tool.coalesce.merge: Merge overlapping clusters
In Mergeomics: Integrative network analysis of omics data

Description Usage Arguments Value Author(s) References Examples

tool.coalesce.merge determines combinable groups and trims clusters by removing rarest items.

1	tool.coalesce.merge(data, ncore)

data

data list including following components:

CLUSTER: cluster label
NODE: item (node) name

ncore

minimum number of items required for trimming

res

data list including GROUPS, ITEMs, and their hit COUNTs

Ville-Petteri Makinen

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.

## 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)
## Default output.
res <- data.frame(CLUSTER=modules, GROUPS=modules, ITEM=members,
stringsAsFactors=FALSE)
## Iterative merging and trimming.
res$COUNT <- 0.0
while(TRUE) {
clust <- tool.coalesce.find(res, rcutoff)    
if(is.null(clust)) break
res <- tool.coalesce.merge(clust, ncore)
}