RunMEGSA: Running Mutually Exclusive Gene Set Analysis.

In lixiangchun/lxctk: Li Xiangchun's tool-kit (lxctk)

Description

Running Mutually Exclusive Gene Set Analysis.

Usage

RunMEGSA(mutationMat, maxSSimu=NULL, resultTableFile="resultMEGS.txt", figureDir="figure", nSimu=1000, nPairStart=10, maxSize=6, level =0.05, detail = FALSE, type="pdf", maxSSimuRdataFile='maxSSimu.RData')
funMEGSA(mutationMatFile, maxSSimuFile = NULL, resultTableFile = "resultMEGS.txt", figureDir = "figure", nSimu = 1000, nPairStart = 10, maxSize = 6, level = 0.05, detail = FALSE, type = "pdf")

Arguments

mutationMat	Binary (1: mutation, 0: no mutation) mutation matrix. The first row has the gene names and the first column has the patient IDs..
maxSSimu	A data frame loading from simulation file.
mutationMatFile	Binary (1: mutation, 0: no mutation) mutation matrix file.
maxSSimuFile	Simulation file.
resultTableFile	Output file.
figureDir	Output figure directory.
nSimu	Number of simulations (recommand 1000 or more, it may take ~ 10 hours for 1000 simulations).
nPairStart	We first tested all pairs of genes and then pick up the top nPairStart gene pairs (ranked by P-values) to perform multiple-path search to include more genes. Increasing nPairStart will slightly increase power but linearly increasing the computational time. Recommended nPairStart: (10, 30).
maxSize	The maximum size of putative MEGS..
level	Significant level, 0.05 by default.
detail	If TRUE, output detail info during simulation.
type	Figure format, can be either pdf, png and jpg.
maxSSimuRdataFile	The file name that maxSSimu will be saved.

Details

See the following example for more info about inputs.

Source

1. Xing Hua et al. MEGSA: A Powerful and Flexible Framework for Analyzing Mutual Exclusivity of Tumor Mutations AJHG 2016.

See Also

...

Examples

# If multi-cores available, use
library(parallel)
options(mc.cores=detectCores())
# or
options(mc.cores=min(detectCores(), 8))

RunMEGSA(...)

lixiangchun/lxctk documentation built on May 21, 2019, 6:44 a.m.