tool.coalesce.find {Mergeomics} | R Documentation |
tool.coalesce.find
finds overlapped clusters of the given
data
according to a given overlapping ratio by using
tool.overlap
and tool.cluster
, respectively.
tool.coalesce.find(data, rmax)
data |
a list including ITEM identities and their GROUP identities |
rmax |
maximum overlap not coalesced |
data list including clustering results and following components:
CLUSTER |
cluster label |
NODE |
item (node) name |
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) }