bcGST—an interactive bias-correction method to identify over-represented gene-sets in boutique arrays

• Published in: Bioinformatics 2019-04, Vol.35 (8), p.1350-1357
• Main Authors: Wang, Kevin Y X, Menzies, Alexander M, Silva, Ines P, Wilmott, James S, Yan, Yibing, Wongchenko, Matthew, Kefford, Richard F, Scolyer, Richard A, Long, Georgina V, Tarr, Garth, Mueller, Samuel, Yang, Jean Y H
• Format: Article
• Language: English
• Subjects: Computational Biology; Gene Expression Profiling; Gene Ontology; Genome; Molecular Sequence Annotation; Software
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/bty783

Abstract Motivation Gene annotation and pathway databases such as Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes are important tools in Gene-Set Test (GST) that describe gene biological functions and associated pathways. GST aims to establish an association relationship between a gene-set of interest and an annotation. Importantly, GST tests for over-representation of genes in an annotation term. One implicit assumption of GST is that the gene expression platform captures the complete or a very large proportion of the genome. However, this assumption is neither satisfied for the increasingly popular boutique array nor the custom designed gene expression profiling platform. Specifically, conventional GST is no longer appropriate due to the gene-set selection bias induced during the construction of these platforms. Results We propose bcGST, a bias-corrected GST by introducing bias-correction terms in the contingency table needed for calculating the Fisher’s Exact Test. The adjustment method works by estimating the proportion of genes captured on the array with respect to the genome in order to assist filtration of annotation terms that would otherwise be falsely included or excluded. We illustrate the practicality of bcGST and its stability through multiple differential gene expression analyses in melanoma and the Cancer Genome Atlas cancer studies. Availability and implementation The bcGST method is made available as a Shiny web application at http://shiny.maths.usyd.edu.au/bcGST/. Supplementary information Supplementary data are available at Bioinformatics online.