pathVarTwoSamplesCont: Compares the distribution of genes in each pathway for two...

In jmarlab/pathVar: Methods to Find Pathways with Significantly Different Variability

Description

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

Usage

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

Arguments

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".

Details

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.

Value

A geneDistributionSet2 object is returned.

Author(s)

Laurence de Torrente, Samuel Zimmerman, Jessica Mar

Examples

# 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")

jmarlab/pathVar documentation built on May 23, 2019, 9:02 p.m.