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Correcting Transcription Factor Gene Sets for Copy Number and Promoter Methylation Variations
Author(s) -
Rathi Komal S.,
Gaykalova Daria A.,
Hennessey Patrick,
Califano Joseph A.,
Ochs Michael F.
Publication year - 2014
Publication title -
drug development research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.582
H-Index - 60
eISSN - 1098-2299
pISSN - 0272-4391
DOI - 10.1002/ddr.21220
Subject(s) - biology , gene , genetics , dna methylation , epigenetics , promoter , gene silencing , transcription factor , enhancer , regulation of gene expression , computational biology , gene expression , methylation
Preclinical ResearchGene set analysis provides a method to generate statistical inferences across sets of linked genes, primarily using high‐throughput expression data. Common gene sets include biological pathways, operons, and targets of transcriptional regulators. In higher eukaryotes, especially when dealing with diseases with strong genetic and epigenetic components such as cancer, copy number loss and gene silencing through promoter methylation can eliminate the possibility that a gene is transcribed. This, in turn, can adversely affect the estimation of transcription factor or pathway activity from a set of target genes, as some of the targets may not be responsive to transcriptional regulation. Here we introduce a simple filtering approach that removes genes from consideration if they show copy number loss or promoter methylation, and demonstrate the improvement in inference of transcription factor activity in a simulated dataset based on the background expression observed in normal head and neck tissue.