Description Usage Arguments Details Value See Also Examples
Returns a matrix of data from a ClusterExperiment
object
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.
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 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75  ## 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()

object 
For 
reduceMethod 
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 builtin
method, in which case if it is not already existing in the object will be
passed to 
filterIgnoresUnassigned 
logical. Whether filtering statistics should
ignore the unassigned samples within the clustering. Only relevant if
'reduceMethod' matches one of builtin filtering statistics in

nDims 
The number of dimensions to keep from 
whichCluster 
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 
whichAssay 
numeric or character specifying which assay to use. See

returnValue 
The format of output. Users will generally want to keep the default (see details) 
reducedDimName 
The name given to the reducedDims slot storing result
(if 
typeToShow 
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. 
filterStats 
character vector of statistics to calculate. Must be one
of the character values given by 
transFun 
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

isCount 
if 
filterNames 
if given, defines the names that will be assigned to the
filtering statistics in the 
whichClusterIgnoreUnassigned 
indicates clustering that should be used
to filter out unassigned samples from the calculations. If 
cutoff 
numeric. A value at which to filter the rows (genes) for the test statistic 
percentile 
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 
absolute 
whether to take the absolute value of the filter statistic 
keepLarge 
logical whether to keep rows (genes) with large values of the test statistic or small values of the test statistic. 
reducedDims 
a vector of character values indicating the methods of dimensionality reduction to be performed. Currently only "PCA" is implemented. 
maxDims 
Numeric vector of integer giving the number of PC dimensions
to calculate. 
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 builtin
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
data.
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.
If 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 reduceMethod
argument
is a filtering method). The option "list" is mainly for internal use, where
we do not want to continually save subseted datasets.
If nDims
is missing, it will be given a default value
depending on the value of reduceMethod
. See
defaultNDims
for details.
If filterIgnoresUnassigned
is missing, then it is set to TRUE
unless: reduceMethod
matches a stored filtering statistic in
rowData
AND does not match a builtin filtering method provided by
the package.
For a 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.
@examples
se<SingleCellExperiment(matrix(rnorm(5000*100),nrow=5000,ncol=100))
defaultNDims(se,"PCA")
defaultNDims(se,"mad")
whichClusterIgnoreUnassigned
is only an option when applied
to a 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
form <filterStats>_<clusterLabel>
, i.e. the clustering label of the
clustering is appended to the standard name for the filtering statistic.
Note that filterData
returns a SingleCellExperiment object.
To get the actual data out use either assay or transformData
if transformed data is desired.
The PCA method uses either prcomp
from the stats
package or svds
from the RSpectra
package to perform PCA.
Both are called on t(assay(x))
with center=TRUE
and
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.
If returnValue="object"
, a ClusterExperiment
object.
If returnValue="list"
a list with elements:
objectUpdate
object, potentially updated if had to
calculate dimensionality reduction or filtering statistic
dataMatrix
the 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 typeToShow
(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 filterStats
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 SingleCellExperiment
containing the calculated dimensionality reduction in the reduceDims
with names corresponding to the name given in reducedDims
.
makeFilterStats
,makeReducedDims
,
filterData
, reducedDim
1 2 3 4 5 6 7 8 9 10 11  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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