BiSEp (version 1.0)

BISEP: BISEP: Bimodality in Gene Expression data.

Description

Detects bimodality and non-normality in all genes across the dataset.

Usage

BISEP(
	data = data, 
	confC = confC
	)

Arguments

data

This should be a log2 gene expression matrix with genes as rownames and samples as column names. Suitable for gene expression data from any platform - NGS datasets should be RPKM or RSEM values.

confC

The confidence with which you would like to call bimodality in the gene expression dataset. Either 'high' 'med' or 'low' can be input.

Value

• A list containing two matrices. Matrix 1 contains the output of the BISEP algorithm - including the midpoint of the bimodal distribution and the associated p value. Matrix 2 contains the output from the BI algorithm - including the delta, pi and BI values.

Details

The lower confidence calls will dramatically affect the number of gene pairs that the tool produces and increase the false positive rate. The tool will take approximately 10 minutes to run a 5,000 row and 200 column input matrix using a 'medium,' confidence interval.

Examples

data(BIGEE_example_input)
outBISEP <- BISEP(data=BIGEE_example_input, confC='high')