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featureScore
implements different
methods to computes basis-specificity scores for each
feature in the data. The function extractFeatures
implements different
methods to select the most basis-specific features of
each basis component.
featureScore(object, ...) ## S3 method for class 'matrix':
featureScore(object,
method = c("kim", "max"))
extractFeatures(object, ...)
## S3 method for class 'matrix':
extractFeatures(object,
method = c("kim", "max"),
format = c("list", "combine", "subset"), nodups = TRUE)
Additionally for extractFeatures
, it may be an
integer vector that indicates the nu
format='combine'
.featureScore
returns a numeric vector of the
length the number of rows in object
(i.e. one
score per feature). extractFeatures
returns the selected features as a
list of indexes, a single integer vector or an object of
the same class as object
that only contains the
selected features.
In NMF models, samples are grouped according to the basis
components that contributes the most to each sample, i.e.
the basis components that have the greatest coefficient
in each column of the coefficient matrix (see
predict,NMF-method
). Each group of samples
is then characterised by a set of features selected based
on basis-specifity scores that are computed on the basis
matrix.
Carmona-Saez P, Pascual-Marqui RD, Tirado F, Carazo JM
and Pascual-Montano A (2006). "Biclustering of gene
expression data by Non-smooth Non-negative Matrix
Factorization." _BMC bioinformatics_, *7*, pp. 78. ISSN
1471-2105,
# roxygen generated flag
options(R_CHECK_RUNNING_EXAMPLES_=TRUE)
# random NMF model
x <- rnmf(3, 50,20)
# probably no feature is selected
extractFeatures(x)
# extract top 5 for each basis
extractFeatures(x, 5L)
# extract features that have a relative basis contribution above a threshold
extractFeatures(x, 0.5)
# ambiguity?
extractFeatures(x, 1) # means relative contribution above 100\%
extractFeatures(x, 1L) # means top contributing feature in each component
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