Description Usage Arguments Value Note Author(s) References Examples
Implementation of the Reweighted Recursive Feature
Elimination (RRFE) algorithm. mapping
must be a
data.frame with at least two columns. The column names
have to be c('probesetID','graphID')
. Where
'probesetID' is the probeset ID present in the expression
matrix (i.e. colnames(x)
) and 'graphID' is any ID
that represents the nodes in the graph (i.e.
colnames(Gsub)
or rownames(Gsub)
). The
purpose of the this mapping is that a gene or protein in
the network might be represented by more than one probe
set on the chip. Therefore, the algorithm must know which
genes/protein in the network belongs to which probeset on
the chip. However, the method is able to use all feature
when one sets the parameter useAllFeatures
to
TRUE
. When doing so, RRFE assigns the minimal
wheight returned by GeneRank to those genes which are not
present in Gsub
.
1 2 3 4 |
x |
a p x n matrix of expression measurements with p samples and n genes. |
y |
a factor of length p comprising the class labels. |
DEBUG |
should debugging information be plotted. |
scale |
a character vector defining if the data
should be centered and/or scaled. Possible values are
center and/or scale. Defaults to
|
Cs |
soft-margin tuning parameter of the SVM.
Defaults to |
stepsize |
amount of features that are discarded in each step of the feature elimination. Defaults to 10%. |
useAllFeatures |
should all features be used for
classification (see also |
mapping |
a mapping that defines how probe sets are summarized to genes. |
Gsub |
an adjacency matrix that represents the underlying biological network. |
d |
the damping factor which controls the influence of the network data and the fold change on the ranking of the genes. Defaults to 0.5 |
a RRFE fit object.
features |
the selected features |
error.bound |
the span bound of the model |
fit |
the fitted SVM model |
The optimal number of features is found by using the span estimate. See Chapelle, O., Vapnik, V., Bousquet, O., and Mukherjee, S. (2002). Choosing multiple parameters for support vector machines. Machine Learning, 46(1), 131-159.
Marc Johannes JohannesMarc@gmail.com
Johannes M, et al. (2010). Integration Of Pathway Knowledge Into A Reweighted Recursive Feature Elimination Approach For Risk Stratification Of Cancer Patients. Bioinformatics
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ## Not run:
library(Biobase)
data(sample.ExpressionSet)
x <- t(exprs(sample.ExpressionSet))
y <- factor(pData(sample.ExpressionSet)$sex)
# create the mapping
library('hgu95av2.db')
mapped.probes <- mappedkeys(hgu95av2REFSEQ)
refseq <- as.list(hgu95av2REFSEQ[mapped.probes])
times <- sapply(refseq, length)
mapping <- data.frame(probesetID=rep(names(refseq), times=times), graphID=unlist(refseq),
row.names=NULL, stringsAsFactors=FALSE)
mapping <- unique(mapping)
library(pathClass)
data(adjacency.matrix)
res.rrfe <- crossval(x, y, DEBUG=TRUE, theta.fit=fit.rrfe, folds=3, repeats=1, parallel=TRUE,
Cs=10^(-3:3), mapping=mapping, Gsub=adjacency.matrix, d=1/2)
## End(Not run)
|
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