runConCub {geecc} | R Documentation |
Perform the enrichment analysis on two- or three-way contingency tables.
runConCub(obj, filter, nthreads = 2, subset = NULL, verbose=list(output.step=0, show.cat1=FALSE, show.cat2=FALSE, show.cat3=FALSE))
obj |
an object with class concub |
filter |
an object with class concubfilter |
nthreads |
number of threads to use in |
subset |
a named list. Restrict enrichment analysis to these category variables |
verbose |
A list to control verbosity:
|
This function applies a test for association for all combinations of all variables of all categories to be tested. Depending on the settings in the concubfilter-object, a one-sided or two-sided test is made, using the exact hypergeometric test as implemented in the hypergea-package if the smallest expected value is smaller than 5, or using the chi-squared test as implemented in the loglm-function implemented in the MASS-package. The minimum expected value can be changed in the concub-object by the user (approx
-parameter).
In this function only filter-settings those filter settings are used, which skip the tests.
An object with class concub.
## ## a completely artificial example run ## through the routines of the package ## R <- 500 #generate R random gene-ids ID <- sapply(1:R, function(r){paste( sample(LETTERS, 10), collapse="" ) } ) ID <- unique(ID) #assign artificial differentially expressed genes randomly category1 <- list( deg.smallFC=sample(ID, 100, rep=FALSE), deg.hughFC=sample(ID, 100, rep=FALSE) ) #assign artificial GO terms of genes randomly category2 <- list( go1=sample(ID, 50, replace=FALSE), go2=sample(ID, 166, replace=FALSE), go3=sample(ID, 74, replace=FALSE), go4=sample(ID, 68, replace=FALSE) ) #assign artificial sequence length of genes randomly LEN <- setNames(sample(seq(100, 1000, 100), length(ID), replace=TRUE), ID) category3 <- split( ID, f=factor(LEN, levels=seq(100, 1000, 100)) ) CatList <- list(deg=category1, go=category2, len=category3) ConCubFilter.obj <- new("concubfilter", names=names(CatList)) ConCub.obj <- new("concub", categories=CatList) ConCub.obj.2 <- runConCub( obj=ConCub.obj, filter=ConCubFilter.obj, nthreads=1 ) ConCub.obj.2