pathVarOneSample: Compares the distribution of genes in each cluster to the...

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

Description

Compares the distribution of genes in each cluster to the distribution of genes in each cluster for every pathway.

Usage

pathVarOneSample(dat.mat, pways,test=c("chisq", "exact"),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.
test	a string specifying the type of significance test to perform. The options are "exact" or "chisq".
varStat	a string specifying the type of variability summary statistic to perform. The options are "sd", "mean", "mad", or "cv".

Details

This function classifies your genes into one to four clusters with respect to the standard deviation (SD), median absolute deviation (MAD), coefficient of variation (CV) or mean. Then, it compares the counts of genes in each class from your dataset in one pathway with the counts of the genes in each class from the whole dataset. For that, it uses a Chi-square or an exact 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 geneDistributionSet object is returned.

Author(s)

Laurence de Torrente, Samuel Zimmerman, Jessica Mar

Examples

results_kegg=pathVarOneSample(bock,pways.kegg,test="chisq",varStat="sd")

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