SummarizedExperiment for Coordinating Experimental Assays, Samples, and Regions of Interest (original) (raw)
Contents
- 1 Introduction
- 2 Anatomy of a SummarizedExperiment
- 3 Constructing a SummarizedExperiment
- 4 Top-level dimnames vs assay-level dimnames
- 5 Common operations on SummarizedExperiment
- 6 Interactive visualization
- 7 Session information
Introduction
The SummarizedExperiment class is used to store rectangular matrices of experimental results, which are commonly produced by sequencing and microarray experiments. Note that SummarizedExperiment can simultaneously manage several experimental results or assays as long as they be of the same dimensions.
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 historicalExpressionSet, 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.
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 aSummarizedExperiment 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 aRangedSummarizedExperiment.
The fundamental difference between the two classes is that the rows of aRangedSummarizedExperiment 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 therowRanges() 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.
library(SummarizedExperiment)
data(airway, package="airway")
se <- airway
se## class: RangedSummarizedExperiment
## dim: 63677 8
## metadata(1): ''
## assays(1): counts
## rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492
## ENSG00000273493
## rowData names(10): gene_id gene_name ... seq_coord_system symbol
## 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‘Row’ (regions-of-interest) data
The rowRanges() accessor is used to view the range information for aRangedSummarizedExperiment. (Note if this were the parentSummarizedExperiment class we’d use rowData()). The data are stored in aGRangesList 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 63677:
## $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
##
## ...
## <63676 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: 63677 4
## metadata(1): ''
## assays(1): counts
## rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492
## ENSG00000273493
## rowData names(10): gene_id gene_name ... seq_coord_system symbol
## colnames(4): SRR1039509 SRR1039513 SRR1039517 SRR1039521
## colData names(9): SampleName cell ... Sample BioSampleConstructing 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): TreatmentTop-level dimnames vs assay-level dimnames
In addition to the dimnames that are set on a SummarizedExperiment object itself, the individual assays that are stored in the object can have their own dimnames or not:
a1 <- matrix(runif(24), ncol=6, dimnames=list(letters[1:4], LETTERS[1:6]))
a2 <- matrix(rpois(24, 0.8), ncol=6)
a3 <- matrix(101:124, ncol=6, dimnames=list(NULL, LETTERS[1:6]))
se3 <- SummarizedExperiment(SimpleList(a1, a2, a3))The dimnames of the SummarizedExperiment object (top-level dimnames):
dimnames(se3)## [[1]]
## [1] "a" "b" "c" "d"
##
## [[2]]
## [1] "A" "B" "C" "D" "E" "F"When extracting assays from the object, the top-level dimnames are put on them by default:
assay(se3, 2) # this is 'a2', but with the top-level dimnames on it## A B C D E F
## a 1 4 0 0 4 0
## b 0 0 2 2 1 0
## c 2 0 3 2 0 0
## d 0 3 0 0 0 0assay(se3, 3) # this is 'a3', but with the top-level dimnames on it## A B C D E F
## a 101 105 109 113 117 121
## b 102 106 110 114 118 122
## c 103 107 111 115 119 123
## d 104 108 112 116 120 124However if using withDimnames=FALSE then the assays are returned_as-is_, i.e. with their original dimnames (this is how they are stored in the SummarizedExperiment object):
assay(se3, 2, withDimnames=FALSE) # identical to 'a2'## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1 4 0 0 4 0
## [2,] 0 0 2 2 1 0
## [3,] 2 0 3 2 0 0
## [4,] 0 3 0 0 0 0assay(se3, 3, withDimnames=FALSE) # identical to 'a3'## A B C D E F
## [1,] 101 105 109 113 117 121
## [2,] 102 106 110 114 118 122
## [3,] 103 107 111 115 119 123
## [4,] 104 108 112 116 120 124rownames(se3) <- strrep(letters[1:4], 3)
dimnames(se3)## [[1]]
## [1] "aaa" "bbb" "ccc" "ddd"
##
## [[2]]
## [1] "A" "B" "C" "D" "E" "F"assay(se3, 1) # this is 'a1', but with the top-level dimnames on it## A B C D E F
## aaa 0.03597588 0.4415240 0.9327776 0.3805434 0.1553065 0.81731544
## bbb 0.44685523 0.3087763 0.3452913 0.2482776 0.8927409 0.08224812
## ccc 0.17487674 0.6414354 0.7521367 0.3058408 0.2291193 0.90517727
## ddd 0.43777252 0.1364569 0.4737047 0.1763254 0.6660831 0.45427178assay(se3, 1, withDimnames=FALSE) # identical to 'a1'## A B C D E F
## a 0.03597588 0.4415240 0.9327776 0.3805434 0.1553065 0.81731544
## b 0.44685523 0.3087763 0.3452913 0.2482776 0.8927409 0.08224812
## c 0.17487674 0.6414354 0.7521367 0.3058408 0.2291193 0.90517727
## d 0.43777252 0.1364569 0.4737047 0.1763254 0.6660831 0.45427178Common 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(10): gene_id gene_name ... seq_coord_system symbol
## colnames(3): SRR1039508 SRR1039509 SRR1039512
## colData names(9): SampleName cell ... Sample BioSample$operates oncolData()columns, for easy sample extraction.
se[, se$cell == "N61311"]## class: RangedSummarizedExperiment
## dim: 63677 2
## metadata(2): '' formula
## assays(1): counts
## rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492
## ENSG00000273493
## rowData names(10): gene_id gene_name ... seq_coord_system symbol
## colnames(2): SRR1039508 SRR1039509
## colData names(9): SampleName cell ... Sample BioSampleGetters and setters
rowRanges()/ (rowData()),colData(),metadata()
counts <- matrix(1:15, 5, 3, dimnames=list(LETTERS[1:5], LETTERS[1:3]))
dates <- SummarizedExperiment(assays=list(counts=counts),
rowData=DataFrame(month=month.name[1:5], day=1:5))
# Subset all January assays
dates[rowData(dates)$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.
# 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(10): gene_id gene_name ... seq_coord_system symbol
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(9): SampleName cell ... Sample BioSampleInteractive visualization
The iSEE package provides functions for creating an interactive user interface based on the shiny package for exploring data stored in SummarizedExperiment objects. Information stored in standard components of SummarizedExperiment objects – including assay data, and row and column metadata – are automatically detected and used to populate the interactive multi-panel user interface. Particular attention is given to the SingleCellExperiment extension of the SummarizedExperiment class, with visualization of dimensionality reduction results.
Extensions to the iSEE package provide support for more context-dependent functionality:
- iSEEde provides additional panels that facilitate the interactive visualization of differential expression results, including the
DESeqDataSetextension ofSummarizedExperimentimplemented in DESeq2. - iSEEpathways provides additional panels for the interactive visualization of pathway analysis results.
- iSEEhub provides functionality to import data sets stored in the Bioconductor ExperimentHub.
- iSEEhub provides functionality to import data sets from custom sources (local and remote).
Session information
sessionInfo()## R version 4.5.0 RC (2025-04-04 r88126)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB 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
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] testthat_3.2.3 SummarizedExperiment_1.38.1
## [3] Biobase_2.68.0 GenomicRanges_1.60.0
## [5] GenomeInfoDb_1.44.0 IRanges_2.42.0
## [7] S4Vectors_0.46.0 BiocGenerics_0.54.0
## [9] generics_0.1.3 MatrixGenerics_1.20.0
## [11] matrixStats_1.5.0 BiocStyle_2.36.0
##
## loaded via a namespace (and not attached):
## [1] Matrix_1.7-3 jsonlite_2.0.0 compiler_4.5.0
## [4] BiocManager_1.30.25 crayon_1.5.3 brio_1.1.5
## [7] jquerylib_0.1.4 yaml_2.3.10 fastmap_1.2.0
## [10] lattice_0.22-7 R6_2.6.1 XVector_0.48.0
## [13] S4Arrays_1.8.0 knitr_1.50 DelayedArray_0.34.1
## [16] bookdown_0.43 desc_1.4.3 rprojroot_2.0.4
## [19] GenomeInfoDbData_1.2.14 pillar_1.10.2 bslib_0.9.0
## [22] rlang_1.1.6 cachem_1.1.0 xfun_0.52
## [25] sass_0.4.10 pkgload_1.4.0 SparseArray_1.8.0
## [28] cli_3.6.5 withr_3.0.2 magrittr_2.0.3
## [31] digest_0.6.37 grid_4.5.0 lifecycle_1.0.4
## [34] vctrs_0.6.5 waldo_0.6.1 glue_1.8.0
## [37] evaluate_1.0.3 abind_1.4-8 rmarkdown_2.29
## [40] httr_1.4.7 tools_4.5.0 htmltools_0.5.8.1
## [43] UCSC.utils_1.4.0