The RangedSummarizedExperiment class is a matrix-like container where rows represent ranges of interest (as a GRanges or GRangesList object) and columns represent samples (with sample data summarized as a 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 SummarizedExperiment and,
as such, all the methods documented in ?SummarizedExperiment
also work on a RangedSummarizedExperiment object. The methods documented
below are additional methods that are specific to RangedSummarizedExperiment
objects.
## Constructor
SummarizedExperiment(assays, ...)
"SummarizedExperiment"(assays, rowData=NULL, rowRanges=GRangesList(), colData=DataFrame(), metadata=list())
"SummarizedExperiment"(assays, ...)
"SummarizedExperiment"(assays, ...)
"SummarizedExperiment"(assays, ...)
## Accessors
rowRanges(x, ...)
rowRanges(x, ...) <- value
## Subsetting
"subset"(x, subset, select, ...)
## rowRanges access
## see 'GRanges compatibility', belowlist or 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 rowRanges and
colData.assays.
If rowRanges is missing, a SummarizedExperiment
instance is returned.list of arbitrary content
describing the overall experiment.SummarizedExperiment, S4 methods list
and matrix, arguments identical to those of the
SimpleList method. For rowRanges, ignored.
rowRanges setter
will also accept a SummarizedExperiment object and will first
coerce it to RangedSummarizedExperiment before it sets value on
it.rowRanges(x), is a logical vector indicating
elements or rows to keep: missing values are taken as false.colData(x), is a logical vector indicating
elements or rows to keep: missing values are taken as false.SummarizedExperiment function with arguments outlined above. x is a RangedSummarizedExperiment
object. rowRanges(x), rowRanges(x) <- value:value is a GenomicRanges object. Row
names of value must be NULL or consistent with the existing
row names of x.rowRanges. Supported operations include: pcompare,
duplicated, end, end<-,
granges, is.unsorted, match,
mcols, mcols<-, order,
ranges, ranges<-, rank,
seqinfo, seqinfo<-, seqnames,
sort, start, start<-,
strand, strand<-,
width, width<-. See also ?shift,
?isDisjoint,
?coverage,
?findOverlaps, and
?nearest for more
GRanges compatibility methods. Not all 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<-). x is a RangedSummarizedExperiment
object. subset(x, subset, select):x
using an expression subset referring to columns of
rowRanges(x) (including ‘seqnames’, ‘start’,
‘end’, ‘width’, ‘strand’, and
names(rowData(x))) and / or select referring to
column names of colData(x).contains="RangedSummarizedExperiment"
in the new class definition. The rows of a RangedSummarizedExperiment object represent ranges
(in genomic coordinates) of interest. The ranges of interest are
described by a GRanges or a GRangesList object, accessible
using the rowRanges function, described below. The GRanges
and 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.
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)))
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