# 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.

## 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
## 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 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 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 genome ## 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$dex == "trt"] ## class: RangedSummarizedExperiment ## dim: 64102 4 ## metadata(1): '' ## assays(1): counts ## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99 ## rowData names(0): ## colnames(4): SRR1039509 SRR1039513 SRR1039517 SRR1039521 ## colData names(9): SampleName cell ... Sample BioSample ## 2.4 Experiment-wide metadata Meta-data describing the experimental methods and publication references can be accessed using metadata(). metadata(se) ## [[1]] ## Experiment data ## Experimenter name: Himes BE ## Laboratory: NA ## Contact information: ## Title: RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells. ## URL: http://www.ncbi.nlm.nih.gov/pubmed/24926665 ## PMIDs: 24926665 ## ## Abstract: A 226 word abstract is available. Use 'abstract' method. Note that metadata() is just a simple list, so it is appropriate for any experiment wide metadata the user wishes to save, such as storing model formulas. metadata(se)$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.
##
se[, se$cell == "N61311"] ## class: RangedSummarizedExperiment ## dim: 64102 2 ## metadata(2): '' formula ## assays(1): counts ## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99 ## rowData names(0): ## colnames(2): SRR1039508 SRR1039509 ## colData names(9): SampleName cell ... Sample BioSample ## 4.2 Getters and setters • rowRanges() / (rowData()), colData(), metadata() counts <- matrix(1:15, 5, 3, dimnames=list(LETTERS[1:5], LETTERS[1:3])) dates <- SummarizedExperiment(assays=list(counts=counts), rowData=DataFrame(month=month.name[1:5], day=1:5)) # Subset all January assays dates[rowData(dates)$month == "January", ]
## class: SummarizedExperiment
## dim: 1 3
## 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

## 4.3 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
## assays(1): counts
## rownames(74): ENSG00000131591 ENSG00000177757 ... ENSG00000272512
##   ENSG00000273443
## rowData names(0):
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(9): SampleName cell ... Sample BioSample

# 5 Advanced: Extending RangedSummarizedExperiment

For representing and manipulating data in their own package, Bioconductor developers are encouraged to re-use existing classes defined in other packages like the RangedSummarizedExperiment or GRanges containers defined in the SummarizedExperiment or GenomicRanges infrastructure packages, respectively. Many Bioconductor packages are designed around these basic containers, that is, they define functions that take and/or return a RangedSummarizedExperiment or GRanges object. For example the csaw package defines various functions that operate on RangedSummarizedExperiment objects, which are used to represent the number of ChIP-seq reads from each BAM file overlapping pre-specified regions.

However, sometimes re-using the RangedSummarizedExperiment class as-is does not satisfy the needs of the package and the developer makes the choice to extend the class in order to accomodate the special needs of the package and/or the specificities of the data that it deals with. For example the DESeq2 package defines the DESeqDataSet class which extends RangedSummarizedExperiment to add the design and dispersionFunction slots to it.

The following subsections describe in a nutshell how the developer would typically proceed for extending RangedSummarizedExperiment in his/her own package. Some familiarity with the S4 class system is required. Readers not familiar with the S4 class system are encouraged to consult the vignette A quick overview of the S4 class system located in the S4Vectors package for the basics of implementing and extending S4 classes.

The approach described below allows the developer to extend RangedSummarizedExperiment it in a way that remains agnostic of its internals. Keeping this separation between the responsibilities of the owners of the parent and child classes facilitate maintenance in the long run. In particular, the implementation of the child class won’t be affected by changes in the internals of the parent class.

## 5.1 Depend on, and import, the SummarizedExperiment package

Add SummarizedExperiment to the Depends field of the DESCRIPTION file of the package and the import(SummarizedExperiment) directive to its NAMESPACE file.

## 5.2 Define and export the RangedSummarizedExperiment subclass

Define the subclass with something like:

setClass("MyRSESubclass",
contains="RangedSummarizedExperiment",
representation=representation(
slot1="integer",
slot2="function"
## ... maybe more ...
)
)

Export it by adding the exportClasses(MyRSESubclass) directive to the NAMESPACE file.

## 5.3 Construct MyRSESubclass instances

When calling new() for constructing a MyRSESubclass instance, specify only the MyRSESubclass-specific slots: new("MyRSESubclass", rse, slot1=value1, slot2=value2), where rse is a RangedSummarizedExperiment object.

Providing a MyRSESubclass constructor function (named as the class itself) is recommended.

## 5.4 Define a validity method

The validity method for MyRSESubclass only needs to take care of what’s new in MyRSESubclass with respect to RangedSummarizedExperiment, that is, of the aspects of MyRSESubclass objects that are not already covered by the validity method for RangedSummarizedExperiment objects. This is because calling validObject() on a MyRSESubclass object automatically validates it as a RangedSummarizedExperiment object first and then calls the validity method for MyRSESubclass objects. In other words, validation works incrementally starting from the root of the class hierarchy and going in the parent-to-child direction.

## 5.5 Use the RangedSummarizedExperiment accessors on MyRSESubclass objects

Like any user of RangedSummarizedExperiment objects, the developer of MyRSESubclass should always use the RangedSummarizedExperiment accessors to access the RangedSummarizedExperiment-specific parts of his/her MyRSESubclass objects.

## 5.6 Use callNextMethod

In case some of the methods defined for RangedSummarizedExperiment objects need to be overwritten, the new methods should call callNextMethod internally.