Mergeomics (version 1.0.0)

tool.coalesce.exec: Find, merge, and trim overlapping clusters

Description

tool.coalesce.exec searchs overlaps, iteratively merges and trims overlapping clusters (by using tool.coalesce.find and tool.coalesce.merge, respectively) until no more overlap is available, and assigns representative label for the merged clusters.

Usage

tool.coalesce.exec(items, groups, rcutoff, ncore)

Arguments

items

array of item identities

groups

array of group identities for items

rcutoff

maximum overlap not coalesced

ncore

minimum number of items required for trimming

Value

CLUSTER

cluster identities after merging and triming (a subset of group identities)

GROUPS

comma separated overlapping group identities

References

Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B, Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics resources to identify pathogenic perturbations to biological systems. bioRxiv doi: http://dx.doi.org/10.1101/036012

Examples

## Generate item and group labels for 100 items:
## Assume that unique gene number (items) is 60:
members <- 1:100 ## will be updated
modules <- 1:100 ## will be updated
set.seed(1)
for (i in 1:10){
## each time pick 10 items (genes) from 60 unique item labels
members[(i*10-9):(i*10)] <- sample(60,10) 
}
## Assume that unique group labels is 30:
for (i in 1:10){
## each time pick 10 items (genes) from 30 unique group labels
modules[(i*10-9):(i*10)] <- sample(30, 10)
}
rcutoff <- 0.33
ncore <- length(members)
## Find and trim clusters after iteratively merging the overlapping ones:
res <- tool.coalesce.exec(members, modules, rcutoff, ncore)