computePageRanks: Compute the pageRanks for the genes.

In bhatturam/prius: Affected pathway ranking in differential gene expression analysis

Description

This function computes the pageRanks for the genes based on the input personalization vectors constructed from experimental data.

Usage

computePageRanks(personalizationVectors, PPIGraph, alpha)

Arguments

personalizationVectors	A data frame with two columns npv - normal personalization vector and dpv - disease personalization vector. There are as many rows as number of HGNC Symbols in experimentData
PPIGraph	PPIGraph PPIGraph A list containing igraph_object - the igraph object and vertex_map - a frame containing node attributes currently indegree and outdegree. The output of createIGraphObject
alpha	The damping factor

Value

A data frame with two columns containing the normal page rank and the disease page rank. There are as many rows as number of HGNC Symbols in PPI Graph

Examples

## Not run: 
gds = getGEO('GDS3837',AnnotGPL = TRUE, getGPL = TRUE,destdir = "/tmp")
gpl = getGEO(Meta(gds)$platform,destdir = "/tmp")
eset = GDS2eSet(gds, GPL=gpl,do.log2=FALSE)
numPairs = dim(pData(eset))[1]/2
expressionDataGEO=importExpressionDataGEO(eset,gpl,defaultProbeSelector,probeCombinerMean)
experimentDataGEO=runTestOnData(expressionDataGEO,1:60,61:120,logPairedTTestFunction)
personalizationVectorsGEO=computePersonalizationVectors(experimentDataGEO,reactomePPIGraph,data.frame(foldChange=1,pValue=1),outdegreeNormalizedFCOneMinusPScoreFunction)
pageRanksGEO=computePageRanks(personalizationVectorsGEO,reactomePPIGraph,0.7)

## End(Not run)

bhatturam/prius documentation built on May 12, 2019, 8:24 p.m.