CausalR (version 1.4.3)

runSCANR: run ScanR

Description

This function will return nodes regulated by the given hypothesisGene

Usage

runSCANR(network, experimentalData, NumberOfDeltaToScan = 5, topNumGenes = 150, correctPredictionsThreshold = 1, doParallel = FALSE, numCores = NULL)

Arguments

network

Computational Causal Graph, as an igraph.

experimentalData

The experimental data read in using ReadExperimentalData. The results is an n x 2 matrix; where the first column contains the node ids of the nodes in the network that the results refer to. The second column contains values indicating the direction of regulation in the results - (+)1 for up, -1 for down and 0 for insignificant amounts of regulation.

NumberOfDeltaToScan

Iteratively scan for 1 to NumberOfDeltaToScan delta values

topNumGenes

A value to select top genes to report (typically top 100 genes)

correctPredictionsThreshold

Minimal score for p-values calculation. Hypotheses with scores below this value will get NAs for p-value and enrichment p-value. The usual default is -inf within the RankTheHypotheses function, where it is employed.

doParallel

A flag for running RankTheHypothesis in parallel mode.

numCores

Number of cores to use if using parallel mode. If the default value of NULL is used, it will attempt to detect the number of cores available and use all of them bar one.

Value

returns list of genes from each delta scan run

Examples

NumberOfDeltaToScan <- 2
topNumGenes <- 4
#get path to example network file
network <- system.file(package = 'CausalR', 'extdata', 'testNetwork.sif')
#create ccg
ccg <-  CreateCCG(network)
#get path to example experimental data
fileName<- system.file(package = 'CausalR', 'extdata', 'testData.txt')
#read in experimetal data
expData <-  ReadExperimentalData(fileName, ccg)
#run in single threaded mode
runSCANR(ccg, expData, NumberOfDeltaToScan, topNumGenes)
#run in parallel mode
runSCANR(ccg, expData, NumberOfDeltaToScan, topNumGenes,
         doParallel = TRUE, numCores = 2)