pathVarTwoSamplesCont: Compares the distribution of genes in each pathway for two...
In pathVar: Methods to Find Pathways with Significantly Different Variability

Description Usage Arguments Details Value Author(s) Examples

Compares the distribution of genes in each pathway for two groups of samples that you define.

1	pathVarTwoSamplesCont(dat.mat, pways,groups,boot=1000,varStat=c("sd","mean", "mad", "cv"))

`dat.mat`	`matrix` with the genes on the rows and the samples on the columns.
`pways`	`list` which contains a `vector` of pathway IDs, a `vector` of pathway names, and a `list` of genes in each pathway.
`groups`	`vector` indicating the amount of samples and replicates of each sample.
`boot`	number of bootstraps to be performed.
`varStat`	a string specifying the type of variability summary statistic to perform. The options are "sd", "mean", "mad", or "cv".

This function splits the samples into two groups that you define. It compares the density of the variability (SD, MAD, CV) or of the mean of the genes in a pathway from group 1 with the density from group 2. For that, it uses the bootstrap Kolmogorov-smirnov test. You can give your own list of pathways (using the output of makeDBList) or use Reactome and KEGG pathways that are already included.

A geneDistributionSet2 object is returned.

Laurence de Torrente, Samuel Zimmerman, Jessica Mar

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# we run the 2 samples analysis on the first 10 pathways from kegg
pways.kegg.10pways <- lapply(pways.kegg, function(x) x[1:10])
results_2samples=pathVarTwoSamplesCont(bock,pways.kegg.10pways,groups=as.factor(c(rep(1,10),rep(2,10))),boot=1000,varStat="sd")