tool.coalesce.exec {Mergeomics} | R Documentation

## 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

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)

[Package Mergeomics version 1.10.0 Index]