diagnoseColRow {biclust}

Bootstrap Procedure for Bicluster Diagnostics

Description

Calculate the significance of the discovered patter in the data based on the bootstrapping procedure.

Usage

diagnoseColRow(x, bicResult, number, nResamplings, replace = TRUE)

Arguments

x

data matrix, which biclust function was applied to

bicResult

object of class biclust, containing result of a biclustering algorithm

number

number of bicluster from the output for the diagnostics

nResamplings

number of bootstrap replicates

replace

logical flag for bootstrap (TRUE), or sampling without replacement (FALSE)

Details

The function computes observed F statistics for row and column effect based on two-way ANOVA model. Bootstrap procedure is used to evaluate the significance of discovered bicluster. Based on nResamplings replicates, the disribution of F statistics for row and column effects are obtained. The p-value is computed as

P(A) = \frac{ \# \left \{ F^{*}(A)_{b} > F(A)^{obs} \right \} } {nResamplings+1}

Low p-values denote non-random selection of columns for a given bicluster. Large p-values show that in other columns for a given set of genes in the bicluster structure is similar. Hence, bicluster columns were just randomly picked by an algorithm for a set of co-regulated genes.

Value

bootstrapFstats

matrix with two columns, containing values of bootstrap F-statistics. The first column corresponds to row, the second column corresponds to column.

observedFstatRow

observed F-statistics for the row effect

observedFstatCol

observed F-statistics for the column effect

bootstrapPvalueRow

bootstrap p value for row effect

bootstrapPvalueCol

bootstrap p value for column effect

Author(s)

Tatsiana KHAMIAKOVA [e-mail]

See Also

diagnosticTest, diagnosticPlot2, diagnosticPlot, computeObservedFstat, ChiaKaruturi

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)

#Run boosotrap procedure:
Bootstrap <- diagnoseColRow(x=xmat, bicResult = plaidmab, number = 1, nResamplings = 999,
  replace = TRUE)
diagnosticPlot(bootstrapOutput = Bootstrap) 	# plotting distribution of bootstrap replicates