tmodSummary: Create a summary of multiple tmod analyses

Description

Create a summary of multiple tmod analyses

Usage

tmodSummary(
  x,
  clust = NULL,
  filter.empty = FALSE,
  filter.unknown = TRUE,
  select = NULL,
  effect.col = NULL,
  pval.col = "adj.P.Val"
)

Value

a data frame with a line for each module encountered anywhere in the list x, two columns describing the module (ID and module title), and two columns(effect size and q value) for each element of list x.

Arguments

x: list, in which each element has been generated with a tmod test function
clust: whether, in the resulting data frame, the modules should be ordered by clustering them with either q-values ("qval") or the effect size ("effect"). If "sort" or NULL, the modules are sorted alphabetically by their ID. If "keep", then the order of the modules is kept.
filter.empty: If TRUE, all elements (columns) with no significant enrichment will be removed
filter.unknown: If TRUE, modules with no annotation will be omitted
select: a character vector of module IDs to show. If clust == "keep", then in that particular order.
effect.col: The name of the column with the effect size (if NULL, the default, the effect size will be taken from the tmod results object attributes)
pval.col: The name of the p-value column

Details

This function is useful if you run an analysis for several conditions or time points and would like to summarize the information in a single data frame. You can use lapply() to generate a list with tmod results and use tmodSummary to convert it to a data frame.

Examples

Run this code

if (FALSE) {
data(Egambia)
E <- Egambia[,-c(1:3)]
pca <- prcomp(t(E), scale.=TRUE)

# Calculate enrichment for each component
gs   <- Egambia$GENE_SYMBOL
gn.f <- function(r) {
    tmodCERNOtest(gs[order(abs(r), 
                decreasing=TRUE)], 
                qval=0.01)
}
x <- apply(pca$rotation, 2, gn.f)
tmodSummary(x)
}