version 2.2.x
o 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 down-regulated). "Dual 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.
o 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.
o 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.
o Whole gene-set level changes (either p-values or statistics) can
now be visualized using heatmaps due to the third change above.
Correspondingly, functions \code{sigGeneSet} and \code{gagePipe}
have been revised to plot heatmaps for whole gene sets.
o Fixed a bug in gs.zTest function: mod <- (length(ix)/s)^(1/2), it
is mod <- length(ix)^(1/2)/s before. Thanks to Nhan Thi HO from
Michigan State University.