term_enrichment_by_subset: term_enrichment_by_subset
In RITAN: Rapid Integration of Term Annotation and Network resources

Description Usage Arguments Value Examples

Run enrichment simultaneously across a group of prioritized gene lists. For example, in a time course dataset, one may have a different list of genes that are differentially expressed at each time point. This function facilitates rapid evaluation of term enrichment across time point comparisons. Alternatively, one may have a different list of differentially expressed genes by drug treatment, environmental condition, ect.

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term_enrichment_by_subset(groups = NA, resources = resources.default,
  q_value_threshold = 0.01, verbose = TRUE, display_type = "q",
  phred = TRUE, ...)

`groups`	A list() of genes for enrichment. Each entry in the list() is an input set of genes. Enrichment is performed for each of these entries.
`resources`	character vector for which resources to use in enrichment
`q_value_threshold`	minimum q-value (FDR adjusted p-value) in any group for the term to be included in results
`verbose`	print additional status updates on what the function is doing
`display_type`	Flag for which data type will be returned. One of "q" (default) for q-values, "p" for unadjusted p-values, or "n" for the number of genes overlapping the term.
`phred`	Logical flag (default TRUE) to return the -log10 of p/q values
`...`	Further arguments are passed on to enrichment_symbols()

Returns a term-by-study matrix of enrichment values (value determined by "display_type")

## Create list of gene sets to evaluate.
##   This example is from a vaccine study where we pre-generated differentially expressed genes.
##   This object will be passed to the groups parameter.
require(RITANdata)
vac1.de.genes <- list(vac1.day0vs31.de.genes, vac1.day0vs56.de.genes)
names(vac1.de.genes) <- c("Day0vs31", "Day0vs56")
print(str(vac1.de.genes))

## Not run: 
## Run term_enrichment_by_subset on the two results.
##   This function usually takes a few seconds to a minute to run.
m <- term_enrichment_by_subset(groups = vac1.de.genes, q_value_threshold = .9)
summary(m)
plot( m, label_size_y = 4, show_values = FALSE )

## End(Not run)