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

1 | ```
diagnoseColRow(x, bicResult, number, nResamplings, replace = TRUE)
``` |

`x` |
data matrix, which |

`bicResult` |
object of class |

`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) |

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) = F^*(A)_b > F(A)^{obs} /(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.

`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 |

Tatsiana KHAMIAKOVA tatsiana.khamiakova@uhasselt.be

`diagnosticPlot`

, `computeObservedFstat`

, `ChiaKaruturi`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
#---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)
#Run boosotrap procedure:
Bootstrap <- diagnoseColRow(x=xmat, bicResult = plaidmab, number = 1, nResamplings = 999,
replace = TRUE)
diagnosticPlot(bootstrapOutput = Bootstrap) # plotting distribution of bootstrap replicates
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.