Vectors]{mcols}} accessors in the \pkg{S4Vectors}
+ package.
+
+ \item \code{\link{saveHDF5SummarizedExperiment}} and
+ \code{\link{loadHDF5SummarizedExperiment}} for saving/loading a
+ HDF5-based SummarizedExperiment object to/from disk.
+
+ \item The \code{\link[DelayedArray]{realize}} generic function in the
+ \pkg{DelayedArray} package for more information about on-disk
+ realization of objects carrying delayed operations.
+ }
+}
+
+\examples{
+nrows <- 200; ncols <- 6
+counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
+colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
+ row.names=LETTERS[1:6])
+se0 <- SummarizedExperiment(assays=SimpleList(counts=counts),
+ colData=colData)
+se0
+dim(se0)
+dimnames(se0)
+assayNames(se0)
+head(assay(se0))
+assays(se0) <- endoapply(assays(se0), asinh)
+head(assay(se0))
+
+rowData(se0)
+colData(se0)
+
+se0[, se0$Treatment == "ChIP"]
+
+## cbind() combines objects with the same features of interest
+## but different samples:
+se1 <- se0
+se2 <- se1[,1:3]
+colnames(se2) <- letters[1:ncol(se2)]
+cmb1 <- cbind(se1, se2)
+dim(cmb1)
+dimnames(cmb1)
+
+## rbind() combines objects with the same samples but different
+## features of interest:
+se1 <- se0
+se2 <- se1[1:50,]
+rownames(se2) <- letters[1:nrow(se2)]
+cmb2 <- rbind(se1, se2)
+dim(cmb2)
+dimnames(cmb2)
+
+## ---------------------------------------------------------------------
+## ON-DISK REALIZATION
+## ---------------------------------------------------------------------
+setRealizationBackend("HDF5Array")
+cmb3 <- realize(cmb2)
+assay(cmb3, withDimnames=FALSE) # an HDF5Matrix object
+}
diff --git a/man/coverage-methods.Rd b/man/coverage-methods.Rd
new file mode 100644
index 0000000..186da4f
--- /dev/null
+++ b/man/coverage-methods.Rd
@@ -0,0 +1,80 @@
+\name{coverage-methods}
+
+\alias{coverage-methods}
+
+\alias{coverage}
+\alias{coverage,RangedSummarizedExperiment-method}
+
+
+\title{Coverage of a RangedSummarizedExperiment object}
+
+\description{
+ This man page documents the \code{coverage} method for
+ \link{RangedSummarizedExperiment} objects.
+}
+
+\usage{
+\S4method{coverage}{RangedSummarizedExperiment}(x, shift=0L, width=NULL, weight=1L,
+ method=c("auto", "sort", "hash"))
+}
+
+\arguments{
+ \item{x}{
+ A \link{RangedSummarizedExperiment} object.
+ }
+ \item{shift, width, weight, method}{
+ See \code{?\link[GenomicRanges]{coverage}} in the \pkg{GenomicRanges}
+ package.
+ }
+}
+
+\details{
+ This method operates on the \code{rowRanges} component of the
+ \link{RangedSummarizedExperiment} object, which can be a
+ \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList}
+ object.
+
+ More precisely, on \link{RangedSummarizedExperiment} object \code{x},
+ \code{coverage(x, ...)} is equivalent to \code{coverage(rowRanges(x), ...)}.
+
+ See \code{?\link[GenomicRanges]{coverage}} in the \pkg{GenomicRanges}
+ package for the details of how \code{coverage} operates on a
+ \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList}
+ object.
+}
+
+\value{
+ See \code{?\link[GenomicRanges]{coverage}} in the \pkg{GenomicRanges}
+ package.
+}
+
+\seealso{
+ \itemize{
+ \item \link{RangedSummarizedExperiment} objects.
+
+ \item The \link[GenomicRanges]{coverage} man page in the
+ \pkg{GenomicRanges} package where the \code{coverage} methods
+ for \link[GenomicRanges]{GenomicRanges} and
+ \link[GenomicRanges]{GRangesList} objects are documented.
+ }
+}
+
+\examples{
+nrows <- 20; ncols <- 6
+counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
+rowRanges <- GRanges(rep(c("chr1", "chr2"), c(5, 15)),
+ IRanges(sample(1000L, 20), width=100),
+ strand=Rle(c("+", "-"), c(12, 8)),
+ seqlengths=c(chr1=1800, chr2=1300))
+colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
+ row.names=LETTERS[1:6])
+rse <- SummarizedExperiment(assays=SimpleList(counts=counts),
+ rowRanges=rowRanges, colData=colData)
+
+cvg <- coverage(rse)
+cvg
+stopifnot(identical(cvg, coverage(rowRanges(rse))))
+}
+
+\keyword{methods}
+\keyword{utilities}
diff --git a/man/findOverlaps-methods.Rd b/man/findOverlaps-methods.Rd
new file mode 100644
index 0000000..ef0fe83
--- /dev/null
+++ b/man/findOverlaps-methods.Rd
@@ -0,0 +1,105 @@
+\name{findOverlaps-methods}
+
+\alias{findOverlaps-methods}
+
+\alias{findOverlaps}
+\alias{findOverlaps,RangedSummarizedExperiment,Vector-method}
+\alias{findOverlaps,Vector,RangedSummarizedExperiment-method}
+\alias{findOverlaps,RangedSummarizedExperiment,RangedSummarizedExperiment-method}
+
+
+\title{Finding overlapping ranges in RangedSummarizedExperiment objects}
+
+\description{
+ This man page documents the \code{findOverlaps} methods for
+ \link{RangedSummarizedExperiment} objects.
+
+ \link{RangedSummarizedExperiment} objects also support
+ \code{countOverlaps}, \code{overlapsAny}, and \code{subsetByOverlaps}
+ thanks to the default methods defined in the \pkg{IRanges} package and
+ to the \code{findOverlaps} methods defined in this package and documented
+ below.
+}
+
+\usage{
+\S4method{findOverlaps}{RangedSummarizedExperiment,Vector}(query, subject,
+ maxgap=0L, minoverlap=1L,
+ type=c("any", "start", "end", "within", "equal"),
+ select=c("all", "first", "last", "arbitrary"),
+ ignore.strand=FALSE)
+\S4method{findOverlaps}{Vector,RangedSummarizedExperiment}(query, subject,
+ maxgap=0L, minoverlap=1L,
+ type=c("any", "start", "end", "within", "equal"),
+ select=c("all", "first", "last", "arbitrary"),
+ ignore.strand=FALSE)
+}
+
+\arguments{
+ \item{query, subject}{
+ One of these two arguments must be a \link{RangedSummarizedExperiment}
+ object.
+ }
+ \item{maxgap, minoverlap, type}{
+ See \code{?\link[GenomicRanges]{findOverlaps}} in the \pkg{GenomicRanges}
+ package.
+ }
+ \item{select, ignore.strand}{
+ See \code{?\link[GenomicRanges]{findOverlaps}} in the \pkg{GenomicRanges}
+ package.
+ }
+}
+
+\details{
+ These methods operate on the \code{rowRanges} component of the
+ \link{RangedSummarizedExperiment} object, which can be a
+ \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList}
+ object.
+
+ More precisely, if any of the above functions is passed a
+ \link{RangedSummarizedExperiment} object thru the \code{query} and/or
+ \code{subject} argument, then it behaves as if \code{rowRanges(query)}
+ and/or \code{rowRanges(subject)} had been passed instead.
+
+ See \code{?\link[GenomicRanges]{findOverlaps}} in the \pkg{GenomicRanges}
+ package for the details of how \code{findOverlaps} and family operate on
+ \link[GenomicRanges]{GenomicRanges} and \link[GenomicRanges]{GRangesList}
+ objects.
+}
+
+\value{
+ See \code{?\link[GenomicRanges]{findOverlaps}} in the \pkg{GenomicRanges}
+ package.
+}
+
+\seealso{
+ \itemize{
+ \item \link{RangedSummarizedExperiment} objects.
+
+ \item The \link[GenomicRanges]{findOverlaps} man page in the
+ \pkg{GenomicRanges} package where the \code{findOverlaps} family
+ of methods for \link[GenomicRanges]{GenomicRanges} and
+ \link[GenomicRanges]{GRangesList} objects is documented.
+ }
+}
+
+\examples{
+nrows <- 20; ncols <- 6
+counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
+rowRanges <- GRanges(rep(c("chr1", "chr2"), c(5, 15)),
+ IRanges(sample(1000L, 20), width=100),
+ strand=Rle(c("+", "-"), c(12, 8)))
+colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
+ row.names=LETTERS[1:6])
+rse0 <- SummarizedExperiment(assays=SimpleList(counts=counts),
+ rowRanges=rowRanges, colData=colData)
+rse1 <- shift(rse0, 100)
+
+hits <- findOverlaps(rse0, rse1)
+hits
+stopifnot(identical(hits, findOverlaps(rowRanges(rse0), rowRanges(rse1))))
+stopifnot(identical(hits, findOverlaps(rse0, rowRanges(rse1))))
+stopifnot(identical(hits, findOverlaps(rowRanges(rse0), rse1)))
+}
+
+\keyword{methods}
+\keyword{utilities}
diff --git a/man/inter-range-methods.Rd b/man/inter-range-methods.Rd
new file mode 100644
index 0000000..e2cf611
--- /dev/null
+++ b/man/inter-range-methods.Rd
@@ -0,0 +1,95 @@
+\name{inter-range-methods}
+
+\alias{inter-range-methods}
+
+\alias{isDisjoint}
+\alias{isDisjoint,RangedSummarizedExperiment-method}
+
+\alias{disjointBins}
+\alias{disjointBins,RangedSummarizedExperiment-method}
+
+
+\title{Inter range transformations of a RangedSummarizedExperiment object}
+
+\description{
+ This man page documents the \emph{inter range transformations} that are
+ supported on \link{RangedSummarizedExperiment} objects.
+}
+
+\usage{
+\S4method{isDisjoint}{RangedSummarizedExperiment}(x, ignore.strand=FALSE)
+
+\S4method{disjointBins}{RangedSummarizedExperiment}(x, ignore.strand=FALSE)
+}
+
+\arguments{
+ \item{x}{
+ A \link{RangedSummarizedExperiment} object.
+ }
+ \item{ignore.strand}{
+ See \code{?\link[GenomicRanges]{isDisjoint}} in the
+ \pkg{GenomicRanges} package.
+ }
+}
+
+\details{
+ These transformations operate on the \code{rowRanges} component of the
+ \link{RangedSummarizedExperiment} object, which can be a
+ \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList}
+ object.
+
+ More precisely, any of the above functions performs the following
+ transformation on \link{RangedSummarizedExperiment} object \code{x}:
+\preformatted{ f(rowRanges(x), ...)
+}
+ where \code{f} is the name of the function and \code{...} any additional
+ arguments passed to it.
+
+ See \code{?\link[GenomicRanges]{isDisjoint}} in the \pkg{GenomicRanges}
+ package for the details of how these transformations operate on a
+ \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList}
+ object.
+}
+
+\value{
+ See \code{?\link[GenomicRanges]{isDisjoint}} in the
+ \pkg{GenomicRanges} package.
+}
+
+\seealso{
+ \itemize{
+ \item \link{RangedSummarizedExperiment} objects.
+
+ \item The \link[GenomicRanges]{isDisjoint} man page in the
+ \pkg{GenomicRanges} package where \emph{inter range transformations}
+ of a \link[GenomicRanges]{GenomicRanges} or
+ \link[GenomicRanges]{GRangesList} object are documented.
+ }
+}
+
+\examples{
+nrows <- 20; ncols <- 6
+counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
+rowRanges <- GRanges(rep(c("chr1", "chr2"), c(5, 15)),
+ IRanges(sample(1000L, 20), width=100),
+ strand=Rle(c("+", "-"), c(12, 8)))
+colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
+ row.names=LETTERS[1:6])
+rse0 <- SummarizedExperiment(assays=SimpleList(counts=counts),
+ rowRanges=rowRanges, colData=colData)
+rse1 <- shift(rse0, 99*start(rse0))
+
+isDisjoint(rse0) # FALSE
+isDisjoint(rse1) # TRUE
+
+bins0 <- disjointBins(rse0)
+bins0
+stopifnot(identical(bins0, disjointBins(rowRanges(rse0))))
+
+bins1 <- disjointBins(rse1)
+bins1
+stopifnot(all(bins1 == bins1[1]))
+}
+
+\keyword{methods}
+\keyword{utilities}
diff --git a/man/intra-range-methods.Rd b/man/intra-range-methods.Rd
new file mode 100644
index 0000000..5dc7956
--- /dev/null
+++ b/man/intra-range-methods.Rd
@@ -0,0 +1,151 @@
+\name{intra-range-methods}
+
+\alias{intra-range-methods}
+
+\alias{shift}
+\alias{shift,RangedSummarizedExperiment-method}
+
+\alias{narrow}
+\alias{narrow,RangedSummarizedExperiment-method}
+
+\alias{resize}
+\alias{resize,RangedSummarizedExperiment-method}
+
+\alias{flank}
+\alias{flank,RangedSummarizedExperiment-method}
+
+\alias{promoters}
+\alias{promoters,RangedSummarizedExperiment-method}
+
+\alias{restrict}
+\alias{restrict,RangedSummarizedExperiment-method}
+
+\alias{trim,RangedSummarizedExperiment-method}
+
+
+\title{Intra range transformations of a RangedSummarizedExperiment object}
+
+\description{
+ This man page documents the \emph{intra range transformations} that are
+ supported on \link{RangedSummarizedExperiment} objects.
+}
+
+\usage{
+\S4method{shift}{RangedSummarizedExperiment}(x, shift=0L, use.names=TRUE)
+
+\S4method{narrow}{RangedSummarizedExperiment}(x, start=NA, end=NA, width=NA, use.names=TRUE)
+
+\S4method{resize}{RangedSummarizedExperiment}(x, width, fix="start", use.names=TRUE,
+ ignore.strand=FALSE)
+
+\S4method{flank}{RangedSummarizedExperiment}(x, width, start=TRUE, both=FALSE,
+ use.names=TRUE, ignore.strand=FALSE)
+
+\S4method{promoters}{RangedSummarizedExperiment}(x, upstream=2000, downstream=200)
+
+\S4method{restrict}{RangedSummarizedExperiment}(x, start=NA, end=NA, keep.all.ranges=FALSE,
+ use.names=TRUE)
+
+\S4method{trim}{RangedSummarizedExperiment}(x, use.names=TRUE)
+}
+
+\arguments{
+ \item{x}{
+ A \link{RangedSummarizedExperiment} object.
+ }
+ \item{shift, use.names}{
+ See \code{?\link[GenomicRanges]{shift}} in the \pkg{GenomicRanges}
+ package.
+ }
+ \item{start, end, width, fix}{
+ See \code{?\link[GenomicRanges]{shift}} in the \pkg{GenomicRanges}
+ package.
+ }
+ \item{ignore.strand, both}{
+ See \code{?\link[GenomicRanges]{shift}} in the \pkg{GenomicRanges}
+ package.
+ }
+ \item{upstream, downstream}{
+ See \code{?\link[GenomicRanges]{shift}} in the \pkg{GenomicRanges}
+ package.
+ }
+ \item{keep.all.ranges}{
+ See \code{?\link[GenomicRanges]{shift}} in the \pkg{GenomicRanges}
+ package.
+ }
+}
+
+\details{
+ These transformations operate on the \code{rowRanges} component of the
+ \link{RangedSummarizedExperiment} object, which can be a
+ \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList}
+ object.
+
+ More precisely, any of the above functions performs the following
+ transformation on \link{RangedSummarizedExperiment} object \code{x}:
+\preformatted{ rowRanges(x) <- f(rowRanges(x), ...)
+}
+ where \code{f} is the name of the function and \code{...} any additional
+ arguments passed to it.
+
+ See \code{?\link[GenomicRanges]{shift}} in the \pkg{GenomicRanges}
+ package for the details of how these transformations operate on a
+ \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList}
+ object.
+}
+
+\seealso{
+ \itemize{
+ \item \link{RangedSummarizedExperiment} objects.
+
+ \item The \link[GenomicRanges]{shift} man page in the
+ \pkg{GenomicRanges} package where \emph{intra range transformations}
+ of a \link[GenomicRanges]{GenomicRanges} or
+ \link[GenomicRanges]{GRangesList} object are documented.
+ }
+}
+
+\examples{
+nrows <- 20; ncols <- 6
+counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
+rowRanges <- GRanges(rep(c("chr1", "chr2"), c(5, 15)),
+ IRanges(sample(1000L, 20), width=100),
+ strand=Rle(c("+", "-"), c(12, 8)))
+colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
+ row.names=LETTERS[1:6])
+rse0 <- SummarizedExperiment(assays=SimpleList(counts=counts),
+ rowRanges=rowRanges, colData=colData)
+
+rse1 <- shift(rse0, 1)
+stopifnot(identical(
+ rowRanges(rse1),
+ shift(rowRanges(rse0), 1)
+))
+
+se2 <- narrow(rse0, start=10, end=-15)
+stopifnot(identical(
+ rowRanges(se2),
+ narrow(rowRanges(rse0), start=10, end=-15)
+))
+
+se3 <- resize(rse0, width=75)
+stopifnot(identical(
+ rowRanges(se3),
+ resize(rowRanges(rse0), width=75)
+))
+
+se4 <- flank(rse0, width=20)
+stopifnot(identical(
+ rowRanges(se4),
+ flank(rowRanges(rse0), width=20)
+))
+
+se5 <- restrict(rse0, start=200, end=700, keep.all.ranges=TRUE)
+stopifnot(identical(
+ rowRanges(se5),
+ restrict(rowRanges(rse0), start=200, end=700, keep.all.ranges=TRUE)
+))
+}
+
+\keyword{methods}
+\keyword{utilities}
diff --git a/man/makeSummarizedExperimentFromDataFrame.Rd b/man/makeSummarizedExperimentFromDataFrame.Rd
new file mode 100644
index 0000000..b3087b8
--- /dev/null
+++ b/man/makeSummarizedExperimentFromDataFrame.Rd
@@ -0,0 +1,87 @@
+\name{makeSummarizedExperimentFromDataFrame}
+
+\alias{makeSummarizedExperimentFromDataFrame}
+
+\title{Make a RangedSummarizedExperiment from a data.frame or DataFrame}
+
+\description{
+ \code{makeSummarizedExperimentFromDataFrame} uses \code{data.frame}
+ or \code{DataFrame} column names to create a \link{GRanges} object for the
+ \code{rowRanges} of the resulting \link{SummarizedExperiment} object.
+ It requires that non-range data columns be coercible into a \code{numeric}
+ \code{matrix} for the \link{SummarizedExperiment} constructor. All columns
+ that are not part of the row ranges attribute are assumed to be experiment
+ data; thus, keeping metadata columns will not be supported. Note that this
+ function only returns \link{SummarizedExperiment} objects with a single
+ assay.
+
+ If metadata columns are to be kept, one can first construct the row ranges
+ attribute by using the \link{makeGRangesFromDataFrame} function and
+ subsequently creating the \link{SummarizedExperiment}.
+}
+
+\usage{
+makeSummarizedExperimentFromDataFrame(df,
+ ...,
+ seqinfo = NULL,
+ starts.in.df.are.0based = FALSE)
+}
+\arguments{
+ \item{df}{
+ A data.frame or \link[S4Vectors]{DataFrame} object. If not, then
+ the function first tries to turn \code{df} into a data frame with
+ \code{as.data.frame(df)}.
+ }
+ \item{...}{
+ Additional arguments passed on to \link{makeGRangesFromDataFrame}
+ }
+ \item{seqinfo}{
+ Either \code{NULL}, or a \link{Seqinfo} object, or a character vector
+ of seqlevels, or a named numeric vector of sequence lengths.
+ When not \code{NULL}, it must be compatible with the genomic ranges
+ in \code{df} i.e. it must include at least the sequence levels
+ represented in \code{df}.
+ }
+ \item{starts.in.df.are.0based}{
+ \code{TRUE} or \code{FALSE} (the default).
+ If \code{TRUE}, then the start positions of the genomic ranges in
+ \code{df} are considered to be \emph{0-based} and are converted to
+ \emph{1-based} in the returned \link{GRanges} object.
+ This feature is intended to make it more convenient to handle input
+ that contains data obtained from resources using the "0-based
+ start" convention. A notorious example of such resource is the UCSC
+ Table Browser (\url{http://genome.ucsc.edu/cgi-bin/hgTables}).
+ }
+}
+\value{
+A \link{RangedSummarizedExperiment} object with rowRanges and a single assay
+}
+\author{
+ M. Ramos
+}
+\seealso{
+ \itemize{
+ \item \link{makeGRangesFromDataFrame}
+ }
+}
+
+\examples{
+## ---------------------------------------------------------------------
+## BASIC EXAMPLES
+## ---------------------------------------------------------------------
+
+# Note that rownames of the data.frame are also rownames of the result
+df <- data.frame(chr="chr2", start = 11:15, end = 12:16,
+ strand = c("+", "-", "+", "*", "."), expr0 = 3:7,
+ expr1 = 8:12, expr2 = 12:16,
+ row.names = paste0("GENE", letters[5:1]))
+df
+
+exRSE <- makeSummarizedExperimentFromDataFrame(df)
+
+exRSE
+
+assay(exRSE)
+
+rowRanges(exRSE)
+}
diff --git a/man/makeSummarizedExperimentFromExpressionSet.Rd b/man/makeSummarizedExperimentFromExpressionSet.Rd
new file mode 100644
index 0000000..28d175e
--- /dev/null
+++ b/man/makeSummarizedExperimentFromExpressionSet.Rd
@@ -0,0 +1,112 @@
+\name{makeSummarizedExperimentFromExpressionSet}
+
+\alias{makeSummarizedExperimentFromExpressionSet}
+\alias{naiveRangeMapper}
+\alias{probeRangeMapper}
+\alias{geneRangeMapper}
+\alias{coerce,ExpressionSet,RangedSummarizedExperiment-method}
+\alias{coerce,RangedSummarizedExperiment,ExpressionSet-method}
+\alias{coerce,SummarizedExperiment,ExpressionSet-method}
+
+
+\title{Make a RangedSummarizedExperiment object from an ExpressionSet and
+ vice-versa}
+
+\description{
+ Coercion between \link{RangedSummarizedExperiment} and
+ \link[Biobase]{ExpressionSet} is supported in both directions.
+
+ For going from \link[Biobase]{ExpressionSet} to
+ \link{RangedSummarizedExperiment}, the
+ \code{makeSummarizedExperimentFromExpressionSet} function is also
+ provided to let the user control how to map features to ranges.
+}
+
+\usage{
+makeSummarizedExperimentFromExpressionSet(from,
+ mapFun=naiveRangeMapper,
+ ...)
+
+## range mapping functions
+naiveRangeMapper(from)
+probeRangeMapper(from)
+geneRangeMapper(txDbPackage, key = "ENTREZID")
+}
+
+\arguments{
+ \item{from}{
+ An \link[Biobase]{ExpressionSet} object.
+ }
+ \item{mapFun}{
+ A function which takes an \link[Biobase]{ExpressionSet} object and
+ returns a \link{GRanges}, or \link{GRangesList} object which
+ corresponds to the genomic ranges used in the ExpressionSet. The
+ \link[base]{rownames} of the returned \link[GenomicRanges]{GRanges}
+ are used to match the \link[Biobase]{featureNames} of the
+ \link[Biobase]{ExpressionSet}.
+
+ The \code{naiveRangeMapper} function is used by default.
+ }
+ \item{...}{
+ Additional arguments passed to \code{mapFun}.
+ }
+ \item{txDbPackage}{
+ A character string with the Transcript Database to use for the mapping.
+ }
+ \item{key}{
+ A character string with the Gene key to use for the mapping.
+ }
+}
+
+\value{
+ \code{makeSummarizedExperimentFromExpressionSet} takes an
+ \link[Biobase]{ExpressionSet} object as input and a \emph{range mapping
+ function} that maps the features to ranges. It then returns a
+ \link{RangedSummarizedExperiment} object that corresponds to the input.
+
+ The range mapping functions return a \link{GRanges} object, with the
+ \code{rownames} corresponding to the \link[Biobase]{featureNames} of
+ the \link[Biobase]{ExpressionSet} object.
+}
+
+\author{Jim Hester, \url{james.f.hester at gmail.com}}
+
+\seealso{
+ \itemize{
+ \item \link{RangedSummarizedExperiment} objects.
+
+ \item \link[Biobase]{ExpressionSet} objects in the \pkg{Biobase} package.
+
+ \item \link[GenomicFeatures]{TxDb} objects in the \pkg{GenomicFeatures}
+ package.
+ }
+}
+
+\examples{
+## ---------------------------------------------------------------------
+## GOING FROM ExpressionSet TO RangedSummarizedExperiment
+## ---------------------------------------------------------------------
+
+data(sample.ExpressionSet, package="Biobase")
+
+# 2 equivalent ways of doing the naive coercion
+makeSummarizedExperimentFromExpressionSet(sample.ExpressionSet)
+as(sample.ExpressionSet, "RangedSummarizedExperiment")
+
+# using probe range mapper
+makeSummarizedExperimentFromExpressionSet(sample.ExpressionSet, probeRangeMapper)
+
+# using the gene range mapper
+makeSummarizedExperimentFromExpressionSet(sample.ExpressionSet,
+ geneRangeMapper("TxDb.Hsapiens.UCSC.hg19.knownGene"))
+
+## ---------------------------------------------------------------------
+## GOING FROM RangedSummarizedExperiment TO ExpressionSet
+## ---------------------------------------------------------------------
+
+example(RangedSummarizedExperiment) # to create 'rse'
+rse
+as(rse, "ExpressionSet")
+}
+
+\keyword{manip}
diff --git a/man/nearest-methods.Rd b/man/nearest-methods.Rd
new file mode 100644
index 0000000..ce4ba46
--- /dev/null
+++ b/man/nearest-methods.Rd
@@ -0,0 +1,149 @@
+\name{nearest-methods}
+
+\alias{nearest-methods}
+
+\alias{precede}
+\alias{precede,RangedSummarizedExperiment,ANY-method}
+\alias{precede,ANY,RangedSummarizedExperiment-method}
+\alias{precede,RangedSummarizedExperiment,RangedSummarizedExperiment-method}
+
+\alias{follow}
+\alias{follow,RangedSummarizedExperiment,ANY-method}
+\alias{follow,ANY,RangedSummarizedExperiment-method}
+\alias{follow,RangedSummarizedExperiment,RangedSummarizedExperiment-method}
+
+\alias{nearest}
+\alias{nearest,RangedSummarizedExperiment,ANY-method}
+\alias{nearest,ANY,RangedSummarizedExperiment-method}
+\alias{nearest,RangedSummarizedExperiment,RangedSummarizedExperiment-method}
+
+\alias{distance}
+\alias{distance,RangedSummarizedExperiment,ANY-method}
+\alias{distance,ANY,RangedSummarizedExperiment-method}
+\alias{distance,RangedSummarizedExperiment,RangedSummarizedExperiment-method}
+
+\alias{distanceToNearest}
+\alias{distanceToNearest,RangedSummarizedExperiment,ANY-method}
+\alias{distanceToNearest,ANY,RangedSummarizedExperiment-method}
+\alias{distanceToNearest,RangedSummarizedExperiment,RangedSummarizedExperiment-method}
+
+
+\title{Finding the nearest range neighbor in RangedSummarizedExperiment objects}
+
+\description{
+ This man page documents the \code{nearest} methods and family (i.e.
+ \code{precede}, \code{follow}, \code{distance}, and \code{distanceToNearest}
+ methods) for \link{RangedSummarizedExperiment} objects.
+}
+
+\usage{
+\S4method{precede}{RangedSummarizedExperiment,ANY}(x, subject, select=c("arbitrary", "all"),
+ ignore.strand=FALSE)
+\S4method{precede}{ANY,RangedSummarizedExperiment}(x, subject, select=c("arbitrary", "all"),
+ ignore.strand=FALSE)
+
+\S4method{follow}{RangedSummarizedExperiment,ANY}(x, subject, select=c("arbitrary", "all"),
+ ignore.strand=FALSE)
+\S4method{follow}{ANY,RangedSummarizedExperiment}(x, subject, select=c("arbitrary", "all"),
+ ignore.strand=FALSE)
+
+\S4method{nearest}{RangedSummarizedExperiment,ANY}(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE)
+\S4method{nearest}{ANY,RangedSummarizedExperiment}(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE)
+
+\S4method{distance}{RangedSummarizedExperiment,ANY}(x, y, ignore.strand=FALSE, ...)
+\S4method{distance}{ANY,RangedSummarizedExperiment}(x, y, ignore.strand=FALSE, ...)
+
+\S4method{distanceToNearest}{RangedSummarizedExperiment,ANY}(x, subject, ignore.strand=FALSE, ...)
+\S4method{distanceToNearest}{ANY,RangedSummarizedExperiment}(x, subject, ignore.strand=FALSE, ...)
+}
+
+\arguments{
+ \item{x, subject}{
+ One of these two arguments must be a \link{RangedSummarizedExperiment}
+ object.
+ }
+ \item{select, ignore.strand}{
+ See \code{?\link[GenomicRanges]{nearest}} in the \pkg{GenomicRanges}
+ package.
+ }
+ \item{y}{
+ For the \code{distance} methods, one of \code{x} or \code{y} must be a
+ \link{RangedSummarizedExperiment} object.
+ }
+ \item{...}{Additional arguments for methods.}
+}
+
+\details{
+ These methods operate on the \code{rowRanges} component of the
+ \link{RangedSummarizedExperiment} object, which can be a
+ \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList}
+ object.
+
+ More precisely, if any of the above functions is passed a
+ \link{RangedSummarizedExperiment} object thru the \code{x}, \code{subject},
+ and/or \code{y} argument, then it behaves as if \code{rowRanges(x)},
+ \code{rowRanges(subject)}, and/or \code{rowRanges(y)} had been passed
+ instead.
+
+ See \code{?\link[GenomicRanges]{nearest}} in the \pkg{GenomicRanges}
+ package for the details of how \code{nearest} and family operate on
+ \link[GenomicRanges]{GenomicRanges} and \link[GenomicRanges]{GRangesList}
+ objects.
+}
+
+\value{
+ See \code{?\link[GenomicRanges]{nearest}} in the \pkg{GenomicRanges}
+ package.
+}
+
+\seealso{
+ \itemize{
+ \item \link{RangedSummarizedExperiment} objects.
+
+ \item The \link[GenomicRanges]{nearest} man page in the
+ \pkg{GenomicRanges} package where the \code{nearest} family
+ of methods for \link[GenomicRanges]{GenomicRanges} and
+ \link[GenomicRanges]{GRangesList} objects is documented.
+ }
+}
+
+\examples{
+nrows <- 20; ncols <- 6
+counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
+rowRanges <- GRanges(rep(c("chr1", "chr2"), c(5, 15)),
+ IRanges(sample(1000L, 20), width=100),
+ strand=Rle(c("+", "-"), c(12, 8)))
+colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
+ row.names=LETTERS[1:6])
+rse0 <- SummarizedExperiment(assays=SimpleList(counts=counts),
+ rowRanges=rowRanges, colData=colData)
+rse1 <- shift(rse0, 100)
+
+res <- nearest(rse0, rse1)
+res
+stopifnot(identical(res, nearest(rowRanges(rse0), rowRanges(rse1))))
+stopifnot(identical(res, nearest(rse0, rowRanges(rse1))))
+stopifnot(identical(res, nearest(rowRanges(rse0), rse1)))
+
+res <- nearest(rse0) # missing subject
+res
+stopifnot(identical(res, nearest(rowRanges(rse0))))
+
+hits <- nearest(rse0, rse1, select="all")
+hits
+stopifnot(identical(
+ hits,
+ nearest(rowRanges(rse0), rowRanges(rse1), select="all")
+))
+stopifnot(identical(
+ hits,
+ nearest(rse0, rowRanges(rse1), select="all")
+))
+stopifnot(identical(
+ hits,
+ nearest(rowRanges(rse0), rse1, select="all")
+))
+}
+
+\keyword{methods}
+\keyword{utilities}
diff --git a/man/readKallisto.Rd b/man/readKallisto.Rd
new file mode 100644
index 0000000..c2627f4
--- /dev/null
+++ b/man/readKallisto.Rd
@@ -0,0 +1,120 @@
+\name{readKallisto}
+\alias{readKallisto}
+\alias{readKallistoBootstrap}
+\alias{KALLISTO_ASSAYS}
+
+\title{
+ Input kallisto or kallisto bootstrap results.
+}
+
+\description{
+ \code{readKallisto} inputs several kallisto output files into a single
+ \code{SummarizedExperiment} instance, with rows corresponding to
+ estimated transcript abundance and columns to
+ samples. \code{readKallistoBootstrap} inputs kallisto bootstrap
+ replicates of a single sample into a matrix of transcript x bootstrap
+ abundance estimates.
+}
+
+\usage{
+readKallisto(files,
+ json = file.path(dirname(files), "run_info.json"),
+ h5 = any(grepl("\\\\.h5$", files)), what = KALLISTO_ASSAYS,
+ as = c("SummarizedExperiment", "list", "matrix"))
+
+readKallistoBootstrap(file, i, j)
+}
+
+\arguments{
+
+ \item{files}{character() paths to kallisto \sQuote{abundance.tsv}
+ output files. The assumption is that files are organized in the way
+ implied by kallisto, with each sample in a distinct directory, and
+ the directory containing files abundance.tsv, run_info.json, and
+ perhaps abundance.h5.}
+
+ \item{json}{character() vector of the same length as \code{files}
+ specifying the location of JSON files produced by kallisto and
+ containing information on the run. The default assumes that json
+ files are in the same directory as the corresponding abundance
+ file.}
+
+ \item{h5}{character() vector of the same length as \code{files}
+ specifying the location of HDF5 files produced by kallisto and
+ containing bootstrap estimates. The default assumes that HDF5 files
+ are in the same directory as the corresponding abundance file.}
+
+ \item{what}{character() vector of kallisto per-sample outputs to be
+ input. See KALLISTO_ASSAYS for available values.}
+
+ \item{as}{character(1) specifying the output format. See \code{Value}
+ for additional detail.}
+
+ \item{file}{character(1) path to a single HDF5 output file.}
+
+ \item{i, j}{integer() vector of row (\code{i}) and column (\code{j})
+ indexes to input.}
+
+}
+
+\value{
+
+ A \code{SummarizedExperiment}, \code{list}, or \code{matrix},
+ depending on the value of argument \code{as}; by default a
+ \code{SummarizedExperiment}. The \code{as="SummarizedExperiment"}
+ \code{rowData(se)} the length of each transcript;
+ \code{colData(se)} includes summary information on each sample,
+ including the number of targets and bootstraps, the kallisto and index
+ version, the start time and operating system call used to create the
+ file. \code{assays()} contains one or more transcript x sample
+ matrices of parameters estimated by kallisto (see
+ \code{KALLISTO_ASSAYS}).
+
+ \code{as="list"} return value contains information simillar to
+ \code{SummarizedExperiment} with row, column and assay data as
+ elements of the list without coordination of row and column
+ annotations into an integrated data container. \code{as="matrix"}
+ returns the specified assay as a simple \emph{R} matrix.
+
+}
+
+\references{
+ \url{http://pachterlab.github.io/kallisto} software for quantifying
+ transcript abundance.
+}
+
+\author{
+ Martin Morgan \url{martin.morgan at roswellpark.org}
+}
+
+\examples{
+outputs <- system.file(package="SummarizedExperiment", "extdata",
+ "kallisto")
+files <- dir(outputs, pattern="abundance.tsv", full=TRUE, recursive=TRUE)
+stopifnot(all(file.exists(files)))
+
+## default: input 'est_counts'
+(se <- readKallisto(files, as="SummarizedExperiment"))
+str(readKallisto(files, as="list"))
+str(readKallisto(files, as="matrix"))
+
+## available assays
+KALLISTO_ASSAYS
+## one or more assay
+readKallisto(files, what=c("tpm", "eff_length"))
+
+## alternatively: read hdf5 files
+files <- sub(".tsv", ".h5", files, fixed=TRUE)
+readKallisto(files)
+
+## input all bootstraps
+xx <- readKallistoBootstrap(files[1])
+ridx <- head(which(rowSums(xx) != 0), 3)
+cidx <- c(1:5, 96:100)
+xx[ridx, cidx]
+
+## selective input of rows (transcripts) and/or bootstraps
+readKallistoBootstrap(files[1], i=c(ridx, rev(ridx)), j=cidx)
+}
+
+\keyword{file}
diff --git a/man/saveHDF5SummarizedExperiment.Rd b/man/saveHDF5SummarizedExperiment.Rd
new file mode 100644
index 0000000..05e4cfd
--- /dev/null
+++ b/man/saveHDF5SummarizedExperiment.Rd
@@ -0,0 +1,144 @@
+\name{saveHDF5SummarizedExperiment}
+
+\alias{saveHDF5SummarizedExperiment}
+\alias{loadHDF5SummarizedExperiment}
+
+\title{Save/load a HDF5-based SummarizedExperiment object}
+
+\description{
+ \code{saveHDF5SummarizedExperiment} and \code{loadHDF5SummarizedExperiment}
+ can be used to save/load a HDF5-based \link{SummarizedExperiment} object
+ to/from disk.
+}
+
+\usage{
+saveHDF5SummarizedExperiment(x, dir="my_h5_se", replace=FALSE,
+ chunk_dim=NULL, level=NULL, verbose=FALSE)
+loadHDF5SummarizedExperiment(dir="my_h5_se")
+}
+
+\arguments{
+ \item{x}{
+ A \link{SummarizedExperiment} object.
+ }
+ \item{dir}{
+ The path (as a single string) to the directory where to save the
+ HDF5-based \link{SummarizedExperiment} object or to load it from.
+ When saving, the directory will be created so should not already exist,
+ unless \code{replace} is set to \code{TRUE}.
+ }
+ \item{replace}{
+ If directory \code{dir} already exists, should it be replaced with a
+ new one? The content of the existing directory will be lost!
+ }
+ \item{chunk_dim, level}{
+ The dimensions of the chunks and the compression level to use for
+ writting the assay data to disk.
+ Passed to the internal calls to \code{HDF5Array::writeHDF5Array}.
+ See \code{?HDF5Array::\link[HDF5Array]{writeHDF5Array}} for more
+ information.
+ }
+ \item{verbose}{
+ Set to \code{TRUE} to make the function display progress.
+ }
+}
+
+\details{
+ These functions use functionalities from the \pkg{rhdf5} and \pkg{HDF5Array}
+ packages internally and so require these packages to be installed.
+
+ \code{saveHDF5SummarizedExperiment} creates the directory specified
+ thru the \code{dir} argument and then populates it with the HDF5 datasets
+ (one per assay in \code{x}) plus a serialized version of \code{x} that
+ contains pointers to these datasets. This directory provides a
+ self-contained HDF5-based representation of \code{x} that can then
+ be loaded back in R with \code{loadHDF5SummarizedExperiment}.
+ Note that this directory is \emph{relocatable} i.e. it can be moved
+ (or copied) to a different place, on the same or a different computer,
+ before calling \code{loadHDF5SummarizedExperiment} on it. For convenient
+ sharing with collaborators, it is suggested to turn it into a tarball
+ (with Unix command \code{tar}), or zip file, before the transfer.
+ Please keep in mind that \code{saveHDF5SummarizedExperiment} and
+ \code{loadHDF5SummarizedExperiment} don't know how to produce/read
+ tarballs or zip files at the moment, so the process of packaging/extracting
+ the tarball or zip file is entirely the user responsibility. It is
+ typically done from outside R.
+
+ Finally please note that, depending on the size of the data to write to
+ disk and the performance of the disk, \code{saveHDF5SummarizedExperiment}
+ can take a long time to complete. Use \code{verbose=TRUE} to see its
+ progress.
+
+ \code{loadHDF5SummarizedExperiment} is generally very fast, even if
+ the assay data is big, because all the assays in the returned object
+ are \link[HDF5Array]{HDF5Array} objects pointing to the on-disk HDF5
+ datasets located in \code{dir}. \link[HDF5Array]{HDF5Array} objects are
+ typically light-weight in memory.
+}
+
+\value{
+ \code{saveHDF5SummarizedExperiment} returns an invisible
+ \link{SummarizedExperiment} object where all the assays are
+ \link[HDF5Array]{HDF5Array} objects pointing to the HDF5 datasets
+ saved in \code{dir}. It's in fact the same obect as the object that
+ would be returned by calling \code{loadHDF5SummarizedExperiment} on
+ \code{dir}.
+}
+
+\author{Hervé Pagès}
+
+\seealso{
+ \itemize{
+ \item \link{SummarizedExperiment} and \link{RangedSummarizedExperiment}
+ objects.
+
+ \item \link[HDF5Array]{HDF5Array} objects in the \pkg{HDF5Array} package.
+
+ \item The \code{\link[HDF5Array]{writeHDF5Array}} function in the
+ \pkg{HDF5Array} package, which \code{saveHDF5SummarizedExperiment}
+ uses internally to write the assay data to disk.
+ }
+}
+
+\examples{
+nrows <- 200; ncols <- 6
+counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
+colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
+ row.names=LETTERS[1:6])
+se0 <- SummarizedExperiment(assays=SimpleList(counts=counts),
+ colData=colData)
+se0
+
+## Save 'se0' as an HDF5-based SummarizedExperiment object:
+dir <- sub("file", "h5_se0_", tempfile())
+h5_se0 <- saveHDF5SummarizedExperiment(se0, dir)
+h5_se0
+assay(h5_se0, withDimnames=FALSE) # HDF5Matrix object
+
+h5_se0b <- loadHDF5SummarizedExperiment(dir)
+h5_se0b
+assay(h5_se0b, withDimnames=FALSE) # HDF5Matrix object
+
+## Sanity checks:
+stopifnot(is(assay(h5_se0, withDimnames=FALSE), "HDF5Matrix"))
+stopifnot(all(DelayedArray(assay(se0)) == assay(h5_se0)))
+stopifnot(is(assay(h5_se0b, withDimnames=FALSE), "HDF5Matrix"))
+stopifnot(all(DelayedArray(assay(se0)) == assay(h5_se0b)))
+
+## ---------------------------------------------------------------------
+## More sanity checks
+## ---------------------------------------------------------------------
+
+## Make a copy of directory 'dir':
+somedir <- sub("file", "somedir", tempfile())
+dir.create(somedir)
+file.copy(dir, somedir, recursive=TRUE)
+dir2 <- list.files(somedir, full.names=TRUE)
+
+## 'dir2' contains a copy of 'dir'. Call loadHDF5SummarizedExperiment()
+## on it.
+h5_se0c <- loadHDF5SummarizedExperiment(dir2)
+
+stopifnot(is(assay(h5_se0c, withDimnames=FALSE), "HDF5Matrix"))
+stopifnot(all(DelayedArray(assay(se0)) == assay(h5_se0c)))
+}
diff --git a/tests/run_unitTests.R b/tests/run_unitTests.R
new file mode 100644
index 0000000..eabf7db
--- /dev/null
+++ b/tests/run_unitTests.R
@@ -0,0 +1,2 @@
+require("SummarizedExperiment") || stop("unable to load SummarizedExperiment package")
+SummarizedExperiment:::.test()
diff --git a/vignettes/SummarizedExperiment.Rmd b/vignettes/SummarizedExperiment.Rmd
new file mode 100644
index 0000000..5702f6b
--- /dev/null
+++ b/vignettes/SummarizedExperiment.Rmd
@@ -0,0 +1,345 @@
+---
+title: "_SummarizedExperiment_ for Coordinating Experimental Assays, Samples, and Regions of Interest"
+author: "Martin Morgan, Valerie Obenchain, Jim Hester, Hervé Pagès"
+date: "Revised: 22 June, 2016"
+output:
+ BiocStyle::html_document:
+ toc: true
+vignette: >
+ %\VignetteIndexEntry{SummarizedExperiment}
+ %\VignetteEngine{knitr::rmarkdown}
+ \usepackage[utf8]{inputenc}
+---
+
+```{r style, echo=FALSE, results='asis'}
+BiocStyle::markdown()
+```
+
+
+# Introduction
+
+The `SummarizedExperiment` class is used to store rectangular matrices of
+experimental results, which are commonly produced by sequencing and microarray
+experiments. Each object stores observations of one or more samples, along
+with additional meta-data describing both the observations (features) and
+samples (phenotypes).
+
+A key aspect of the `SummarizedExperiment` class is the coordination of the
+meta-data and assays when subsetting. For example, if you want to exclude a
+given sample you can do for both the meta-data and assay in one operation,
+which ensures the meta-data and observed data will remain in sync. Improperly
+accounting for meta and observational data has resulted in a number of
+incorrect results and retractions so this is a very desirable
+property.
+
+`SummarizedExperiment` is in many ways similar to the historical
+`ExpressionSet`, the main distinction being that `SummarizedExperiment` is more
+flexible in it's row information, allowing both `GRanges` based as well as those
+described by arbitrary `DataFrame`s. This makes it ideally suited to a variety
+of experiments, particularly sequencing based experiments such as RNA-Seq and
+ChIp-Seq.
+
+# Anatomy of a `SummarizedExperiment`
+
+The _SummarizedExperiment_ package contains two classes:
+`SummarizedExperiment` and `RangedSummarizedExperiment`.
+
+`SummarizedExperiment` is a matrix-like container where rows represent features
+of interest (e.g. genes, transcripts, exons, etc.) and columns represent
+samples. The objects contain one or more assays, each represented by a
+matrix-like object of numeric or other mode. The rows of a
+`SummarizedExperiment` object represent features of interest. Information
+about these features is stored in a `DataFrame` object, accessible using the
+function `rowData()`. Each row of the `DataFrame` provides information on the
+feature in the corresponding row of the `SummarizedExperiment` object. Columns
+of the DataFrame represent different attributes of the features of interest,
+e.g., gene or transcript IDs, etc.
+
+`RangedSummarizedExperiment` is the child of the `SummarizedExperiment` class
+which means that all the methods on `SummarizedExperiment` also work on a
+`RangedSummarizedExperiment`.
+
+The fundamental difference between the two classes is that the rows of a
+`RangedSummarizedExperiment` object represent genomic ranges of interest
+instead of a `DataFrame` of features. The `RangedSummarizedExperiment` ranges
+are described by a `GRanges` or a `GRangesList` object, accessible using the
+`rowRanges()` function.
+
+The following graphic displays the class geometry and highlights the
+vertical (column) and horizontal (row) relationships.
+
+
+```{r include = FALSE}
+# download current version of SE diagram
+#download.file("https://docs.google.com/feeds/download/drawings/Export?id=18OcDb80FpvSGRYnFl-8vUqwNNLaNHrG1I9SWKHCselo&exportFormat=svg", "SE.svg")
+download.file("https://docs.google.com/feeds/download/drawings/Export?id=1kiC8Qlo1mhSnLDqkGiRNPSo6GWn3C2duBszCFbJCB-g&exportFormat=svg", "SE.svg")
+```
+
+
+
+## Assays
+
+The `airway` package contains an example dataset from an RNA-Seq experiment of
+read counts per gene for airway smooth muscles. These data are stored
+in a `RangedSummarizedExperiment` object which contains 8 different
+experimental and assays 64,102 gene transcripts.
+
+```{r, echo=FALSE}
+suppressPackageStartupMessages(library(SummarizedExperiment))
+suppressPackageStartupMessages(data(airway, package="airway"))
+```
+
+```{r}
+library(SummarizedExperiment)
+data(airway, package="airway")
+se <- airway
+se
+```
+
+To retrieve the experiment data from a `SummarizedExperiment` object one can
+use the `assays()` accessor. An object can have multiple assay datasets
+each of which can be accessed using the `$` operator.
+The `airway` dataset contains only one assay (`counts`). Here each row
+represents a gene transcript and each column one of the samples.
+
+```{r assays, eval = FALSE}
+assays(se)$counts
+```
+
+```{r assays_table, echo = FALSE}
+knitr::kable(assays(se)$counts[1:10,])
+```
+
+## 'Row' (regions-of-interest) data
+The `rowRanges()` accessor is used to view the range information for a
+`RangedSummarizedExperiment`. (Note if this were the parent
+`SummarizedExperiment` class we'd use `rowData()`). The data are stored in a
+`GRangesList` object, where each list element corresponds to one gene
+transcript and the ranges in each `GRanges` correspond to the exons in the
+transcript.
+
+```{r rowRanges}
+rowRanges(se)
+```
+
+## 'Column' (sample) data
+
+Sample meta-data describing the samples can be accessed using `colData()`, and
+is a `DataFrame` that can store any number of descriptive columns for each
+sample row.
+
+```{r colData}
+colData(se)
+```
+
+This sample metadata can be accessed using the `$` accessor which makes it
+easy to subset the entire object by a given phenotype.
+
+```{r columnSubset}
+# subset for only those samples treated with dexamethasone
+se[, se$dex == "trt"]
+```
+
+## Experiment-wide metadata
+
+Meta-data describing the experimental methods and publication references can be
+accessed using `metadata()`.
+
+```{r metadata}
+metadata(se)
+```
+
+Note that `metadata()` is just a simple list, so it is appropriate for _any_
+experiment wide metadata the user wishes to save, such as storing model
+formulas.
+
+```{r metadata-formula}
+metadata(se)$formula <- counts ~ dex + albut
+
+metadata(se)
+```
+
+# Constructing a `SummarizedExperiment`
+
+Often, `SummarizedExperiment` or `RangedSummarizedExperiment` objects are
+returned by functions written by other packages. However it is possible to
+create them by hand with a call to the `SummarizedExperiment()` constructor.
+
+Constructing a `RangedSummarizedExperiment` with a `GRanges` as the
+_rowRanges_ argument:
+
+```{r constructRSE}
+nrows <- 200
+ncols <- 6
+counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
+rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),
+ IRanges(floor(runif(200, 1e5, 1e6)), width=100),
+ strand=sample(c("+", "-"), 200, TRUE),
+ feature_id=sprintf("ID%03d", 1:200))
+colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
+ row.names=LETTERS[1:6])
+
+SummarizedExperiment(assays=list(counts=counts),
+ rowRanges=rowRanges, colData=colData)
+```
+
+A `SummarizedExperiment` can be constructed with or without supplying
+a `DataFrame` for the _rowData_ argument:
+
+```{r constructSE}
+SummarizedExperiment(assays=list(counts=counts), colData=colData)
+```
+
+# Common operations on `SummarizedExperiment`
+
+## Subsetting
+
+- `[` Performs two dimensional subsetting, just like subsetting a matrix
+ or data frame.
+```{r 2d}
+# subset the first five transcripts and first three samples
+se[1:5, 1:3]
+```
+- `$` operates on `colData()` columns, for easy sample extraction.
+```{r colDataExtraction}
+se[, se$cell == "N61311"]
+```
+
+## Getters and setters
+
+- `rowRanges()` / (`rowData()`), `colData()`, `metadata()`
+```{r getSet}
+counts <- matrix(1:15, 5, 3, dimnames=list(LETTERS[1:5], LETTERS[1:3]))
+
+dates <- SummarizedExperiment(assays=list(counts=counts),
+ rowData=DataFrame(month=month.name[1:5], day=1:5))
+
+# Subset all January assays
+dates[rowData(dates)$month == "January", ]
+```
+
+- `assay()` versus `assays()`
+There are two accessor functions for extracting the assay data from a
+`SummarizedExperiment` object. `assays()` operates on the entire list of assay
+data as a whole, while `assay()` operates on only one assay at a time.
+`assay(x, i)` is simply a convenience function which is equivalent to
+`assays(x)[[i]]`.
+
+```{r assay_assays}
+assays(se)
+
+assays(se)[[1]][1:5, 1:5]
+
+# assay defaults to the first assay if no i is given
+assay(se)[1:5, 1:5]
+
+assay(se, 1)[1:5, 1:5]
+```
+
+## Range-based operations
+
+- `subsetByOverlaps()`
+`SummarizedExperiment` objects support all of the `findOverlaps()` methods and
+associated functions. This includes `subsetByOverlaps()`, which makes it easy
+to subset a `SummarizedExperiment` object by an interval.
+
+```{r overlap}
+# Subset for only rows which are in the interval 100,000 to 110,000 of
+# chromosome 1
+roi <- GRanges(seqnames="1", ranges=100000:1100000)
+subsetByOverlaps(se, roi)
+```
+
+# Advanced: Extending `RangedSummarizedExperiment`
+
+For representing and manipulating data in their own package, Bioconductor
+developers are encouraged to re-use existing classes defined in other
+packages like the `RangedSummarizedExperiment` or `GRanges` containers
+defined in the `SummarizedExperiment` or `GenomicRanges` infrastructure
+packages, respectively. Many Bioconductor packages are designed around
+these basic containers, that is, they define functions that take and/or
+return a `RangedSummarizedExperiment` or `GRanges` object.
+For example the `csaw` package defines various functions that operate
+on `RangedSummarizedExperiment` objects, which are used to represent the
+number of ChIP-seq reads from each BAM file overlapping pre-specified
+regions.
+
+However, sometimes re-using the `RangedSummarizedExperiment` class as-is
+does not satisfy the needs of the package and the developer makes the choice
+to extend the class in order to accomodate the special needs of the package
+and/or the specificities of the data that it deals with. For example the
+`DESeq2` package defines the `DESeqDataSet` class which extends
+`RangedSummarizedExperiment` to add the `design` and `dispersionFunction`
+slots to it.
+
+The following subsections describe in a nutshell how the developer would
+typically proceed for extending `RangedSummarizedExperiment` in his/her
+own package. Some familiarity with the S4 class system is required. Readers
+not familiar with the S4 class system are encouraged to consult the vignette
+`A quick overview of the S4 class system` located in the `S4Vectors` package
+for the basics of implementing and extending S4 classes.
+
+The approach described below allows the developer to extend
+`RangedSummarizedExperiment` it in a way that remains agnostic of its
+internals. Keeping this separation between the responsibilities of the owners
+of the parent and child classes facilitate maintenance in the long run.
+In particular, the implementation of the child class won't be affected by
+changes in the internals of the parent class.
+
+## Depend on, and import, the `SummarizedExperiment` package
+
+Add `SummarizedExperiment` to the Depends field of the DESCRIPTION file
+of the package and the `import(SummarizedExperiment)` directive to its
+NAMESPACE file.
+
+## Define and export the `RangedSummarizedExperiment` subclass
+
+Define the subclass with something like:
+```{r rseSubclass}
+setClass("MyRSESubclass",
+ contains="RangedSummarizedExperiment",
+ representation=representation(
+ slot1="integer",
+ slot2="function"
+ ## ... maybe more ...
+ )
+)
+```
+
+Export it by adding the `exportClasses(MyRSESubclass)` directive to the
+NAMESPACE file.
+
+## Construct `MyRSESubclass` instances
+
+When calling `new()` for constructing a `MyRSESubclass` instance, specify
+only the MyRSESubclass-specific slots:
+`new("MyRSESubclass", rse, slot1=value1, slot2=value2)`,
+where `rse` is a `RangedSummarizedExperiment` object.
+
+Providing a `MyRSESubclass` constructor function (named as the class itself)
+is recommended.
+
+## Define a validity method
+
+The validity method for `MyRSESubclass` only needs to take care of what's
+new in `MyRSESubclass` with respect to `RangedSummarizedExperiment`, that is,
+of the aspects of `MyRSESubclass` objects that are not already covered by the
+validity method for `RangedSummarizedExperiment` objects. This is because
+calling `validObject()` on a `MyRSESubclass` object automatically validates
+it as a `RangedSummarizedExperiment` object first and then calls the validity
+method for `MyRSESubclass` objects. In other words, validation works
+incrementally starting from the root of the class hierarchy and going in the
+parent-to-child direction.
+
+## Use the `RangedSummarizedExperiment` accessors on `MyRSESubclass` objects
+
+Like any user of `RangedSummarizedExperiment` objects, the developer of
+`MyRSESubclass` should always use the `RangedSummarizedExperiment` accessors
+to access the `RangedSummarizedExperiment`-specific parts of his/her
+`MyRSESubclass` objects.
+
+## Use `callNextMethod`
+
+In case some of the methods defined for `RangedSummarizedExperiment` objects
+need to be overwritten, the new methods should call `callNextMethod`
+internally.
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