gage(exprs, gsets, ref = NULL, samp = NULL, set.size = c(10, 500), same.dir = TRUE, compare = "paired", rank.test = FALSE, use.fold = TRUE, FDR.adj = TRUE, weights = NULL, full.table = FALSE, saaPrep = gagePrep, saaTest = gs.tTest, saaSum = gageSum, use.stouffer=TRUE, ...)gagePrep(exprs, ref = NULL, samp = NULL, same.dir = TRUE, compare = "paired", rank.test = FALSE, use.fold = TRUE, weights = NULL, full.table = FALSE, ...)gageSum(rawRes, ref = NULL, test4up = TRUE, same.dir = TRUE, compare = "paired", use.fold = TRUE, weights = NULL, full.table = FALSE, use.stouffer=TRUE, ...)
head(kegg.gs). A gene set can also be a "smc" object defined in PGSEA package. Please make sure that the same gene ID system is used for both
For PAGE-like analysis, the default is compare='as.group', which is the only option provided in the original PAGE method. All other comparison schemas are set here for direct comparison to gage.
gagefunction, i.e. when test for one-directional changes.
gagePrepis a data matrix derived from
exprs, but ready for column-wise gene est tests. In the matrix, genes are rows, and columns are the per gene test statistics from the ref-samp pairwise comparison.The result returned by
gageSumis almost identical to the results of
gagefunction, it is also a named list but has only 2 elements, "p.glob" and "results", with one round of test results.
some important updates has been made to gage package since version 2.2.0
First, more robust p-value summarization using Stouffer's method
through argument use.stouffer=TRUE. The original p-value
summarization, i.e. negative log sum following a Gamma distribution as
the Null hypothesis, may produce less stable global p-values for large
or heterogenous datasets. In other words, the global p-value could be
heavily affected by a small subset of extremely small individual
p-values from pair-wise comparisons. Such sensitive global p-value
leads to the "dual signficance" phenomenon. Dual-signficant means a gene set is called
significant simultaneously in both 1-direction tests (up- and
signficance" could be informative in revealing the sub-types or
sub-classes in big clinical or disease studies, but may not be
desirable in other cases.
Second, output of gage function now includes the gene set test
statistics from pair-wise comparisons for all proper gene sets. The
output is always a named list now, with either 3 elements
("greater", "less", "stats") for one-directional test or 2 elements
("greater", "stats") for two-directional test.
Third, the individual p-value (and test statistics)from dependent pair-wise
comparisions, i.e. comparisions between the same experiment vs
different controls, are now summarized into a single value. In other
words, the column number of individual p-values or statistics is
always the same as the sample number in the experiment (or disease)
group. This change made the argument value compare="1ongroup"
and argument full.table less useful. It also became easier to check the
perturbations at gene-set level for individual samples.
Fourth, whole gene-set level changes (either p-values or statistics)
can now be visualized using heatmaps due to the third change above.
been revised to plot heatmaps for whole gene sets.
gs.KSTestfunctions used for gene set test;
heter.gagefunction used for multiple GAGE analysis in a batch or combined GAGE analysis on heterogeneous data
data(gse16873) cn=colnames(gse16873) hn=grep('HN',cn, ignore.case =TRUE) dcis=grep('DCIS',cn, ignore.case =TRUE) data(kegg.gs) data(go.gs) #kegg test for 1-directional changes gse16873.kegg.p <- gage(gse16873, gsets = kegg.gs, ref = hn, samp = dcis) #go.gs with the first 1000 entries as a fast example. gse16873.go.p <- gage(gse16873, gsets = go.gs, ref = hn, samp = dcis) str(gse16873.kegg.p) head(gse16873.kegg.p$greater) head(gse16873.kegg.p$less) head(gse16873.kegg.p$stats) #kegg test for 2-directional changes gse16873.kegg.2d.p <- gage(gse16873, gsets = kegg.gs, ref = hn, samp = dcis, same.dir = FALSE) head(gse16873.kegg.2d.p$greater) head(gse16873.kegg.2d.p$stats) ###alternative ways to do GAGE analysis### #with unpaired samples gse16873.kegg.unpaired.p <- gage(gse16873, gsets = kegg.gs, ref = hn, samp = dcis, compare = "unpaired") #other options to tweak includes: #saaTest, use.fold, rank.test, etc. Check arguments section above for #details and the vignette for more examples.