SummarizedExperiment-class: SummarizedExperiment objects
In SummarizedExperiment: SummarizedExperiment container

Description Usage Arguments Details Constructor Accessors Subsetting Combining Implementation and Extension Author(s) See Also Examples

The SummarizedExperiment class is a matrix-like container where rows represent features of interest (e.g. genes, transcripts, exons, etc...) and columns represent samples (with sample data summarized as a DataFrame). A SummarizedExperiment object contains one or more assays, each represented by a matrix-like object of numeric or other mode.

Note that SummarizedExperiment is the parent of the RangedSummarizedExperiment class which means that all the methods documented below also work on a RangedSummarizedExperiment object.

## Constructor

# See ?RangedSummarizedExperiment for the constructor function.

## Accessors

assayNames(x, ...)
assayNames(x, ...) <- value
assays(x, withDimnames=TRUE, ...)
assays(x, withDimnames=TRUE, ...) <- value
assay(x, i, withDimnames=TRUE, ...)
assay(x, i, withDimnames=TRUE, ...) <- value
rowData(x, use.names=TRUE, ...)
rowData(x, ...) <- value
colData(x, ...)
colData(x, ...) <- value
#dim(x)
#dimnames(x)
#dimnames(x) <- value

## Quick colData access

## S4 method for signature 'SummarizedExperiment'
x$name
## S4 replacement method for signature 'SummarizedExperiment'
x$name <- value
## S4 method for signature 'SummarizedExperiment,ANY,missing'
x[[i, j, ...]]
## S4 replacement method for signature 'SummarizedExperiment,ANY,missing'
x[[i, j, ...]] <- value

## Subsetting

## S4 method for signature 'SummarizedExperiment'
x[i, j, ..., drop=TRUE]
## S4 replacement method for signature 'SummarizedExperiment,ANY,ANY,SummarizedExperiment'
x[i, j] <- value
## S4 method for signature 'SummarizedExperiment'
subset(x, subset, select, ...)

## Combining

## S4 method for signature 'SummarizedExperiment'
cbind(..., deparse.level=1)
## S4 method for signature 'SummarizedExperiment'
rbind(..., deparse.level=1)

## On-disk realization
## S4 method for signature 'SummarizedExperiment'
realize(x, BACKEND=getAutoRealizationBackend())

`x`	A SummarizedExperiment object.
`...`	For `assay`, arguments in `...` are forwarded to `assays`. For `cbind`, `rbind`, `...` contains SummarizedExperiment objects to be combined. For other accessors, ignored.
`value`	An object of a class specified in the S4 method signature or as outlined in ‘Details’.
`i, j`	For `assay`, `assay<-`, `i` is an integer or numeric scalar; see ‘Details’ for additional constraints. For `[,SummarizedExperiment`, `[,SummarizedExperiment<-`, `i`, `j` are subscripts that can act to subset the rows and columns of `x`, that is the `matrix` elements of `assays`. For `[[,SummarizedExperiment`, `[[<-,SummarizedExperiment`, `i` is a scalar index (e.g., `character(1)` or `integer(1)`) into a column of `colData`.
`name`	A symbol representing the name of a column of `colData`.
`withDimnames`	A `logical(1)`, indicating whether the dimnames of the SummarizedExperiment object should be applied (i.e. copied) to the extracted assays. More precisely, setting `withDimnames=FALSE` in the getter returns the assays as-is whereas setting `withDimnames=FALSE` return them with possibly modified dimnames. Setting `withDimnames=FALSE` in the setter (`assays<-`) is required when the dimnames on the supplied assays are not identical to the dimnames on the SummarizedExperiment object; it does not influence actual assignment of dimnames to assays (they're always stored as-is). Note that assays(x, withDimnames=FALSE) <- assays(x, withDimnames=FALSE) is guaranteed to always work and be a no-op. This is not the case if `withDimnames=TRUE` is used or if `withDimnames` is not specified.
`use.names`	Like `mcols(x)`, by default `rowData(x)` propagates the rownames of `x` to the returned DataFrame object (note that for a SummarizedExperiment object, the rownames are also the names i.e. `rownames(x)` is always the same as `names(x)`). Setting `use.names=FALSE` suppresses this propagation i.e. it returns a DataFrame object with no rownames. Use this when `rowData(x)` fails, which can happen when the rownames contain NAs (because the rownames of a SummarizedExperiment object can contain NAs, but the rownames of a DataFrame object cannot).
`drop`	A `logical(1)`, ignored by these methods.
`deparse.level`	See `?base::cbind` for a description of this argument.
`subset`	An expression which, when evaluated in the context of `rowData(x)`, is a logical vector indicating elements or rows to keep: missing values are taken as false.
`select`	An expression which, when evaluated in the context of `colData(x)`, is a logical vector indicating elements or rows to keep: missing values are taken as false.
`BACKEND`	`NULL` (the default), or a single string specifying the name of the backend. When the backend is set to `NULL`, each element of `assays(x)` is realized in memory as an ordinary array by just calling `as.array` on it.

The SummarizedExperiment class is meant for numeric and other data types derived from a sequencing experiment. The structure is rectangular like a matrix, but with additional annotations on the rows and columns, and with the possibility to manage several assays simultaneously.

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. The DataFrame must have as many rows as there are rows in the SummarizedExperiment object, with each row of the DataFrame providing 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.

Each column of a SummarizedExperiment object represents a sample. Information about the samples are stored in a DataFrame, accessible using the function colData, described below. The DataFrame must have as many rows as there are columns in the SummarizedExperiment object, with each row of the DataFrame providing information on the sample in the corresponding column of the SummarizedExperiment object. Columns of the DataFrame represent different sample attributes, e.g., tissue of origin, etc. Columns of the DataFrame can themselves be annotated (via the mcols function). Column names typically provide a short identifier unique to each sample.

A SummarizedExperiment object can also contain information about the overall experiment, for instance the lab in which it was conducted, the publications with which it is associated, etc. This information is stored as a list object, accessible using the metadata function. The form of the data associated with the experiment is left to the discretion of the user.

The SummarizedExperiment container is appropriate for matrix-like data. The data are accessed using the assays function, described below. This returns a SimpleList object. Each element of the list must itself be a matrix (of any mode) and must have dimensions that are the same as the dimensions of the SummarizedExperiment in which they are stored. Row and column names of each matrix must either be NULL or match those of the SummarizedExperiment during construction. It is convenient for the elements of SimpleList of assays to be named.

SummarizedExperiment instances are constructed using the SummarizedExperiment function documented in ?RangedSummarizedExperiment.

In the following code snippets, x is a SummarizedExperiment object.

assays(x), assays(x) <- value:: Get or set the assays. value is a list or SimpleList, each element of which is a matrix with the same dimensions as x.
assay(x, i), assay(x, i) <- value:: A convenient alternative (to assays(x)[[i]], assays(x)[[i]] <- value) to get or set the ith (default first) assay element. value must be a matrix of the same dimension as x, and with dimension names NULL or consistent with those of x.
assayNames(x), assayNames(x) <- value:: Get or set the names of assay() elements.
rowData(x, use.names=TRUE), rowData(x) <- value:: Get or set the row data. value is a DataFrame object.
colData(x), colData(x) <- value:: Get or set the column data. value is a DataFrame object. Row names of value must be NULL or consistent with the existing column names of x.
metadata(x), metadata(x) <- value:: Get or set the experiment data. value is a list with arbitrary content.
dim(x):: Get the dimensions (features of interest x samples) of the SummarizedExperiment.
dimnames(x), dimnames(x) <- value:: Get or set the dimension names. value is usually a list of length 2, containing elements that are either NULL or vectors of appropriate length for the corresponding dimension. value can be NULL, which removes dimension names. This method implies that rownames, rownames<-, colnames, and colnames<- are all available.

In the code snippets below, x is a SummarizedExperiment object.

x[i,j], x[i,j] <- value:: Create or replace a subset of x. i, j can be numeric, logical, character, or missing. value must be a SummarizedExperiment object with dimensions, dimension names, and assay elements consistent with the subset x[i,j] being replaced.
subset(x, subset, select):: Create a subset of x using an expression subset referring to columns of rowData(x) and / or select referring to column names of colData(x).

Additional subsetting accessors provide convenient access to colData columns

x$name, x$name <- value: Access or replace column name in x.
x[[i, ...]], x[[i, ...]] <- value: Access or replace column i in x.

In the code snippets below, ... are SummarizedExperiment objects to be combined.

cbind(...):

cbind combines objects with the same features of interest but different samples (columns in assays). The colnames in colData(SummarizedExperiment) must match or an error is thrown. Duplicate columns of rowData(SummarizedExperiment) must contain the same data.

Data in assays are combined by name matching; if all assay names are NULL matching is by position. A mixture of names and NULL throws an error.

metadata from all objects are combined into a list with no name checking.

rbind(...):

rbind combines objects with the same samples but different features of interest (rows in assays). The colnames in rowData(SummarizedExperiment) must match or an error is thrown. Duplicate columns of colData(SummarizedExperiment) must contain the same data.

Data in assays are combined by name matching; if all assay names are NULL matching is by position. A mixture of names and NULL throws an error.

metadata from all objects are combined into a list with no name checking.

This section contains advanced material meant for package developers.

SummarizedExperiment is implemented as an S4 class, and can be extended in the usual way, using contains="SummarizedExperiment" in the new class definition.

In addition, the representation of the assays slot of SummarizedExperiment is as a virtual class Assays. This allows derived classes (contains="Assays") to implement alternative requirements for the assays, e.g., backed by file-based storage like NetCDF or the ff package, while re-using the existing SummarizedExperiment class without modification. See Assays for more information.

Martin Morgan, mtmorgan@fhcrc.org

RangedSummarizedExperiment objects.
DataFrame, SimpleList, and Annotated objects in the S4Vectors package.
The metadata and mcols accessors in the S4Vectors package.
saveHDF5SummarizedExperiment and loadHDF5SummarizedExperiment in the HDF5Array package for saving/loading an HDF5-based SummarizedExperiment object to/from disk.
The realize generic function in the DelayedArray package for more information about on-disk realization of objects carrying delayed operations.

nrows <- 200; ncols <- 6
counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
                     row.names=LETTERS[1:6])
se0 <- SummarizedExperiment(assays=SimpleList(counts=counts),
                            colData=colData)
se0
dim(se0)
dimnames(se0)
assayNames(se0)
head(assay(se0))
assays(se0) <- endoapply(assays(se0), asinh)
head(assay(se0))

rowData(se0)
colData(se0)

se0[, se0$Treatment == "ChIP"]
subset(se0, select = Treatment == "ChIP")

## cbind() combines objects with the same features of interest
## but different samples:
se1 <- se0
se2 <- se1[,1:3]
colnames(se2) <- letters[seq_len(ncol(se2))]
cmb1 <- cbind(se1, se2)
dim(cmb1)
dimnames(cmb1)

## rbind() combines objects with the same samples but different
## features of interest:
se1 <- se0
se2 <- se1[1:50,]
rownames(se2) <- letters[seq_len(nrow(se2))]
cmb2 <- rbind(se1, se2)
dim(cmb2)
dimnames(cmb2)

## ---------------------------------------------------------------------
## ON-DISK REALIZATION
## ---------------------------------------------------------------------
library(DelayedArray)
setAutoRealizationBackend("HDF5Array")
cmb3 <- realize(cmb2)
assay(cmb3, withDimnames=FALSE)  # an HDF5Matrix object