Oscope (version 1.2.0)

scanK: Run k-medoid algorithm with varying k on similarity matrix

Description

Run k-medoid algorithm with varying k on similarity matrix

Usage

scanK(SimiMatIn, quan=.95,cut=NULL, maxK=NULL,minSize=0, maxSize=200, fixK=NULL, rawscale=FALSE)

Arguments

SimiMatIn

gene-by-gene similarity matrix

quan

only gene pairs with similarity score >= quan th quantile will be considered in the cluster 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

scanK() function runs k-medoid clustering with varying number of clusters (k). The k is varied from 2 to maxK. The input of scanK() function should be a similarity matrix. 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 is a list with 4 sublists: membOut: members in each cluster. clusters are sorted by median similarity score within cluster;MedCor: median similarity score for each cluster;Mat: input similarity matrix;filteredMat: similarity matrix, only showing the top genes used in clustering;Kcluster: cluster indicator of each top gene.

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 <- scanK(res1$SimiMat, quan=.8, maxK=5)