tool.coalesce {Mergeomics} | R Documentation |
tool.coalesce
is utilized to merge and trim either overlapping
modules (containing shared genes) or overlapping genes (containing
shared markers)
tool.coalesce(items, groups, rcutoff = 0, ncore = NULL)
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 |
a data list with the following components:
CLUSTER |
cluster identities after merging and triming (a subset of group identities) |
ITEM |
item identities |
GROUPS |
comma separated overlapping group identities |
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.
## read the coexpr module file as an example: moddata <- tool.read(system.file("extdata", "modules.mousecoexpr.liver.human.txt", package="Mergeomics")) ## let us find the overlapping ratio between first 10 modules in the file: ## to merge overlapping modules first collect member genes: mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)), 10)] moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),] ## Merge and trim overlapping modules.(max allowed overlap ratio is 0.33) rmax <- 0.33 moddata$OVERLAP <- moddata$MODULE moddata <- tool.coalesce(items=moddata$GENE, groups=moddata$MODULE, rcutoff=rmax) moddata$MODULE <- moddata$CLUSTER moddata$GENE <- moddata$ITEM moddata$OVERLAP <- moddata$GROUPS moddata <- moddata[,c("MODULE", "GENE", "OVERLAP")] moddata <- unique(moddata)