mutsigclfn: Permutation-based identification of Significantly Mutated...

Description Usage Arguments Details See Also Examples

View source: R/mutsigclfn.R

Description

Permutation-based identification of Significantly Mutated Genes, i.e. MutSigCL or MutSigFN.

Usage

1
mutsigclfn(bkgrSQLiteDB, obs_data, outfile='out.txt', genes=c(), type=c('CL','FN'), hotspot.alg=c('hclust','ratio'), min.cl=0.2, nperm=1000, mc.cores=4, bkgr_data=dbConnect(dbDriver('SQLite'), bkgrSQLiteDB))

Arguments

bkgrSQLiteDB

An SQLite DB storing background mutation-related info.

obs_data

A data frame of the observed input data.

outfile

Output file for mutsigclfn.

genes

Genes to be permutated.

type

Algorithm, i.e. CL - MutSigCL, FN - MutSigFN.

hotspot.alg

Algorithm to define hotspot statistic.

min.cl

Genes with fraction of hotspot >min.cl are selected for MutSigCL analysis. To disable it set min.cl to 0.

nperm

Number of permutation.

mc.cores

Number of cores used in mclapply.

bkgr_data

An RSQLite object, never change unless your're quite sure!

Details

Make sure that bkgrSQLiteDB and obs_data are consistent.

When hotspot.alg is set to 'hclust', mutsigclfn will employ the following algorithm to define hotspot statistic (fraction):

"A hotspot is defined as a 3-base-pair region of the gene containing many mutations: at least 2, and at least 2 the total mutations (nature12912)."

This involves using hclust in package stats or fastcluster to perform hierarchical clustering and call cutree to calculate hotspot statistic. It's quite time-consuming in large permutations.

When hotspot.alg is set to 'ratio' (much faster than 'hclust' above), I define the hotspot statistic as:

The ratio of the number of mutations to the total number of mutation positions, at least <e2><89><a5>2 mutations is required.

Change Log: Function name pbiSMG was changed to mutsigclfn in v 1.45.

See Also

mutsig.gene,mutsig.pathway

Examples

1
2
3
4
library(parallel)
library(RSQLite)
library(fastcluster) # accelerate MutSigCL analysis
mutsigclfn(...)

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