tool.coalesce.find {Mergeomics} | R Documentation

## Find overlapping clusters

### Description

`tool.coalesce.find` finds overlapped clusters of the given `data` according to a given overlapping ratio by using `tool.overlap` and `tool.cluster`, respectively.

### Usage

    tool.coalesce.find(data, rmax)

### Arguments

`data` | a list including ITEM identities and their GROUP identities
`rmax` | maximum overlap not coalesced

### Value

data list including clustering results and following components:

`CLUSTER` | cluster label
`NODE` | item (node) name

### 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)
    ## 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)
    }

[Package Mergeomics version 1.15.0 Index]