Description Usage Arguments Details Value Author(s) References See Also Examples
GSZ scoring function profile data
1 | StabPlotData(mGSZobj,rank.vector,sample.perm.data=FALSE)
|
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. |
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.
An R object with running GSZ scores for positive and permuted data to be used as input for profile plot.
Pashupati Mishra, Petri Toronen
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | gene.names <- paste("g",1:1000, sep = "")
# create random gene expression data matrix
set.seed(100)
expr.data <- matrix(rnorm(1000*50),ncol=50)
rownames(expr.data) <- gene.names
b <- matrix(2*rnorm(2500),ncol=25)
ind <- sample(1:100,replace=FALSE)
expr.data[ind,26:50] <- expr.data[ind,26:50] + b
sample.labels <- rep(1:2,c(25,25))
# create random gene sets
gene.sets <- vector("list", 100)
for(i in 1:length(gene.sets)){
gene.sets[[i]] <- sample(gene.names, size = 20)
}
names(gene.sets) <- paste("set", as.character(1:100), sep="")
mGSZ.obj <- mGSZ(expr.data, gene.sets, sample.labels, perm.number = 100)
top.mGSZ.sets <- toTable(mGSZ.obj, no.top.sets = 10)
# Scoring function profile data across the ordered gene list for top 5 gene sets
data4plot <- StabPlotData(mGSZ.obj,rank.vector=c(1,2,3,4,5))
# Profile plot for the top gene set
plotProfile(data4plot,1)
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