biclust (version 2.0.3.1)

ChiaKaruturi: Chia and Karuturi Function

Description

Function computing scores as described in the paper of Chia and Karuturi (2010)

Usage

ChiaKaruturi(x, bicResult, number)

Value

Data.Frame with 6 slots: T, B scores for within and outside bicluster, SB and TS scores

Arguments

x

Data Matrix

bicResult

Biclust object from biclust package

number

Number of bicluster in the output for computing the scores

Author

Tatsiana KHAMIAKOVA

Details

The function computes row (T) and column (B) effects for a chosen bicluster. The scores for columns within bicluster have index 1, the scores for columns outside the bicluster have index 2. Ranking score is SB, stratification score is TS.

References

Chia, B. K. H. and Karuturi, R. K. M. (2010) Differential co-expression framework to quantify goodness of biclusters and compare biclustering algorithms. Algorithms for Molecular Biology, 5, 23.

See Also

diagnosticPlot, computeObservedFstat, diagnoseColRow

Examples

#---simulate dataset with 1 bicluster ---#
xmat<-matrix(rnorm(50*50,0,0.25),50,50) # background noise only 
rowSize <- 20 #number of rows in a bicluster 
colSize <- 10 #number of columns in a bicluster
a1<-rnorm(rowSize,1,0.1) #sample row effect from N(0,0.1) #adding a coherent values bicluster:
b1<-rnorm((colSize),2,0.25)  #sample column effect from N(0,0.05)
mu<-0.01 #constant value signal
 for ( i in 1 : rowSize){
 	for(j in 1: (colSize)){
 		xmat[i,j] <- xmat[i,j] + mu + a1[i] + b1[j] 	
 	}
 }
 #--obtain a bicluster by running an algorithm---# 
plaidmab <- biclust(x=xmat, method=BCPlaid(), cluster="b", fit.model = y ~ m + a+ b,  
background = TRUE, row.release = 0.6, col.release = 0.7, shuffle = 50, back.fit = 5, 
max.layers = 1, iter.startup = 100, iter.layer = 100, verbose = TRUE)

#Get Chia and Karuturi scores:
ChiaKaruturi(x=xmat, bicResult = plaidmab, number = 1)