fit.networkBasedSVM: Implementation of the network-based Support Vector Machine...
In pathClass: Classification using biological pathways as prior knowledge

Description Usage Arguments Value 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.

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  fit.networkBasedSVM(exps, y, DEBUG = FALSE, n.inner = 3,
    scale = c("center", "scale"), sd.cutoff = 1,
    lambdas = 10^(-2:4), adjacencyList)

`exps`	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.
`n.inner`	number of fold for the inner cross-validation.
`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')`.
`sd.cutoff`	a cutoff on the standard deviation (sd) of genes. Only genes with sd > sd.cutoff stay in the analysis.
`lambdas`	a set of values for lambda regularization parameter of the L_∞-Norm. Which, if properly chosen, eliminates factors that are completely irrelevant to the response, what in turn leads to a factor-wise (subnetwork-wise) feature selection. The 'best' lambda is found by an inner-cross validation.
`adjacencyList`	a adjacency list representing the network structure. The list can be generated from a adjacency matrix by using the function `as.adjacencyList`

a networkBasedSVM object containing

`features`	the selected features
`lambda.performance`	overview how different values of lambda performed in the inner cross validation
`fit`	the fitted network based SVM model

Marc Johannes JohannesMarc@gmail.com

Zhu Y. et al. (2009). Network-based support vector machine for classification of microarray samples. BMC Bioinformatics

## Not run: 
library(Biobase)
data(sample.ExpressionSet)
x <- t(exprs(sample.ExpressionSet))
y <- factor(pData(sample.ExpressionSet)$sex)
# create the mapping
library('hgu95av2.db')
mapped.probes <- mappedkeys(hgu95av2REFSEQ)
refseq <- as.list(hgu95av2REFSEQ[mapped.probes])
times <- sapply(refseq, length)
mapping <- data.frame(probesetID=rep(names(refseq), times=times), graphID=unlist(refseq), 
row.names=NULL, stringsAsFactors=FALSE)
mapping <- unique(mapping)
library(pathClass)
data(adjacency.matrix)
matched <- matchMatrices(x=x, adjacency=adjacency.matrix, mapping=mapping)
ad.list <- as.adjacencyList(matched$adjacency)
res.nBSVM <- crossval(matched$x, y, theta.fit=fit.networkBasedSVM, folds=3, repeats=1, DEBUG=TRUE,
parallel=FALSE, adjacencyList=ad.list, lambdas=10^(-1:2), sd.cutoff=50)

## End(Not run)