tool.coalesce.exec {Mergeomics} | R Documentation |
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.
tool.coalesce.exec(items, groups, rcutoff, ncore)
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) |
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.
## 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)