fit.graph.svm: Implementation of a supervised classification framework...

In pathClass: Classification using biological pathways as prior knowledge

Description

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.

Usage

  fit.graph.svm(x, y, DEBUG = FALSE,
    scale = c("center", "scale"), Cs = 10^c(-3:3),
    stepsize = 0.1, mapping, diffusionKernel,
    useOrigMethod = FALSE)

Arguments

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 c('center', 'scale').
Cs	soft-margin tuning parameter of the SVM. Defaults to 10^c(-3:3).
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 calc.diffusionKernel
useOrigMethod	use the method originally decribed in the paper by Franck Rapaport et al. 2007

Value

a graphSVM object

features	the selected features
error.bound	the span bound of the model
fit	the fitted SVM model

Note

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.

Author(s)

Marc Johannes [e-mail]

References

Rapaport F. et al. (2007). Classification of microarray data using gene networks. BMC Bioinformatics

Examples

## 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)

pathClass documentation built on May 29, 2017, 11:44 p.m.