tool.coalesce.merge {Mergeomics} | R Documentation |
tool.coalesce.merge
determines combinable groups and trims clusters
by removing rarest items.
tool.coalesce.merge(data, ncore)
data |
data list including following components: CLUSTER: cluster label NODE: item (node) name |
ncore |
minimum number of items required for trimming |
res |
data list including GROUPS, ITEMs, and their hit COUNTs |
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) }