Description Usage Arguments Value Note Author(s) References Examples
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 diffusionKernel (i.e.
colnames(diffusionKernel)
or
rownames(diffusionKernel)
). 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.
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%. |
mapping |
a mapping that defines how probe sets are summarized to genes. |
diffusionKernel |
the diffusion kernel which was
pre-computed by using the function
|
useOrigMethod |
use the method originally decribed in the paper by Franck Rapaport et al. 2007 |
a graphSVM object
features |
the selected features |
error.bound |
the span bound of the model |
fit |
the fitted SVM model |
We combined the original method with a Recursive Feature Elimination in order to allow a feature selection. 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
Rapaport F. et al. (2007). Classification of microarray data using gene networks. BMC Bioinformatics
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ## Not run:
library(Biobase)
data(sample.ExpressionSet)
x <- t(exprs(sample.ExpressionSet))
y <- factor(pData(sample.ExpressionSet)$sex)
# create the mapping
library('hgu95av2.db')
mapped.probes <- mappedkeys(hgu95av2REFSEQ)
refseq <- as.list(hgu95av2REFSEQ[mapped.probes])
times <- sapply(refseq, length)
mapping <- data.frame(probesetID=rep(names(refseq), times=times), graphID=unlist(refseq),
row.names=NULL, stringsAsFactors=FALSE)
mapping <- unique(mapping)
library(pathClass)
data(adjacency.matrix)
matched <- matchMatrices(x=x, adjacency=adjacency.matrix, mapping=mapping)
dk <- calc.diffusionKernel(L=matched$adjacency, is.adjacency=TRUE, beta=0) # beta should be tuned
res.gSVM <- crossval(matched$x, y, theta.fit=fit.graph.svm, folds=5, repeats=2, DEBUG=TRUE,
parallel=FALSE, Cs=10^(-3:3), mapping=matched$mapping, diffusionKernel=dk)
## End(Not run)
|
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