Oscope (version 1.2.0)

OscopeKM: Oscope K medoid module

Description

Oscope K medoid module

Usage

OscopeKM(SineRes, quan=.95,cut=NULL,maxK=NULL,minSize=0, maxSize=200, fixK=NULL, rawscale=TRUE)

Arguments

SineRes

output of OscopeSine function.

quan

only gene pairs with similarity score >= quan th quantile will be considered in the clustering analyses. Default is 0.95.

cut

pre-defined cutoff. Gene pairs with similarity score >= cut will be considered in cluster analyses. If cut is defined, quan will be ignored.

maxK

max number of clusters to consider (scan). if numbC=NULL, it will be calculated as [number of gene considered]/10

minSize,maxSize

Only clusters with minSize

fixK

if fixK is specified, the k-medoids algorithm will be applied with fixK clusters.

rawscale

Recall the input is the similarity matrix (-log10(distance from the sine model)). the k-medoids clustering will be applied using (-Input) as distance. If rawscale is defined as TRUE, the k-medoids clustering will be applied using -10^Input as distance.

Value

OscopeKM() calls scanK() function, which runs k-medoid clustering with varying number of clusters (k). The k is varied from 2 to maxK. The input should be the output of OscopeSine() function. scanK() function will cluster genes in gene pairs with high similarity score (the threshold can be defined using parameter quan). To select the top genes, the function first calculate the max similarity score for each gene, then select the genes with high max score.The output object shows members in each cluster. clusters are sorted by median similarity score within cluster.

Examples

aa <- sin(seq(0,1,.1))
bb <- sin(seq(0.5,1.5,.1))
cc <- sin(seq(0.9,1.9,.1))
tmp <- matrix(sin(rnorm(330)),ncol=11)
rownames(tmp) <- paste0("tmp",1:30)
Dat <- rbind(aa, bb, cc, tmp)
res1 <- OscopeSine(Dat)
res2 <- OscopeKM(res1, quan=.8, maxK=5)