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Function computing scores as described in the paper of Chia and Karuturi (2010)
ChiaKaruturi(x, bicResult, number)Data.Frame with 6 slots: T, B scores for within and outside bicluster, SB and TS scores
Data Matrix
Biclust object from biclust package
Number of bicluster in the output for computing the scores
Tatsiana KHAMIAKOVA tatsiana.khamiakova@uhasselt.be
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.
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.
diagnosticPlot, computeObservedFstat, diagnoseColRow
#---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)
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