ChiaKaruturi: Chia and Karuturi Function

In biclust: BiCluster Algorithms

Description

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

Usage

ChiaKaruturi(x, bicResult, number)

Arguments

x	Data Matrix
bicResult	Biclust object from biclust package
number	Number of bicluster in the output for computing the scores

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.

Value

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

Author(s)

Tatsiana KHAMIAKOVA [e-mail]

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(20*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)

Example output

Loading required package: MASS
Loading required package: grid
Loading required package: colorspace
Loading required package: lattice
layer: 0 
 901.0827
layer: 1 
[1]  0 20 10
[1] 100  20  10
[1] 101  20  10
[1] 200  20  10
[1] 1
 [1] 416.93189   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000
 [8]   0.00000   0.00000  15.02406   0.00000   0.00000   0.00000   0.00000
[15]   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000
[22]   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000
[29]   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000
[36]   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000
[43]   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000
[50]   0.00000   0.00000
back fitting 5 times
     
Layer Rows Cols Df      SS    MS Convergence Rows Released Cols Released
    0   50   50 99   12.02  0.12          NA            NA            NA
    1   20   10 29 1726.04 59.52           1             0             0
   Tscore1  Bscore1    Tscore2       Bscore2  SBscore      TSscore
1 8.692427 8.751864 0.00142508 -0.0001175226 6.642353 -0.006806751

biclust documentation built on May 2, 2019, 5:56 p.m.