mGSZ (version 1.0)

StabPlotData: GSZ scoring function profile data

Description

GSZ scoring function profile data

Usage

StabPlotData(mGSZobj,rank.vector,sample.perm.data=FALSE)

Arguments

mGSZobj

mGSZ object

rank.vector

A vector of ranks for gene sets for which GSZ scoring function profile data is required.

sample.perm.data

Profile data for sample permutation data when both gene and sample permutation are used.

Value

An R object with running GSZ scores for positive and permuted data to be used as input for profile plot.

Details

Once significant gene sets are reported, it is useful to evaluate a gene set in more detail to see the behavior of the gene set. This can be done by visualizing the scoring function profile across the gene list as shown in the GSEA article (Subramanian et al., 2005). It is even more relevant to compare signals from positive and permuted data. Positive data corresponds to differential gene expression test scores calculated from gene expression data with correct sample labels and permuted data corresponds to differential gene expression test scores calculated from gene expression data with permuted sample labels.This function outputs scoring function profile data for both positive and permuted data to be used as input for the visualization that shows the signal from positive data and a summary of the signal from permuted data.

References

Mishra Pashupati, Toronen Petri, Leino Yrjo, Holm Liisa. Gene Set Analysis: Limitations in popular existing methods and proposed improvements (Not yet published) http://ekhidna.biocenter.helsinki.fi/downloads/pashupati/mGSZ.html Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., et al. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America, 102(43), 15545-15550.

Toronen, P., Ojala, P. J., Marttinen, P., and Holm, L. (2009). Robust extraction of functional signals from gene set analysis using a generalized threshold free scoring function. BMC Bioinformatics, 10(1), 307.

See Also

plotProfile

Examples

gene.names <- paste("g",1:100, sep = "")

# create random gene expression data matrix

set.seed(100)
x <- matrix(rnorm(100*10),ncol=10)
rownames(x) <- gene.names
b <- matrix(2*rnorm(50),ncol=5)
ind <- sample(1:10,replace=FALSE)
x[ind,6:10] <- x[ind,6:10] + b

l <- rep(1:2,c(5,5))

# create random gene sets

y <- vector("list", 20)
for(i in 1:length(y)){
	y[[i]] <- sample(gene.names, size = 10)
}
names(y) <- paste("set", as.character(1:20), sep="")

mGSZ.obj <- mGSZ(x, y, l, p = 100)
top.mGSZ.sets <- toTable(mGSZ.obj, n = 10) 

# scoring function profile data across the ordered gene list for top 2 gene sets

data4plot <- StabPlotData(mGSZ.obj,rank.vector=c(1,2))

# profile plot for the top gene set

plotProfile(data4plot,1)