SummarizedExperiment-class {SummarizedExperiment} | R Documentation

## SummarizedExperiment objects

### Description

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.

### Usage

    ## 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, ...)
    assay(x, i, ...) <- value
    rowData(x, ...)
    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=getRealizationBackend())

### Arguments

`x` | A SummarizedExperiment object.
`...` | For `assay`, `...` may contain `withDimnames`, which is forwarded to `assays`. For `rowData`, arguments passed thru `...` are forwarded to `mcols`. For `cbind`, `rbind`, `...` contains SummarizedExperiment objects to be combined. For other accessors, ignored.
`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 dimnames should be applied to extracted assay elements. Setting `withDimnames=FALSE` increases the speed and memory efficiency with which assays are extracted. `withDimnames=TRUE` in the getter `assays<-` allows efficient complex assignments (e.g., updating names of assays, `names(assays(x, withDimnames=FALSE)) = ...` is more efficient than `names(assays(x)) = ...`); it does not influence actual assignment of dimnames to assays.
`drop` | A `logical(1)`, ignored by these methods.
`value` | An object of a class specified in the S4 method signature or as outlined in ‘Details’.
`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.

### Details

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.

### Constructor

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

### Accessors

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 `i`th (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)`, `rowData(x) <- value`:

  Get or set the row data. `value` is a DataFrame object. Row names of `value` must be NULL or consistent with the existing row names of `x`.

`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.

### Subsetting

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`.

### Combining

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.

### Implementation and Extension

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 easily 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.

The current `assays` slot is implemented as a reference class that has copy-on-change semantics. This means that modifying non-assay slots does not copy the (large) assay data, and at the same time the user is not surprised by reference-based semantics. Updates to non-assay slots are very fast; updating the assays slot itself can be 5x or more faster than with an S4 instance in the slot. One useful technique when working with `assay` or `assays` function is use of the `withDimnames=FALSE` argument, which benefits speed and memory use by not copying dimnames from the row- and colData elements to each assay.

### Author(s)

Martin Morgan, mtmorgan@fhcrc.org

### See Also

- 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.

### Examples

    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
    ## ---------------------------------------------------------------------
    setRealizationBackend("HDF5Array")
    cmb3 <- realize(cmb2)
    assay(cmb3, withDimnames=FALSE)  # an HDF5Matrix object

[Package SummarizedExperiment version 1.10.1 Index]