Description Usage Arguments Details Value Author(s) References See Also Examples
This function is used to compare the results after running multiple
rounds of GAGE analysis. It is frequently used after batch analysis
gagePipe, but may also be used after multiple runs of
character vector, the names of the sample groups, on which the GAGE
analysis has been done and to be compared. This same argument is
character, the name of the data on which the GAGE analysis has been
done. This same argument is used in
character, the name(s) of the gene set collection(s) to be
considered in the comparison. In other words, this argument
specifies GAGE analysis results with what type(s) of gene sets
are to be compared on. Default to be
character, what columns in the
numeric, q-value cutoff between 0 and 1 for signficant gene sets
selection. Default to be 0.1. The same argument is used in
boolean, whether to plot the venn diagram for the comparison results. Default to be TRUE.
gageComp works with the results of
gagePipe run by
default. Try to load the .RData file named after
first. It there is no such file, it assumes that the
result objects have been loaded and exist in the global environment.
For the GAGE analysis results with each gene set collection specified
gagePipe compares the signficant gene set
lists between the sample groups specified in
each gene set collection, three comparisons will be done, on the
2-direction perturbed, up-regulated, and down-regulated gene sets.
The comparison results are output as tab-delimited text files. Venn digrams are only plot for comparison between 2-3 parties. But the text file outputs are not limited by the number of parties under comparison. The venn diagram is generated by calling a revised function based on the VennDigram function from limma package.
The function returns invisible 1 when successfully executed.
Weijun Luo <email@example.com>
Luo, W., Friedman, M., Shedden K., Hankenson, K. and Woolf, P GAGE: Generally Applicable Gene Set Enrichment for Pathways Analysis. BMC Bioinformatics 2009, 10:161
gagePipe pipeline for multiple GAGE analysis in a batch;
gage the main function for GAGE analysis
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data(gse16873) cn=colnames(gse16873) hn=grep('HN',cn, ignore.case =TRUE) dcis=grep('DCIS',cn, ignore.case =TRUE) data(kegg.gs) library(gageData) data(gse16873.2) cn2=colnames(gse16873.2) hn2=grep('HN',cn2, ignore.case =TRUE) dcis2=grep('DCIS',cn2, ignore.case =TRUE) #multiple GAGE analysis in a batch with the combined data gse16873=cbind(gse16873, gse16873.2) dataname='gse16873' #output data prefix sampnames=c('dcis.1', 'dcis.2') refList=list(hn, hn2+12) sampList=list(dcis, dcis2+12) gagePipe(gse16873, gsname = "kegg.gs", dataname = "gse16873", sampnames = sampnames, ref.list = refList, samp.list = sampList, comp.list = "paired") #follow up comparison between the analyses load('gse16873.gage.RData') #list gage result objects objects(pat = "[.]p$") gageComp(sampnames, dataname, gsname = "kegg.gs", do.plot = TRUE)
 "dcis.1.kegg.gs.2d.p" "dcis.1.kegg.gs.p" "dcis.2.kegg.gs.2d.p"  "dcis.2.kegg.gs.p"
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