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Create a summary of multiple tmod analyses

tmodSummary.Rd

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"
)

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

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.

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.

See also

tmodPanelPlot

Examples

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)
}