runGSA: GSA analysis

Description

Determines the significance of pre-defined sets of genes with respect to an outcome variable, such as a group indicator, a quantitative variable or a survival time

Usage

runGSA(nData, labels, gmtfile, chip = "hgu133plus2", np = 1000,
 minsize = 10, maxsize = 800, resp.type = "Two class unpaired",
fdr = 0.25)

Arguments

nData

a matrix or a data frame of expression data. Each row of 'data' must correspond to a gene, and each column to a sample.

labels

a vector of length 'ncol(data)' containing the class labels of the samples. In "Two class unpaired", 'labels' should be a vector containing 0's (specifying the samples of, e.g., the control group) and 1's (specifying, e.g., the case group) or more if multiclass (0,1,2...) In "Two class paired" for paired outcomes, coded -1,1 (first pair), -2,2 (second pair), etc..

gmtfile

a character string corresponding to the path file of a gmt file, corresponding to a gene set collection (a list).

chip

a character string corresponding to the chip type of the data.

a numerical value corresponding to the number of permutations.

minsize

a numerical value corresponding to the minimum number of genes in genesets to be considered.

maxsize

a numerical value corresponding to the minimum number of genes in genesets to be considered.

resp.type

Problem type: "quantitative" for a continuous parameter; "Two class unpaired" ; "Survival" for censored survival outcome; "Multiclass" : more than 2 groups. "Two class paired" for paired outcomes.

fdr

a numerical value corresponding to the fdr threshold.

Value

A list of geneset found If it is a LIST, use

FDRcut

a numerical value corresponding to the threshold FDR.

negative

a character matrix corresponding to the downexpressed gene sets found.

positive

a character matrix corresponding to the upexpressed gene sets found.

nsets.neg

a numerical value corresponding to the number of downexpressed gene sets found.

nsets.pos

a numerical value corresponding to the number of upexpressed gene sets found.

Details

The GSA package is presented as an improvement of the GSEA approach. It differs from a GSEA in its use of the "maxmean" statistic: this is the mean of the positive or negative part of gene scores in the gene set, whichever is large in absolute values.\ Efron and Tibshirani shows that this is often more powerful than the modified KS statistic used in GSEA. GSA also does "restandardization" of the genes (rows), on top of the permutation of columns (done in GSEA).

References

Efron, B. and Tibshirani, R. On testing the significance of sets of genes. Stanford tech report rep 2006. http://www-stat.stanford.edu/~tibs/ftp/GSA.pdf

Subramanian, A. and Tamayo, P. Mootha, V. K. and Mukherjee, S. and Ebert, B. L. and Gillette, M. A. and Paulovich, A. and Pomeroy, S. L. and Golub, T. R. and Lander, E. S. and Mesirov, J. P. (2005) A knowledge-based approach for interpreting genome-wide expression profiles. PNAS. 102, pg 15545-15550.

Examples

Run this code

# NOT RUN {
require(hgu133plus2.db)

## Two class unpaired comparison
## load data
data(marty)

## filtering data
marty <- expFilter(marty, threshold=3.5, graph=FALSE)

##Class label 0/1
marty.type.num <- ifelse(marty.type.cl=="Her2+",0,1)

## run sam analysis
gsaOUT <- runGSA(marty, marty.type.num ,
   gmtfile="./c2.kegg.v2.5.symbols.gmt", chip="hgu133plus2")
# }

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