SummarizedExperiment-class: SummarizedExperiment objects
In LiNk-NY/SummarizedExperiment: SummarizedExperiment container
Description
Usage
Arguments
Details
Constructor
Accessors
Subsetting
Combining
Implementation and Extension
Author(s)
See Also
Examples
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
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## 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, 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=getRealizationBackend())
Arguments
x
A SummarizedExperiment object.
...
For assay, ... may contain withDimnames, which is
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 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.
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.
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 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.
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 <- valueAccess or replace
column name in x.
x[[i, ...]], x[[i, ...]] <- valueAccess 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
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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
LiNk-NY/SummarizedExperiment documentation built on May 8, 2019, 11:20 p.m.
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Description Usage Arguments Details Constructor Accessors Subsetting Combining Implementation and Extension Author(s) See Also Examples
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ## 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, 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=getRealizationBackend())
|
Arguments
x |
A SummarizedExperiment object. |
... |
For For For other accessors, ignored. |
value |
An object of a class specified in the S4 method signature or as outlined in ‘Details’. |
i, j |
For For For |
name |
A symbol representing the name of a column of
|
withDimnames |
A |
use.names |
Like |
drop |
A |
deparse.level |
See |
subset |
An expression which, when evaluated in the
context of |
select |
An expression which, when evaluated in the
context of |
BACKEND |
|
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.
valueis alistorSimpleList, each element of which is a matrix with the same dimensions asx.assay(x, i),assay(x, i) <- value:A convenient alternative (to
assays(x)[[i]],assays(x)[[i]] <- value) to get or set theith (default first) assay element.valuemust be a matrix of the same dimension asx, and with dimension namesNULLor consistent with those ofx.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.
valueis a DataFrame object. colData(x),colData(x) <- value:Get or set the column data.
valueis a DataFrame object. Row names ofvaluemust be NULL or consistent with the existing column names ofx.metadata(x),metadata(x) <- value:Get or set the experiment data.
valueis alistwith 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.
valueis usually a list of length 2, containing elements that are eitherNULLor vectors of appropriate length for the corresponding dimension.valuecan beNULL, which removes dimension names. This method implies thatrownames,rownames<-,colnames, andcolnames<-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,jcan benumeric,logical,character, ormissing.valuemust be a SummarizedExperiment object with dimensions, dimension names, and assay elements consistent with the subsetx[i,j]being replaced.subset(x, subset, select):Create a subset of
xusing an expressionsubsetreferring to columns ofrowData(x)and / orselectreferring to column names ofcolData(x).
Additional subsetting accessors provide convenient access to
colData columns
x$name,x$name <- valueAccess or replace column
nameinx.x[[i, ...]],x[[i, ...]] <- valueAccess or replace column
iinx.
Combining
In the code snippets below, ... are SummarizedExperiment objects
to be combined.
cbind(...):-
cbindcombines objects with the same features of interest but different samples (columns inassays). The colnames incolData(SummarizedExperiment)must match or an error is thrown. Duplicate columns ofrowData(SummarizedExperiment)must contain the same data.Data in
assaysare combined by name matching; if all assay names are NULL matching is by position. A mixture of names and NULL throws an error.metadatafrom all objects are combined into alistwith no name checking. rbind(...):-
rbindcombines objects with the same samples but different features of interest (rows inassays). The colnames inrowData(SummarizedExperiment)must match or an error is thrown. Duplicate columns ofcolData(SummarizedExperiment)must contain the same data.Data in
assaysare combined by name matching; if all assay names are NULL matching is by position. A mixture of names and NULL throws an error.metadatafrom all objects are combined into alistwith 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
metadataandmcolsaccessors in the S4Vectors package.-
saveHDF5SummarizedExperimentandloadHDF5SummarizedExperimentin the HDF5Array package for saving/loading an HDF5-based SummarizedExperiment object to/from disk. The
realizegeneric function in the DelayedArray package for more information about on-disk realization of objects carrying delayed operations.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | 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
|
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