SummarizedExperiment for Coordinating Experimental Assays, Samples, and Regions of Interest
Revised: 3 May, 2018
Contents
1 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.
2 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
2.1 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.
library(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 |
2.2 ‘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
##
## ...
## <64101 more elements>
## -------
## seqinfo: 722 sequences (1 circular) from an unspecified genome2.3 ‘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$dex == "trt"]## class: RangedSummarizedExperiment
## dim: 64102 4
## metadata(1): ''
## assays(1): counts
## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
## rowData names(0):
## colnames(4): SRR1039509 SRR1039513 SRR1039517 SRR1039521
## colData names(9): SampleName cell ... Sample BioSample2.4 Experiment-wide metadata
Meta-data describing the experimental methods and publication references can be
accessed using metadata().
metadata(se)## [[1]]
## Experiment data
## Experimenter name: Himes BE
## Laboratory: NA
## Contact information:
## Title: RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells.
## URL: http://www.ncbi.nlm.nih.gov/pubmed/24926665
## PMIDs: 24926665
##
## Abstract: A 226 word abstract is available. Use 'abstract' method.Note that metadata() is just a simple list, so it is appropriate for any
experiment wide metadata the user wishes to save, such as storing model
formulas.
metadata(se)$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.
##
## $formula
## counts ~ dex + albut3 Constructing a SummarizedExperiment
Often, SummarizedExperiment or RangedSummarizedExperiment objects are
returned by functions written by other packages. However it is possible to
create them by hand with a call to the SummarizedExperiment() constructor.
Constructing a RangedSummarizedExperiment with a GRanges as the
rowRanges argument:
nrows <- 200
ncols <- 6
counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),
IRanges(floor(runif(200, 1e5, 1e6)), width=100),
strand=sample(c("+", "-"), 200, TRUE),
feature_id=sprintf("ID%03d", 1:200))
colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
row.names=LETTERS[1:6])
SummarizedExperiment(assays=list(counts=counts),
rowRanges=rowRanges, colData=colData)## class: RangedSummarizedExperiment
## dim: 200 6
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(1): feature_id
## colnames(6): A B ... E F
## colData names(1): TreatmentA SummarizedExperiment can be constructed with or without supplying
a DataFrame for the rowData argument:
SummarizedExperiment(assays=list(counts=counts), colData=colData)## class: SummarizedExperiment
## dim: 200 6
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(0):
## colnames(6): A B ... E F
## colData names(1): Treatment4 Common operations on SummarizedExperiment
4.1 Subsetting
[Performs two dimensional subsetting, just like subsetting a matrix or data frame.
# subset the first five transcripts and first three samples
se[1:5, 1:3]## class: RangedSummarizedExperiment
## dim: 5 3
## metadata(2): '' formula
## assays(1): counts
## rownames(5): ENSG00000000003 ENSG00000000005 ENSG00000000419
## ENSG00000000457 ENSG00000000460
## rowData names(0):
## colnames(3): SRR1039508 SRR1039509 SRR1039512
## colData names(9): SampleName cell ... Sample BioSample$operates oncolData()columns, for easy sample extraction.
se[, se$cell == "N61311"]## class: RangedSummarizedExperiment
## dim: 64102 2
## metadata(2): '' formula
## assays(1): counts
## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
## rowData names(0):
## colnames(2): SRR1039508 SRR1039509
## colData names(9): SampleName cell ... Sample BioSample4.2 Getters and setters
rowRanges()/ (rowData()),colData(),metadata()
counts <- matrix(1:15, 5, 3, dimnames=list(LETTERS[1:5], LETTERS[1:3]))
dates <- SummarizedExperiment(assays=list(counts=counts),
rowData=DataFrame(month=month.name[1:5], day=1:5))
# Subset all January assays
dates[rowData(dates)$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 35 784.3 Range-based operations
subsetByOverlaps()SummarizedExperimentobjects support all of thefindOverlaps()methods and associated functions. This includessubsetByOverlaps(), which makes it easy to subset aSummarizedExperimentobject 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
## rowData names(0):
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(9): SampleName cell ... Sample BioSample5 Session information
sessionInfo()## R version 3.5.1 Patched (2018-07-12 r74967)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.8-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.8-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] testthat_2.0.1 SummarizedExperiment_1.12.0
## [3] DelayedArray_0.8.0 BiocParallel_1.16.0
## [5] matrixStats_0.54.0 Biobase_2.42.0
## [7] GenomicRanges_1.34.0 GenomeInfoDb_1.18.0
## [9] IRanges_2.16.0 S4Vectors_0.20.0
## [11] BiocGenerics_0.28.0 BiocStyle_2.10.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.19 compiler_3.5.1 BiocManager_1.30.3
## [4] highr_0.7 XVector_0.22.0 bitops_1.0-6
## [7] tools_3.5.1 zlibbioc_1.28.0 digest_0.6.18
## [10] evaluate_0.12 lattice_0.20-35 rlang_0.3.0.1
## [13] Matrix_1.2-14 yaml_2.2.0 xfun_0.4
## [16] GenomeInfoDbData_1.2.0 stringr_1.3.1 knitr_1.20
## [19] rprojroot_1.3-2 grid_3.5.1 R6_2.3.0
## [22] rmarkdown_1.10 bookdown_0.7 magrittr_1.5
## [25] backports_1.1.2 htmltools_0.3.6 stringi_1.2.4
## [28] RCurl_1.95-4.11