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 it that the meta-data and assays are coordinated when subsetting. For instance 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 (CITATIONS???), so this is a very desirable property.
The 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 GRange based as well as those described by arbitrary DataFrames. This makes it ideally suited to a variety of experiments, particularly sequencing based experiments such as RNA-Seq and ChIp-Seq.
redo fig. 2 of http://www.nature.com/nmeth/journal/v12/n2/abs/nmeth.3252.html
The airway package contains an example dataset from an RNA-Seq experiment of read counts per gene for airway smooth muscles. This data is stored in a SummarizedExperiment object which contains 8 different experimental and assays 64,102 gene transcripts.
data(airway, package="airway")
se <- airway
se## Loading required package: SummarizedExperiment
## Loading required package: GenomicRanges
## Loading required package: BiocGenerics
## Loading required package: parallel
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## Attaching package: 'BiocGenerics'
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## The following objects are masked from 'package:parallel':
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## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB
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## The following objects are masked from 'package:stats':
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## IQR, mad, xtabs
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## The following objects are masked from 'package:base':
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## Filter, Find, Map, Position, Reduce, anyDuplicated, append,
## as.data.frame, as.vector, cbind, 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, rownames,
## sapply, setdiff, sort, table, tapply, union, unique, unlist,
## unsplit
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## Loading required package: S4Vectors
## Loading required package: stats4
## 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")'.## class: RangedSummarizedExperiment
## dim: 64102 8
## metadata(1): ''
## assays(1): counts
## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
## rowRanges metadata column names(0):
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(9): SampleName cell ... Sample BioSampleTo retrieve the experiment data from a SummarizedExperiment object one can use the assays() accessor. Each SummarizedExperiment 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.
assays(se)$counts| SRR1039508 | SRR1039509 | SRR1039512 | SRR1039513 | SRR1039516 | SRR1039517 | SRR1039520 | SRR1039521 | |
|---|---|---|---|---|---|---|---|---|
| ENSG00000000003 | 679 | 448 | 873 | 408 | 1138 | 1047 | 770 | 572 |
| ENSG00000000005 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ENSG00000000419 | 467 | 515 | 621 | 365 | 587 | 799 | 417 | 508 |
| ENSG00000000457 | 260 | 211 | 263 | 164 | 245 | 331 | 233 | 229 |
| ENSG00000000460 | 60 | 55 | 40 | 35 | 78 | 63 | 76 | 60 |
| ENSG00000000938 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 |
| ENSG00000000971 | 3251 | 3679 | 6177 | 4252 | 6721 | 11027 | 5176 | 7995 |
| ENSG00000001036 | 1433 | 1062 | 1733 | 881 | 1424 | 1439 | 1359 | 1109 |
| ENSG00000001084 | 519 | 380 | 595 | 493 | 820 | 714 | 696 | 704 |
| ENSG00000001167 | 394 | 236 | 464 | 175 | 658 | 584 | 360 | 269 |
The rowRanges() accessor can be used to view the range information for each row in the dataset. This data is stored as a GRangesList object, where each object in the list corresponds to one gene transcript, and each GRanges object corresponds to one gene exon.
rowRanges(se)## GRangesList object of length 64102:
## $ENSG00000000003
## GRanges object with 17 ranges and 2 metadata columns:
## seqnames ranges strand | exon_id exon_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] X [99883667, 99884983] - | 667145 ENSE00001459322
## [2] X [99885756, 99885863] - | 667146 ENSE00000868868
## [3] X [99887482, 99887565] - | 667147 ENSE00000401072
## [4] X [99887538, 99887565] - | 667148 ENSE00001849132
## [5] X [99888402, 99888536] - | 667149 ENSE00003554016
## ... ... ... ... ... ... ...
## [13] X [99890555, 99890743] - | 667156 ENSE00003512331
## [14] X [99891188, 99891686] - | 667158 ENSE00001886883
## [15] X [99891605, 99891803] - | 667159 ENSE00001855382
## [16] X [99891790, 99892101] - | 667160 ENSE00001863395
## [17] X [99894942, 99894988] - | 667161 ENSE00001828996
##
## ...
## <64101 more elements>
## -------
## seqinfo: 722 sequences (1 circular) from an unspecified genomeThe SummarizedExperiment class can hold ranged information in the form of Genomic Range objects (GRanges and GRangesList), but it can also alternatively be a simple DataFrame object (TODO: clarify how to construct this).
counts <- matrix(1:15, 5, 3, dimnames=list(LETTERS[1:5], LETTERS[1:3]))
se0 <- SummarizedExperiment(assays=list(counts=counts))
mcols(se0) <- DataFrame(month=month.name[1:5], day=1:5)
# Subset all January assays
se0[mcols(se0)$month == "January", ]## class: SummarizedExperiment0
## dim: 1 3
## metadata(0):
## assays(1): counts
## rownames(1): A
## metadata column names(2): month day
## colnames(3): A B C
## colData names(0):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.
colData(se)## 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 SAMN02422677This sample metadata can be accessed using the $ accessor for the SummarizedExperiment object, which makes it easy to subset the entire object by a given phenotype.
# subset for only those samples treated with dexamethasone
se[, se$dex == "trt"]## class: RangedSummarizedExperiment
## dim: 64102 4
## metadata(1): ''
## assays(1): counts
## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
## rowRanges metadata column names(0):
## colnames(4): SRR1039509 SRR1039513 SRR1039517 SRR1039521
## colData names(9): SampleName cell ... Sample BioSampleMeta-data describing the experimental methods and publication references can be accessed using metadata().
metadata(se)## [[1]]
## Experiment data
## Experimenter name: Himes BE
## Laboratory: NA
## Contact information:
## Title: RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells.
## URL: http://www.ncbi.nlm.nih.gov/pubmed/24926665
## PMIDs: 24926665
##
## Abstract: A 226 word abstract is available. Use 'abstract' method.Note that metadata() is just a simple list, so it is appropriate for any experiment wide metadata the user wishes to save, such as storing model formulas.
metadata(se)$formula <- counts ~ dex + albut
metadata(se)## [[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.
##
## $formula
## counts ~ dex + albutOften, SummarizedExperiment objects are returned by functions written by other packages. However it is possible to create a SummarizedExperiment ‘by hand’.
nrows <- 200
ncols <- 6
counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),
IRanges(floor(runif(200, 1e5, 1e6)), width=100),
strand=sample(c("+", "-"), 200, TRUE),
feature_id=sprintf("ID%03d", 1:200))
colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
row.names=LETTERS[1:6])
rse <- SummarizedExperiment(assays=SimpleList(counts=counts),
rowRanges=rowRanges, colData=colData)
rse## class: RangedSummarizedExperiment
## dim: 200 6
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowRanges metadata column names(1): feature_id
## colnames(6): A B ... E F
## colData names(1): TreatmentSummarizedExperiment[ Performs two dimensional subsetting, just like subsetting a matrix or data frame.# subset the first five transcripts and first three samples
se[1:5, 1:3]## class: RangedSummarizedExperiment
## dim: 5 3
## metadata(2): '' formula
## assays(1): counts
## rownames(5): ENSG00000000003 ENSG00000000005 ENSG00000000419
## ENSG00000000457 ENSG00000000460
## rowRanges metadata column names(0):
## colnames(3): SRR1039508 SRR1039509 SRR1039512
## colData names(9): SampleName cell ... Sample BioSample$ operates on colData() columns, for easy sample extraction.se[, se$cell == "N61311"]## class: RangedSummarizedExperiment
## dim: 64102 2
## metadata(2): '' formula
## assays(1): counts
## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
## rowRanges metadata column names(0):
## colnames(2): SRR1039508 SRR1039509
## colData names(9): SampleName cell ... Sample BioSamplerowRanges() / (mcols()), colData(), metadata()counts <- matrix(1:15, 5, 3, dimnames=list(LETTERS[1:5], LETTERS[1:3]))
se0 <- SummarizedExperiment(assays=list(counts=counts))
mcols(se0) <- DataFrame(month=month.name[1:5], day=1:5)
# Subset all January assays
se0[mcols(se0)$month == "January", ]## class: SummarizedExperiment0
## dim: 1 3
## metadata(0):
## assays(1): counts
## rownames(1): A
## metadata column names(2): month day
## colnames(3): A B C
## colData names(0):assay() versus assays() There exist two accessor functions to extract 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]].assays(se)## List of length 1
## names(1): countsassays(se)[[1]][1:5, 1:5]## 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# assay defaults to the first assay if no i is given
assay(se)[1:5, 1:5]## 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 78assay(se, 1)[1:5, 1:5]## 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 78subsetByOverlaps() 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.# 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)## class: RangedSummarizedExperiment
## dim: 74 8
## metadata(2): '' formula
## assays(1): counts
## rownames(74): ENSG00000131591 ENSG00000177757 ... ENSG00000272512
## ENSG00000273443
## rowRanges metadata column names(0):
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(9): SampleName cell ... Sample BioSampleTODO, although see airways above
TODO