RangedSummarizedExperiment-class: RangedSummarizedExperiment objects

In SummarizedExperiment: SummarizedExperiment container

Description

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 class?SummarizedExperiment also work on a RangedSummarizedExperiment object. The methods documented below are additional methods that are specific to RangedSummarizedExperiment objects.

Usage

## Constructor

SummarizedExperiment(assays=SimpleList(),
                     rowData=NULL, rowRanges=GRangesList(),
                     colData=DataFrame(),
                     metadata=list())

## Accessors

rowRanges(x, ...)
rowRanges(x, ...) <- value

## Subsetting

## S4 method for signature 'RangedSummarizedExperiment'
subset(x, subset, select, ...)

## rowRanges access
## see 'GRanges compatibility', below

Arguments

assays	A list or SimpleList of matrix-like elements, or a matrix-like object (e.g. an ordinary matrix, a data frame, a DataFrame object from the S4Vectors package, a sparseMatrix derivative from the Matrix package, a DelayedMatrix object from the DelayedArray package, etc...). 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.
rowData	A DataFrame object describing the rows. Row names, if present, become the row names of the SummarizedExperiment object. The number of rows of the DataFrame must equal the number of rows of the matrices in assays.
rowRanges	A GRanges or GRangesList object describing the ranges of interest. Names, if present, become the row names of the SummarizedExperiment object. The length of the GRanges or GRangesList must equal the number of rows of the matrices in assays. If rowRanges is missing, a SummarizedExperiment instance is returned.
colData	An optional DataFrame describing the samples. Row names, if present, become the column names of the RangedSummarizedExperiment.
metadata	An optional list of arbitrary content describing the overall experiment.
x	A RangedSummarizedExperiment object. The rowRanges setter will also accept a SummarizedExperiment object and will first coerce it to RangedSummarizedExperiment before it sets value on it.
...	Further arguments to be passed to or from other methods.
value	A GRanges or GRangesList object.
subset	An expression which, when evaluated in the context of rowRanges(x), is a logical vector indicating elements or rows to keep: missing values are taken as false.
select	An expression which, when evaluated in the context of 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 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.

Constructor

RangedSummarizedExperiment instances are constructed using the SummarizedExperiment() function with arguments outlined above.

Accessors

In the following code snippets, x is a RangedSummarizedExperiment object.

rowRanges(x), rowRanges(x) <- value:

Get or set the row data. value is a GenomicRanges object. Row names of value must be NULL or consistent with the existing row names of x.

GRanges compatibility (rowRanges access)

Many GRanges and GRangesList operations are supported on RangedSummarizedExperiment objects, using 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<-).

Subsetting

In the code snippets below, x is a RangedSummarizedExperiment object.

subset(x, subset, select):

Create a subset of 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).

Extension

RangedSummarizedExperiment is implemented as an S4 class, and can be extended in the usual way, using contains="RangedSummarizedExperiment" in the new class definition.

Author(s)

Martin Morgan, [e-mail]

See Also

• SummarizedExperiment-class

• shift, isDisjoint, coverage, findOverlaps, and nearest for more GRanges compatibility methods.

• GRanges objects in the 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)))

Example output

Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colMeans, colSums, colnames, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
    pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
    setdiff, sort, table, tapply, union, unique, unsplit, which,
    which.max, which.min

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following object is masked from 'package:base':

    expand.grid

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: DelayedArray
Loading required package: matrixStats

Attaching package: 'matrixStats'

The following objects are masked from 'package:Biobase':

    anyMissing, rowMedians


Attaching package: 'DelayedArray'

The following objects are masked from 'package:matrixStats':

    colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges

The following object is masked from 'package:base':

    apply

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
[1] 200   6
[[1]]
NULL

[[2]]
[1] "A" "B" "C" "D" "E" "F"

[1] "counts"
            A        B         C         D        E        F
[1,] 6330.330 6360.622 8845.1648 8292.1898 5743.753 6693.240
[2,] 7982.410 3963.526 1872.7335 9053.0384 3011.502 5394.960
[3,] 1688.534 4319.061 8877.4737 4707.2695 6254.098 1976.238
[4,] 4286.985 4694.187 3225.5930 1643.3986 9373.420 9340.242
[5,] 9368.335 4752.894  917.7643 9899.4702 6577.467 4170.116
[6,] 3948.635 8345.333 3117.4793  918.5788 9253.385 6434.552
            A        B        C        D        E        F
[1,] 9.446255 9.451029 9.780773 9.716217 9.349015 9.502000
[2,] 9.678143 8.978036 8.228302 9.804003 8.703341 9.286368
[3,] 8.124763 9.063940 9.784419 9.150010 9.434139 8.282097
[4,] 9.056486 9.147227 8.772019 8.097669 9.838780 9.835235
[5,] 9.838238 9.159656 7.515088 9.893384 9.484552 9.028846
[6,] 8.974272 9.722605 8.737927 7.515975 9.825892 9.462585
GRanges object with 200 ranges and 1 metadata column:
        seqnames           ranges strand |  feature_id
           <Rle>        <IRanges>  <Rle> | <character>
    [1]     chr1 [279582, 279681]      - |       ID001
    [2]     chr1 [497279, 497378]      - |       ID002
    [3]     chr1 [289724, 289823]      + |       ID003
    [4]     chr1 [743432, 743531]      - |       ID004
    [5]     chr1 [712372, 712471]      - |       ID005
    ...      ...              ...    ... .         ...
  [196]     chr2 [227832, 227931]      - |       ID196
  [197]     chr2 [851492, 851591]      + |       ID197
  [198]     chr2 [259616, 259715]      - |       ID198
  [199]     chr2 [624392, 624491]      - |       ID199
  [200]     chr2 [124487, 124586]      - |       ID200
  -------
  seqinfo: 2 sequences from an unspecified genome; no seqlengths
DataFrame with 200 rows and 1 column
     feature_id
    <character>
1         ID001
2         ID002
3         ID003
4         ID004
5         ID005
...         ...
196       ID196
197       ID197
198       ID198
199       ID199
200       ID200
DataFrame with 6 rows and 1 column
    Treatment
  <character>
A        ChIP
B       Input
C        ChIP
D       Input
E        ChIP
F       Input
class: RangedSummarizedExperiment 
dim: 200 3 
metadata(0):
assays(1): counts
rownames: NULL
rowData names(1): feature_id
colnames(3): A C E
colData names(1): Treatment
[1] 200   9
[[1]]
NULL

[[2]]
[1] "A" "B" "C" "D" "E" "F" "a" "b" "c"

[1] 250   6
[[1]]
  [1] ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  "" 
 [19] ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  "" 
 [37] ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  "" 
 [55] ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  "" 
 [73] ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  "" 
 [91] ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  "" 
[109] ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  "" 
[127] ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  "" 
[145] ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  "" 
[163] ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  "" 
[181] ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  "" 
[199] ""  ""  "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p"
[217] "q" "r" "s" "t" "u" "v" "w" "x" "y" "z" NA  NA  NA  NA  NA  NA  NA  NA 
[235] NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

[[2]]
[1] "A" "B" "C" "D" "E" "F"

class: SummarizedExperiment 
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
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
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

SummarizedExperiment documentation built on Nov. 8, 2020, 8:28 p.m.