hic_exactTest: Perform exact test based difference detection on a Hi-C...

In jstansfield0/multiHiCcompare: Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are available

Description

Perform exact test based difference detection on a Hi-C experiment

Usage

hic_exactTest(hicexp, parallel = FALSE, p.method = "fdr", max.pool = 0.7)

Arguments

hicexp	A hicexp object.
parallel	Logical, should parallel processing be used?
p.method	Charact string to be input into p.adjust() as the method for multiple testing correction. Defaults to "fdr".
max.pool	The proportion of unit distances after which all further distances will be pooled. Distances before this value will be progressively pooled and any distances after this value will be combined into a single pool. Defaults to 0.7. Warning: do not adjust this value from the default unless you are getting errors related to the lfproc function or due to sparsity in fastlo normalization. If these errors occur it is due to either sparsity or low variance and max.pool will need to be lowered; typically to 0.5 or 0.6.

Details

This function performs the edgeR exact test on a per distance basis for Hi-C data. It tests for differences between two groups when the groups are the only variable of interest. This is an application of the negative binomial exact test proposed by Robinson and Smyth (2008) for a difference in mean between the groups. These exact tests are applied to the Hi-C data on a distance group basis using "progressive pooling" of distances.

Value

A hicexp object with the comparison slot filled.

Examples

## Not run: 
data("hicexp_diff")
hicexp_diff <- hic_exactTest(hicexp_diff)
## End(Not run)

jstansfield0/multiHiCcompare documentation built on May 4, 2021, 7:34 a.m.