NoiseScree {BiBitR}

Noise Scree Plots

Description

Extract patterns from either a Biclust or BiBitWorkflow object (see Details) and plot the Noise Scree plot (same as plot 4 in BiBitWorkflow). Additionally, if FisherResult is available (from RowTest_Fisher), this info will be added to the plot.

Usage

NoiseScree(result, matrix, type = c("Added", "Total"), pattern = NULL,
  noise_select = 0, alpha = 0.05)

Arguments

result

A Biclust or BiBitWorkflow Object.

matrix

Accompanying binary data matrix which was used to obtain result.

type

Either "Added" or "Total". Should the noise level be plotted against the number of added rows (at that noise level) or the total number of rows (up to that noise level)?

pattern

Numeric vector for which patterns the noise scree plot should be drawn (default = all patterns).

noise_select

Should an automatic noise selection be applied and drawn (blue vertical line) on the plot? (Using ad hoc method to find the elbow/kink in the Noise Scree plots)

• noise_select=0: No noise selection is applied and no line is drawn (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.

alpha

If info from the Fisher Exact test is available, which significance level should be used to in the plot (Noise versus Significant Fisher Exact Test rows). (default=0.05)

Details

Biclust S4 Object

Using the column patterns of the Biclust result, the noise level is plotted versus the number of "Total" or "Added" rows.

BiBitWorkflow S3 Object

The merged column patterns (after cutting the hierarchical tree) are extracted from the BiBitWorkflow object, namely the $info$MergedColPatterns slot. These patterns are used to plot the noise level versus the number of "Total" or "Added" rows.

If information on the Fisher Exact Test is available, then this info will added to the plot (noise level versus significant rows).

Value

NULL

Author(s)

Ewoud De Troyer

Examples

## Not run: 
## 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=0.2,cut_type="number",cut_pm=4)
# Make Noise Scree Plot - Default
NoiseScree(result=out,matrix=mat,type="Added")
NoiseScree(result=out,matrix=mat,type="Total")
# Make Noise Scree Plot - Use Automatic Noies Selection
NoiseScree(result=out,matrix=mat,type="Added",noise_select=2)
NoiseScree(result=out,matrix=mat,type="Total",noise_select=2)

## Apply RowTest_Fisher on BiBitWorkflow Object ##
out2 <- RowTest_Fisher(result=out,matrix=mat)
# Fisher output is added to "NoiseScree" plot
NoiseScree(result=out2,matrix=mat,type="Added")
NoiseScree(result=out2,matrix=mat,type="Total")

## End(Not run)