BEEM {BiSEp}R Documentation

BEEM: Bimodal Expression Exclusive with Mutation


Takes the output from the function BISEP and a discreet mutation matrix as input. The mutation matrix samples (columns) must mirror or overlap with the gene expression matrix. The data in the mutation matrix must be a discreet 'WT' or 'MUT' call based on the status of each gene with each sample. Detects mutations of genes enriched in either the high or low gene expression modes.


	sampleType=c("cell_line", "cell_line_low", "patient", "patient_low"), 



This should be the output from the BISEP function.


This should be a matrix with genes rownames and samples as column names. All cells should be made up of a discreet 'WT' or 'MUT' call. There should be overlap (by sample) with the gene expression matrix.


The type of sample being analysed. Select 'cell_line' or 'patient' for datasets with greater than ~200 samples. For datasets with less than ~200 samples, use 'cell_line_low' or 'patient_low'.


The minimum number of mutations you for a gene would consider for analysis.


Lower sample numbers have more stringent bimodality hurdles to clear in order to keep the false positive rate lower. The tool returns a percentage complete text window so the user can observe the status of the job.


A matrix containing 10 columns. Column 1 contains the bimodal genes from the expression data (gene 1) and column 2 contains the mutated candidate synthetic lethal gene pair (gene 2). Columns 3 and 4 contain the number of mutations of gene 2 in the low and high expression modes of gene 1. Column 5 contains the fishers p value that evaluates enrichment of mutation in either the high or low mode (indicated by column 10). Columns 6 and 7 contain the percentage of samples in the low and high expression modes of gene 1 that are mutated for gene 2. Columns 8 and 9 contain information on the overall size (in terms of sample) of the low and high expression modes of gene 1.


Mark Wappett

[Package BiSEp version 2.2 Index]