nc_eval {netcutter} | R Documentation |
Compute co-occurrence probabilities
Description
The main NetCutter function. It generates p-values for all the co-occurring modules.
Usage
nc_eval(
occ_matrix,
occ_probs,
terms_of_interest = NULL,
module_size = 2,
min_occurrences = 0,
min_support = 0,
mc.cores = 1
)
Arguments
occ_matrix |
The original occurrence matrix. |
occ_probs |
The matrix of occurrence probabilities, as computed by
|
terms_of_interest |
Vector of column names or indices representing the terms that should be included in the analysis. |
module_size |
The number of terms that should be tested for co-occurrence. |
min_occurrences |
Minimum number of occurrences of each term. |
min_support |
Minimum number of occurrences of each module. |
mc.cores |
Number of parallel computations with mclapply() (set to 1 for serial execution) |
Details
If terms_of_interest
is NULL
, all the terms in occ_matrix
are used. If
it is not null, only modules containing at least one of these terms will be
considered. min_occurrences
and min_support
are still used to further
restrict the list of terms that are considered.
Value
A data.frame
with one row for each valid module, and corresponding
number of co-occurrences and p-value.
Examples
# Generate an occurrence matrix.
m <- matrix(FALSE, 3, 9, dimnames = list(paste0("ID", 1:3), paste0("gene", 1:9)))
m[1, 1:3] <- m[2, c(1:2, 4:5)] <- m[3, c(1, 6:9)] <- TRUE
# Set the seed using the "L'Ecuyer-CMRG" random number generator.
set.seed(1, "L'Ecuyer-CMRG")
# Compute the occurrence probabilities.
occ_probs <- nc_occ_probs(m, R = 20, S = 50)
# Evaluate the co-occurrences of pairs of terms and their statistical significance.
nc_eval(m, occ_probs, module_size = 2)
# Now evaluate triples; no need to recompute the occurrence probabilities.
nc_eval(m, occ_probs, module_size = 3)
# Now consider only modules involving gene1 or gene2.
nc_eval(m, occ_probs, module_size = 2, terms_of_interest = c("gene1", "gene2"))