similarity {superbiclust}

Similarity Matrix for bicluster output

Description

computes similarity matrix for the biclustering output based on one of the pairwise similarity indices of biclusters in a given bicluster set

Usage

similarity(x, index = "jaccard", type="rows")

Arguments

x

BiclustSet object containing row and column indicators of bicluster membership, number of biclusters

index

similarity index for the biclusters in output

type

whether to perform similarity in two dimensions, "both" (recommended for biclustering), row dimension, "rows" (default, requires less computations) or column dimension "cols"

Details

This function operates on BiclustSet object and computes pairwise similarity based on the common elements between biclusters. type variable controls whether similarity index is constructed for all elements, or in one dimension (rows or columns) only. In general, similarity indices for one dimension (row or column) are higher than for two-dimensions. Several options for similarity indices are offered: jaccard (default), kulczynski, sensitivity, specificity, sorensen and ochiai indices.

Value

a "similarity" object containing similarity matrix

Author(s)

Tatsiana Khamiakova

See Also

HCLtree, plotSuper , jaccardMat,kulczynskiMat, ochiaiMat, sensitivityMat, specificityMat,sorensenMat

Examples

#compute sensitivity for BiMAX biclusters
 test <- matrix(rnorm(5000), 100, 50)
 test[11:20,11:20] <- rnorm(100, 3, 0.1)
 test[17:26,21:30] <- rnorm(100, 3, 0.1)
 testBin <- binarize(test,2)
 res <- biclust(x=testBin, method=BCBimax(), minr=4, minc=4, number=10)
 BiMaxBiclustSet <-  BiclustSet(res)
 SensitivityMatr<- similarity(BiMaxBiclustSet,index="sensitivity", type="rows")
 SensitivityMatr