Contents

1 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 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: 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 BioSample

To 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 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>

2.3 ‘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 SAMN02422677

This 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 BioSample

2.4 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 + albut

3 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): Treatment

A 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): Treatment

4 Top-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 0 2 1 0 0 2
## b 1 2 0 1 1 1
## c 3 1 0 0 1 0
## d 0 1 0 1 1 0
assay(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 104 108 112 116 120 124

However 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,]    0    2    1    0    0    2
## [2,]    1    2    0    1    1    1
## [3,]    3    1    0    0    1    0
## [4,]    0    1    0    1    1    0
assay(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,] 104 108 112 116 120 124
rownames(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 0.83362448 0.79540114 0.6378059 0.44139807 0.4485638 0.7342455
## bbb 0.22049187 0.29894403 0.5600608 0.30701858 0.4484535 0.4311587
## ccc 0.93404096 0.08438451 0.7690366 0.47550487 0.5151575 0.3103303
## ddd 0.01686751 0.68980657 0.4902932 0.03879237 0.2177989 0.3920195
assay(se3, 1, withDimnames=FALSE)  # identical to 'a1'
##            A          B         C          D         E         F
## a 0.83362448 0.79540114 0.6378059 0.44139807 0.4485638 0.7342455
## b 0.22049187 0.29894403 0.5600608 0.30701858 0.4484535 0.4311587
## c 0.93404096 0.08438451 0.7690366 0.47550487 0.5151575 0.3103303
## d 0.01686751 0.68980657 0.4902932 0.03879237 0.2177989 0.3920195

5 Common operations on SummarizedExperiment

5.1 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(10): gene_id gene_name ... seq_coord_system symbol
## 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: 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 BioSample

5.2 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() versus assays() There are two accessor functions for extracting 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): counts
assays(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         78
assay(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

5.3 Range-based operations

  • subsetByOverlaps() 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
## rowData names(10): gene_id gene_name ... seq_coord_system symbol
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(9): SampleName cell ... Sample BioSample

6 Interactive 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:

7 Session information

sessionInfo()
## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## 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_3.2.1.1            SummarizedExperiment_1.34.0
##  [3] Biobase_2.64.0              GenomicRanges_1.56.0       
##  [5] GenomeInfoDb_1.40.0         IRanges_2.38.0             
##  [7] S4Vectors_0.42.0            BiocGenerics_0.50.0        
##  [9] MatrixGenerics_1.16.0       matrixStats_1.3.0          
## [11] BiocStyle_2.32.0           
## 
## loaded via a namespace (and not attached):
##  [1] sass_0.4.9              utf8_1.2.4              SparseArray_1.4.0      
##  [4] lattice_0.22-6          digest_0.6.35           magrittr_2.0.3         
##  [7] evaluate_0.23           grid_4.4.0              bookdown_0.39          
## [10] pkgload_1.3.4           fastmap_1.1.1           rprojroot_2.0.4        
## [13] jsonlite_1.8.8          Matrix_1.7-0            brio_1.1.5             
## [16] BiocManager_1.30.22     httr_1.4.7              fansi_1.0.6            
## [19] UCSC.utils_1.0.0        jquerylib_0.1.4         abind_1.4-5            
## [22] cli_3.6.2               rlang_1.1.3             crayon_1.5.2           
## [25] XVector_0.44.0          withr_3.0.0             cachem_1.0.8           
## [28] DelayedArray_0.30.0     yaml_2.3.8              S4Arrays_1.4.0         
## [31] tools_4.4.0             GenomeInfoDbData_1.2.12 vctrs_0.6.5            
## [34] R6_2.5.1                lifecycle_1.0.4         zlibbioc_1.50.0        
## [37] waldo_0.5.2             desc_1.4.3              bslib_0.7.0            
## [40] pillar_1.9.0            glue_1.7.0              xfun_0.43              
## [43] knitr_1.46              htmltools_0.5.8.1       rmarkdown_2.26         
## [46] compiler_4.4.0