BiBitR (version 0.3.1)

UpdateBiclust_RowNoise: Update a Biclust or BiBitWorkflow Object with a new Noise Level

Description

Apply a new noise level on a Biclust object result or BiBitWorkflow result. See Details on how both objects are affected.

Usage

UpdateBiclust_RowNoise(result, matrix, noise = 0.1, noise_select = 0,
  removeBC = FALSE)

Arguments

result

A Biclust or BiBitWorkflow Object.

matrix

Accompanying binary data matrix which was used to obtain result.

noise

The new noise level which should be used in the rows of the biclusters. (default=0.1, namely allow 10% noise.).

• noise=0: No noise allowed.

• 0<noise<1: The noise parameter will be a noise percentage. The number of allowed 0's in a row in the bicluster will depend on the column size of the bicluster. More specifically zeros_allowed = ceiling(noise * columnsize). For example for noise=0.10 and a bicluster column size of 5, the number of allowed 0's would be 1.

• noise>=1: The noise parameter will be the number of allowed 0's in a row in the bicluster independent from the column size of the bicluster. In this noise option, the noise parameter should be an integer.

noise_select

Should the allowed noise level be automatically selected for each pattern? (Using ad hoc method to find the elbow/kink in the Noise Scree plots)

• noise_select=0: Do NOT automatically select the noise levels. Use the the noise level given in the noise parameter (default)

• noise_select=1: Using the Noise Scree plot (with 'Added Rows' on the y-axis), find the noise level where the current number of added rows at this noise level is larger than the mean of 'added rows' at the lower noise levels. After locating this noise level, lower the noise level by 1. This is your automatically selected elbow/kink and therefore your noise level.

• noise_select=2: Applies the same steps as for noise_select=1, but instead of decreasing the noise level by only 1, keep decreasing the noise level until the number of added rows isn't decreasing anymore either.

removeBC

(Only applicable when result is a Biclust object) Logical value if after applying a new noise level, duplicate and non-maximal BC's should be deleted.

Value

A Biclust or BiBitWorkflow Object (See Details)

Details

Biclust S4 Object

Using the column patterns of the Biclust result, new grows are grown using the inputted noise level. The removeBC parameter decides if duplicate and non-maximal BC's should be deleted. Afterwards a new Biclust S4 object is returned with the new biclusters.

BiBitWorkflow S3 Object

The merged column patterns (after cutting the hierarchical tree) are extracted from the BiBitWorkflow object, namely the $info$MergedColPatterns slot. Afterwards, using the new noise level, new rows are grown and the returned object is an updated BiBitWorkflow object. (e.g. The final Biclust slot, MergedNoiseThresholds, coverage,etc. are updated)

Examples

## Prepare some data ##
set.seed(254)
mat <- matrix(sample(c(0,1),5000*50,replace=TRUE,prob=c(1-0.15,0.15)),
              nrow=5000,ncol=50)
mat[1:200,1:10] <- matrix(sample(c(0,1),200*10,replace=TRUE,prob=c(1-0.9,0.9)),
                          nrow=200,ncol=10)
mat[300:399,6:15] <- matrix(sample(c(0,1),100*10,replace=TRUE,prob=c(1-0.9,0.9)),
                            nrow=100,ncol=10)
mat[400:599,21:30] <- matrix(sample(c(0,1),200*10,replace=TRUE,prob=c(1-0.9,0.9)),
                             nrow=200,ncol=10)
mat[700:799,29:38] <- matrix(sample(c(0,1),100*10,replace=TRUE,prob=c(1-0.9,0.9)),
                             nrow=100,ncol=10)
mat <- mat[sample(1:5000,5000,replace=FALSE),sample(1:50,50,replace=FALSE)]

## Apply BiBitWorkflow ##
out <- BiBitWorkflow(matrix=mat,minr=50,minc=5,noise=0.1,cut_type="number",cut_pm=4)
summary(out$Biclust)

## Update Rows with new noise level on Biclust Obect -> returns Biclust Object ##
out_new <- UpdateBiclust_RowNoise(result=out$Biclust,matrix=mat,noise=0.3)
summary(out_new)
out_new@info$Noise.Threshold # New Noise Levels

## Update Rows with new noise level on BiBitWorkflow Obect -> returns BiBitWorkflow Object ##
out_new2 <- UpdateBiclust_RowNoise(result=out,matrix=mat,noise=0.2)
summary(out_new2$Biclust)
out_new2$info$MergedNoiseThresholds # New Noise Levels