ISAModules object stores the results of one ISA run. It
contains a set of biclusters (=modules or transcription modules) and
some meta information about the ISA run and the input data.
"dim"(x)
"featureNames"(modules)
"sampleNames"(modules)
"annotation"(modules)
"getOrganism"(modules)
"pData"(modules)
"seedData"(modules)
"runData"(modules)
"featureThreshold"(modules, mods)
"sampleThreshold"(modules, mods)
"length"(x)
"getNoFeatures"(modules, mods)
"getNoSamples"(modules, mods)
"getFeatures"(modules, mods)
"getSamples"(modules, mods)
"getFeatureNames"(modules, mods)
"getSampleNames"(modules, mods)
"getFeatureScores"(modules, mods)
"getSampleScores"(modules, mods)
"getFeatureMatrix"(modules, binary = FALSE, sparse = FALSE, mods)
"getSampleMatrix"(modules, binary = FALSE, sparse = FALSE, mods)
"getFullFeatureMatrix"(modules, eset, mods)
"getFullSampleMatrix"(modules, eset, mods)
"["(x, i, j, ..., drop = FALSE)
"[["(x, i, j, ..., drop = FALSE)ISAModules object.Matrix package is required for sparse matrices.ExpressionSet or ISAExpressionSet
object. This is needed for calculating the scores of the
features/samples that are not in the module.
If an ExpressionSet object is supplied, then it is
normalised by calling ISANormalize on it.[’ an index vector for selecting features
(=probes, genes). For ‘[[’ an index vector for
selecting modules.[’ an index vector for selecting
samples. It is ignored for ‘[[’.dim returns a numeric vector of length two.
featureNames and sampleNames return a character vector.
annotation and getOrganism return a character vector of length one.
pData returns a data frame.seedData returns a data frame, see more below.
runData returns a named list, see more below.
featureThreshold and sampleThreshold return a numeric
vector.length returns a numeric scalar.
getNoFeatures and getNoSamples return a numeric vector.getFeatures and getSamples return a list of named
numeric vectors. getFeatureNames and getSampleNames
return a list of character vectors. getFeatureScores and
getSampleScores return a list of named numeric
vectors. getFeatureMatrix, getSampleMatrix,
getFullFeatureMatrix and getFullSampleMatrix return a
numeric matrix.
dim returns the dimension of the input expression matrix,
number of features times number of samples. featureNames returns a character vector, the names of the
features in the original input matrix. I.e. in the input was an
ExpressionSet for an Affymetrix array, then the Affymetrix
probe IDs are returned. sampleNames returns a character vector, the names of the
samples in the original expression set. annotation returns a character scalar, the name of the array
for the input expression set. More precisely, the annotation
slot of the input ExpressionSet is returned, this is the name
of the annotation package to use for the ExpressionSet. getOrganism returns the scientific name of the organism for
which the input expression data was measures. This is obtained by
loading the annotation package of the input ExpressionSet
object, so that must be installed. pData returns the phenotypic data attached to the input
ExpressionSet object, in a data frame, samples as rows and
various phenotypic variables as columns.seedData returns information about the modules. Each row of the
returned data frame corresponds to one module, the columns are various
variables:
ISAUnique was performed.ISARobustness for details.runData returns information about the ISA runs, it is a named
list with elements:
direction parameter of the ISA. Please
see ISAIterate for details.ISAIterate for
details.cor”
convergence criterium, see ISAIterate for
details.eps” convergence
criterium, see ISAIterate for details.corx”
convergence criterium, see ISAIterate for
details.ISAUnique was
run on the modules.ISANormalize.NA or NaN values.rob.perms is
only present if ISAFilterRobust was performed. featureThreshold returns the feature thresholds that were used
to find the modules. sampleThreshold returns the sample thresholds that were used to
find the modules.length returns the number of modules. getNoFeatures returns the number of features (=genes) in the
input data. The number of features after filtering is returned
if the input data was filtered. getNoSamples returns the number of samples (=conditions) in the
input data.getFeatures returns the indices of the features included in the
modules. It returns a list, with one entry for each module. Each entry
contains the indices of the features (=genes) in the corresponding
module. getSamples does the same as getFeatures, but for samples. getFeatureNames is similar to getFeatures, but returns
feature names instead of feature indices. getSampleNames is similar to getSamples, but returns
sample names instead of sample indices. getFeatureScores returns the feature scores for the selected
modules (all modules by default). It returns a list, with one entry
for each module. Each list entry contains the feature scores for one
module, in a named numeric vector. getSampleScores is similar to getFeatureScores, but for
samples and sample scores. getFeatureMatrix returns feature scores for the specified
modules (all modules by default) in a matrix form. The number of rows
is the number of features and the number of columns is the number of
modules requested. It can optionally binarize the values. getSampleMatrix is similar to getFeatureMatrix, but for
sample scores. getFullFeatureMatrix is similar to getFeatureMatrix, but
is also calculates scores for the features that were not included in
the module. For this it performs one ISA iteration and omits the
thresholding step. You need to supply the normalized (or the original)
expression data to make this possible. getFullSampleMatrix is the same as getFullFeatureMatrix,
but for sample scores.ISAModules object. The ‘[[’ double bracket indexing operator can be used
with a single index vector to select a subset of modules. The ‘[’ single bracket indexing operator can be used to
restrict an ISAModules object to a subset of features and/or
samples. The first index corresponds to features, the second to
samples. Indices can be numeric, logical or character vectors, for the
latter feature and sample names are used.ISAModules object contains a set of biclusters, obtained
using one run of the Iterative Signature Algorithm.Various operations are defined such an object, here we document all of them, in several groups.
eisa package.
data(ALLModulesSmall)
ALLModulesSmall
length(ALLModulesSmall)
dim(ALLModulesSmall)
annotation(ALLModulesSmall)
getOrganism(ALLModulesSmall)
seedData(ALLModulesSmall)
getNoFeatures(ALLModulesSmall)
getNoSamples(ALLModulesSmall)
getFeatureScores(ALLModulesSmall, 1)[[1]]
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