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 DataFrames. This makes it ideally
suited to a variety of experiments, particularly sequencing based
experiments such as RNA-Seq and ChIp-Seq.
BiocManager::install('airway')
BiocManager::install('SummarizedExperiment')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.
Summarized Experiment
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.
## Loading required package: airwaylibrary(SummarizedExperiment)
data(airway, package="airway")
se <- airway
se## 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 BioSampleTo 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.
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 |
‘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.
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
## -------
## seqinfo: 722 sequences (1 circular) from an unspecified genome
##
## ...
## <64101 more elements>‘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.
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 which makes it easy to subset the entire object by a given
phenotype.
# subset for only those samples treated with dexamethasone
se[, se$dex == "trt"]## class: RangedSummarizedExperiment
## dim: 64102 4
## metadata(1): ''
## assays(1): counts
## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
## rowData names(0):
## colnames(4): SRR1039509 SRR1039513 SRR1039517 SRR1039521
## colData names(9): SampleName cell ... Sample BioSampleExperiment-wide metadata
Meta-data describing the experimental methods and publication
references can be accessed using metadata().
metadata(se)## [[1]]
## Experiment data
## Experimenter name: Himes BE
## Laboratory: NA
## Contact information:
## Title: RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells.
## URL: http://www.ncbi.nlm.nih.gov/pubmed/24926665
## PMIDs: 24926665
##
## Abstract: A 226 word abstract is available. Use 'abstract' method.Note that metadata() is just a simple list, so it is
appropriate for any experiment wide metadata the user wishes to
save, such as storing model formulas.
metadata(se)$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.
##
## $formula
## counts ~ dex + albutCommon operations on SummarizedExperiment
Subsetting
[Performs two dimensional subsetting, just like subsetting a matrix or data frame.
# subset the first five transcripts and first three samples
se[1:5, 1:3]## class: RangedSummarizedExperiment
## dim: 5 3
## metadata(2): '' formula
## assays(1): counts
## rownames(5): ENSG00000000003 ENSG00000000005 ENSG00000000419
## ENSG00000000457 ENSG00000000460
## rowData names(0):
## colnames(3): SRR1039508 SRR1039509 SRR1039512
## colData names(9): SampleName cell ... Sample BioSample$operates oncolData()columns, for easy sample extraction.
se[, se$cell == "N61311"]## class: RangedSummarizedExperiment
## dim: 64102 2
## metadata(2): '' formula
## assays(1): counts
## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
## rowData names(0):
## colnames(2): SRR1039508 SRR1039509
## colData names(9): SampleName cell ... Sample BioSampleGetters and setters
rowRanges()/ (rowData()),colData(),metadata()
counts <- matrix(1:15, 5, 3, dimnames=list(LETTERS[1:5], LETTERS[1:3]))
dates <- SummarizedExperiment(assays=list(counts=counts),
rowData=DataFrame(month=month.name[1:5], day=1:5))
# Subset all January assays
dates[rowData(dates)$month == "January", ]## 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):assay()versusassays()There are two accessor functions for extracting the assay data from aSummarizedExperimentobject.assays()operates on the entire list of assay data as a whole, whileassay()operates on only one assay at a time.assay(x, i)is simply a convenience function which is equivalent toassays(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 78Range-based operations
subsetByOverlaps()SummarizedExperimentobjects support all of thefindOverlaps()methods and associated functions. This includessubsetByOverlaps(), which makes it easy to subset aSummarizedExperimentobject by an interval.
In tne next code block, we define a region of interest (or many
regions of interest) and then subset our
SummarizedExperiment by overlaps with this region.
# Subset for only rows which are in the interval 100,000 to 110,000 of
# chromosome 1
roi <- GRanges(seqnames="1", ranges=100000:1100000)
sub_se = subsetByOverlaps(se, roi)
sub_se## 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 BioSampledim(sub_se)## [1] 74 8Constructing 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. The code below is simply to illustrate the mechanics of
creating an object from scratch. In practice, you will probably have the
pieces of the object from other sources such as Excel files or csv
files.
Constructing a RangedSummarizedExperiment with a
GRanges as the rowRanges argument:
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)## 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): TreatmentA SummarizedExperiment can be constructed with or
without supplying a DataFrame for the rowData
argument:
SummarizedExperiment(assays=list(counts=counts), colData=colData)## class: SummarizedExperiment
## dim: 200 6
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(0):
## colnames(6): A B ... E F
## colData names(1): TreatmentsessionInfo()
sessionInfo()## R version 4.2.0 (2022-04-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur/Monterey 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] airway_1.16.0 SummarizedExperiment_1.26.1
## [3] Biobase_2.56.0 GenomicRanges_1.48.0
## [5] GenomeInfoDb_1.32.0 IRanges_2.30.0
## [7] S4Vectors_0.34.0 BiocGenerics_0.42.0
## [9] MatrixGenerics_1.8.0 matrixStats_0.62.0
##
## loaded via a namespace (and not attached):
## [1] highr_0.9 bslib_0.3.1 compiler_4.2.0
## [4] jquerylib_0.1.4 XVector_0.36.0 bitops_1.0-7
## [7] tools_4.2.0 zlibbioc_1.42.0 digest_0.6.29
## [10] jsonlite_1.8.0 evaluate_0.15 lattice_0.20-45
## [13] rlang_1.0.2 Matrix_1.4-1 DelayedArray_0.22.0
## [16] cli_3.3.0 rstudioapi_0.13 yaml_2.3.5
## [19] xfun_0.30 fastmap_1.1.0 GenomeInfoDbData_1.2.8
## [22] stringr_1.4.0 knitr_1.39 sass_0.4.1
## [25] grid_4.2.0 R6_2.5.1 rmarkdown_2.14
## [28] magrittr_2.0.3 htmltools_0.5.2 stringi_1.7.6
## [31] RCurl_1.98-1.6