runSeqGSEA: An all-in function that allows end users to apply SeqGSEA to...

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/SeqGSEA.R

Description

This function provides typical SeqGSEA analysis pipelines for end users to apply the SeqGSEA method in the easiest fashion. It assumes the pipelines start with exon reads counts, even for the DE-only analysis. Users should specify their file locations and a few parameters before running this pipeline.

It allows DE-only analysis, which will skip the DS analysis portion, and it also allows users to try different weights in integrating DE and DS scores, which will save time in computing the DE and DS scores.

The function returns a list of SeqGSEA analysis results in the format of GSEAresultTable, and generates a few plots and writes a few files, whose name prefix can be specified. The output files will either be in PDF format or TXT format, and generated by plotGeneScore, writeScores, plotES, plotSig, plotSigGeneSet, and writeSigGeneSet.

Usage

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runSeqGSEA(data.dir, case.pattern, ctrl.pattern, geneset.file, output.prefix, topGS=10, 
           geneID.type=c("gene.symbol", "ensembl"), nCores=1, perm.times=1000, seed=NULL, 
           minExonReadCount=5, integrationMethod=c("linear", "quadratic", "rank"), 
           DEweight=c(0.5), DEonly=FALSE, minGSsize=5, maxGSsize=1000, GSEA.WeightedType=1) 

Arguments

data.dir

a character vector, the path to your count data directory.

case.pattern

a character vector, the unique pattern in the file names of case samples. E.g, if file names starting with "SC", the pattern writes "^SC".

ctrl.pattern

a character vector, the unique pattern in the file names of control samples.

geneset.file

a character vector, the path to your gene set file. The gene set file must be in GMT format. Please refer to the link follows for details. http://www.broadinstitute.org/cancer/software/gsea/wiki/index.php/Data_formats#GMT:_Gene_Matrix_Transposed_file_format_.28.2A.gmt.29

output.prefix

a character vector, the path with prefix for output files.

topGS

an integer, this number of top ranked gene sets will be output with details; if geneset.file contains less than this number of gene sets, all gene sets' result details will be output. Default: 10.

geneID.type

the gene ID type in geneset.file. Currently only support "gene.symbol" and "ensembl". Default: gene.symbol.

nCores

an integer. The number of cores for running SeqGSEA. Default: 1

perm.times

an integer. The number of times for permutation, which will be used for normalizing DE and DS scores and for GSEA significance analysis. Recommended values are greater than 1000. Default: 1000.

seed

an integer or NULL, used for setting the seeds to generate random numbers. The same seed will guarantee the same analysis results given by SeqGSEA. Default: NULL.

minExonReadCount

an integer. An exon with total read count across all samples less than this number will be marked as untestable and be excluded in SeqGSEA analysis. Default: 5.

integrationMethod

one of the three integration methods for DE and DS score integration: linear, quadratic, or rank. Default: linear.

DEweight

a real number between 0 and 1 OR a vector of those. Each number is the DE weight in DE and DS integration. If using a vector of real numbers, SeqGSEA will run with each of them individually. Default: 0.5.

DEonly

logical, whether to run SeqGSEA only considering DE. Default: FALSE

minGSsize

an integer. The minimum gene set size: gene sets with genes less than this number will be skipped. Default: 5.

maxGSsize

an integer. The maximum gene set size: gene sets with genes greater than this number will be skipped. Default: 1000.

GSEA.WeightedType

the weight type of the main GSEA algorithm, can be 0 (unweighted = Kolmogorov-Smirnov), 1 (weighted), and 2 (over-weighted). Default: 1. It is recommended not to change it.

Value

A list of SeqGSEA analysis results in the format of GSEAresultTable, which allows users for meta-analysis.

Author(s)

Xi Wang, xi.wang@mdc-berlin.de

References

Xi Wang and Murray J. Cairns (2013). Gene Set Enrichment Analysis of RNA-Seq Data: Integrating Differential Expression and Splicing. BMC Bioinformatics, 14(Suppl 5):S16.

See Also

GSEAresultTable, geneScore, GSEnrichAnalyze

Examples

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### Initialization ###
# input file location and pattern
data.dir <- system.file("extdata", package="SeqGSEA", mustWork=TRUE)
case.pattern <- "^SC" # file name starting with "SC"
ctrl.pattern <- "^SN" # file name starting with "SN"
# gene set file and type
geneset.file <- system.file("extdata", "gs_symb.txt",
                            package="SeqGSEA", mustWork=TRUE)
geneID.type <- "ensembl"
# output file prefix
output.prefix <- "SeqGSEAexample"
# analysis parameters
nCores <- 1
perm.times <- 10
DEonly <- FALSE
DEweight <- c(0.2, 0.5, 0.8) # a vector for different weights
integrationMethod <- "linear"

### one step SeqGSEA running ###
# Caution: if running the following command line, it will generate many files in your working directory
runSeqGSEA(data.dir=data.dir, case.pattern=case.pattern, ctrl.pattern=ctrl.pattern, 
           geneset.file=geneset.file, geneID.type=geneID.type, output.prefix=output.prefix,
           nCores=nCores, perm.times=perm.times, integrationMethod=integrationMethod,
           DEonly=DEonly, DEweight=DEweight)

SeqGSEA documentation built on May 31, 2017, 3:19 p.m.