Mergeomics (version 1.0.0)

tool.coalesce.merge: Merge overlapping clusters

Description

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

Usage

tool.coalesce.merge(data, ncore)

Arguments

data

data list including following components:

CLUSTER: cluster label
NODE: item (node) name

ncore

minimum number of items required for trimming

Value

res

data list including GROUPS, ITEMs, and their hit COUNTs

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)
## 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)
}