ChiaKaruturi {biclust} R Documentation

## Chia and Karuturi Function

### 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 tatsiana.khamiakova@uhasselt.be

### 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.

`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)
```

[Package biclust version 2.0.3 Index]