Returns a matrix of data from a
based on the choices of dimensionality reduction given by the user.
Functions for calculating and manipulating either filtering statistics, stored in rowData, or the dimensionality reduction results, stored in reducedDims.
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## S4 method for signature 'ClusterExperiment' getReducedData( object, reduceMethod, filterIgnoresUnassigned, nDims = defaultNDims(object, reduceMethod), whichCluster = "primary", whichAssay = 1, returnValue = c("object", "list"), reducedDimName ) ## S4 method for signature 'SingleCellExperiment' defaultNDims(object, reduceMethod, typeToShow) ## S4 method for signature 'matrixOrHDF5' defaultNDims(object, ...) ## S4 method for signature 'SummarizedExperiment' makeFilterStats( object, filterStats = listBuiltInFilterStats(), transFun = NULL, isCount = FALSE, filterNames = NULL, whichAssay = 1 ) ## S4 method for signature 'matrixOrHDF5' makeFilterStats(object, ...) ## S4 method for signature 'ClusterExperiment' makeFilterStats( object, whichClusterIgnoreUnassigned = NULL, filterStats = listBuiltInFilterStats(), ... ) listBuiltInFilterStats() ## S4 method for signature 'SummarizedExperiment' filterData( object, filterStats, cutoff, percentile, absolute = FALSE, keepLarge = TRUE, whichAssay = 1 ) ## S4 method for signature 'SummarizedExperiment' filterNames(object) ## S4 method for signature 'SingleCellExperiment' makeReducedDims( object, reducedDims = "PCA", maxDims = 500, transFun = NULL, isCount = FALSE, whichAssay = 1 ) ## S4 method for signature 'matrixOrHDF5' makeReducedDims(object, ...) ## S4 method for signature 'SummarizedExperiment' makeReducedDims(object, ...) ## S4 method for signature 'ClusterExperiment' makeReducedDims(object, ...) listBuiltInReducedDims()
character. A method (or methods) for reducing the size of
the data, either by filtering the rows (genes) or by a dimensionality
reduction method. Must either be 1) must match the name of a built-in
method, in which case if it is not already existing in the object will be
logical. Whether filtering statistics should
ignore the unassigned samples within the clustering. Only relevant if
'reduceMethod' matches one of built-in filtering statistics in
The number of dimensions to keep from
argument that can be a single numeric or character value
indicating the single clustering to be used. Giving values that result in more than one clustering will result in an error. See details of
numeric or character specifying which assay to use. See
The format of output. Users will generally want to keep the default (see details)
The name given to the reducedDims slot storing result
character (optional). If given, should be one of "filterStats" or "reducedDims" to indicate of the values in the reduceMethod vector, only show those corresponding to "filterStats" or "reducedDims" options.
Values passed on the the 'SingleCellExperiment' method.
character vector of statistics to calculate. Must be one
of the character values given by
a transformation function to be applied to the data. If the
transformation applied to the data creates an error or NA values, then the
function will throw an error. If object is of class
if given, defines the names that will be assigned to the
filtering statistics in the
indicates clustering that should be used
to filter out unassigned samples from the calculations. If
numeric. A value at which to filter the rows (genes) for the test statistic
numeric. Either a number between 0,1 indicating what percentage of the rows (genes) to keep or an integer value indicated the number of rows (genes) to keep
whether to take the absolute value of the filter statistic
logical whether to keep rows (genes) with large values of the test statistic or small values of the test statistic.
a vector of character values indicating the methods of dimensionality reduction to be performed. Currently only "PCA" is implemented.
Numeric vector of integer giving the number of PC dimensions
getReducedData determines the matrix of values that can be used for
computation based on the user's choice of dimensionality methods. The
methods can be either of the filtering kind or the more general
dimensionality reduction. The function will first look at any stored
ReducedDims or filtering statistics already present in the data, and
if missing, will assume that
reduceMethod is one of the built-in
method provided by the package and calculate the necessary. Note that if
reduceMethod is a filtering statistic, in addition to filtering the
features, the function will also perform the stored transformation of the
Note that this is used internally by functions, but is mainly only of interest for the user if they want to have the filtered, transformed data available as a matrix for continual use.
returnValue="object", then the output is a single, updated
ClusterExperiment object with the reduced data matrix stored as an
element of the list in
reducedDims slot (with name given by
reducedDimName if given). If "list", then a list with one element
that is the object and the other that is the reduced data matrix. Either
way, the object returned in the list will be updated to contain with the
filtering statistics or the dimensionality reduction. The only difference
is that if "list", the reduced dimension matrix is NOT saved in the object
(and so only really makes a difference if the
is a filtering method). The option "list" is mainly for internal use, where
we do not want to continually save subseted datasets.
nDims is missing, it will be given a default value
depending on the value of
defaultNDims for details.
filterIgnoresUnassigned is missing, then it is set to TRUE
reduceMethod matches a stored filtering statistic in
rowData AND does not match a built-in filtering method provided by
reduceMethod that corresponds to a filtering statistics
the current default is 1000 (or the length of the number of features, if
less). For a dimensionality reduction saved in the reducedDims slot the
default is 50 or the maximum number of dimensions if less than 50.
reduceMethod will first be checked to see if it corresponds
with an existing saved filtering statistic or a dimensionality reduction to
determine which of these two types it is. If it does not match either, then
it will be checked against the built in functions provided by the package.
whichClusterIgnoreUnassigned is only an option when applied
ClusterExperiment classs and indicates that the filtering
statistics should be calculated based on samples that are unassigned by the
designated clustering. The name given to the filter in this case is of the
<filterStats>_<clusterLabel>, i.e. the clustering label of the
clustering is appended to the standard name for the filtering statistic.
filterData returns a SingleCellExperiment object.
To get the actual data out use either assay or
if transformed data is desired.
The PCA method uses either
prcomp from the
svds from the
RSpectra package to perform PCA.
Both are called on
scale=TRUE (i.e. the feature are centered and scaled), so that it is
performing PCA on the correlation matrix of the features.
Note that this function does not check if such a reduceDim value already exists, and will recalculate (and overwrite) if it does.
returnValue="list" a list with elements:
objectUpdateobject, potentially updated if had to
calculate dimensionality reduction or filtering statistic
dataMatrixthe reduced dimensional matrix with the samples in
columns, features in rows
defaultNDims returns a numeric vector giving the default
dimensions the methods in
clusterExperiment will use for reducing
the size of the data. If
typeToShow is missing, the resulting vector
will be equal to the length of
reduceMethod. Otherwise, it will be a
vector with all the unique valid default values for the
(note that different dimensionality reduction methods can have different
maximal dimensions, so the result may not be of length one in this case).
makeFilterStats returns a
SummarizedExperiment object with the
requested filtering statistics will be added to the
DataFrame in the
rowData slot and given names corresponding to the
values. Warning: the function will overwrite existing columns in
rowData with the same name. Columns in the
rowData slot with
different names should not be affected.
filterData returns a SingleCellExperiment object with the rows
(genes) removed based on filters
filterNames returns a vector of the columns of the rowData
that are considered valid filtering statistics. Currently any numeric
column in rowData is a valid filtering statistic.
makeReducedDims returns a
containing the calculated dimensionality reduction in the
with names corresponding to the name given in
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data(simData) listBuiltInFilterStats() scf<-makeFilterStats(simData,filterStats=c("var","mad")) scf scfFiltered<-filterData(scf,filterStats="mad",percentile=10) scfFiltered assay(scfFiltered)[1:10,1:10] data(simData) listBuiltInReducedDims() scf<-makeReducedDims(simData, reducedDims="PCA", maxDims=3) scf
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