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

In zeynebkurtUCLA/Mergeomics: Integrative network analysis of omics data

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

a data list with the following components:

CLUSTER	cluster identities after merging and triming (a subset of group identities)
GROUPS	comma separated overlapping group identities

Author(s)

Ville-Petteri Makinen

References

Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD, Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X. Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems. BMC genomics. 2016;17(1):874.

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)

zeynebkurtUCLA/Mergeomics documentation built on May 14, 2019, 1:59 a.m.