plotAllTwoSampleDistributionCounts: Compares the distribution of genes in clusters for every gene...
In pathVar: Methods to Find Pathways with Significantly Different Variability

Description Usage Arguments Details Value Author(s) Examples

Compares the distribution of genes for every gene in your data set for two groups of samples that you define and plot the counts of each gene.

1	plotAllTwoSampleDistributionCounts(dat,pvalue_results,perc=c(1/3,2/3),pvalue,plotName)

`dat`	`matrix` with the genes on the rows and the samples on the columns.
`pvalue_results`	output of `pathVarTwoSamples` step.
`perc`	numeric `vector` of probabilities with values between 0 and 1. Used to put genes into clusters
`pvalue`	the significance level to test.
`plotName`	a string specifying the file name of the output plot. Default value is NULL which prints, but does not save the graph in a pdf.

This function looks at the gene counts in each category of 2 groups previously defined in the pathVarTwoSamples step and compare the 2 samples to each other with all the genes from the data set. Then it plots the counts for each group.

A plot of results returned.

Laurence de Torrente, Samuel Zimmerman, Jessica Mar

# 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=pathVarTwoSamplesDisc(bock,pways.kegg.10pways,groups=as.factor(c(rep(1,10),rep(2,10))),perc=c(1/3,2/3),test="exact",varStat="sd")
plotAllTwoSampleDistributionCounts(bock, results_2samples, perc=c(1/3,2/3), pvalue=0.05, "bock.group1.group2.pdf")