Journal Article

Down-weighting overlapping genes improves gene set analysis

Draghici, Sorin, Tarca, Adi Laurentiu, Bhatti, Gaurav, Romero, Roberto

2012

Overview

Background The identification of gene sets that are significantly impacted in a given condition based on microarray data is a crucial step in current life science research. Most gene set analysis methods treat genes equally, regardless how specific they are to a given gene set. Results In this work we propose a new gene set analysis method that computes a gene set score as the mean of absolute values of weighted moderated gene t-scores. The gene weights are designed to emphasize the genes appearing in few gene sets, versus genes that appear in many gene sets. We demonstrate the usefulness of the method when analyzing gene sets that correspond to the KEGG pathways, and hence we called our method P athway A nalysis with D own-weighting of O verlapping G enes ( PADOG ). Unlike most gene set analysis methods which are validated through the analysis of 2-3 data sets followed by a human interpretation of the results, the validation employed here uses 24 different data sets and a completely objective assessment scheme that makes minimal assumptions and eliminates the need for possibly biased human assessments of the analysis results. Conclusions PADOG significantly improves gene set ranking and boosts sensitivity of analysis using information already available in the gene expression profiles and the collection of gene sets to be analyzed. The advantages of PADOG over other existing approaches are shown to be stable to changes in the database of gene sets to be analyzed. PADOG was implemented as an R package available at: http://bioinformaticsprb.med.wayne.edu/PADOG/ or http://www.bioconductor.org .

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Analysis / Bioinformatics / Biomedical and Life Sciences / Colorectal cancer / Computational Biology - methods / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Data processing / Databases, Genetic / DNA microarrays / Experiments / Gene expression / Gene Expression Profiling - methods / Gene Regulatory Networks / Gene set analysis / Genes / Genes, Overlapping / Genetics / Humans / Information retrieval / Life Sciences / Metabolic Networks and Pathways / Methods / Microarrays / Oligonucleotide Array Sequence Analysis - methods / Overlapping gene sets / Overlapping genes / Pathway analysis / Physiological aspects / R&D / Research & development / Research Article / Sequence analysis (applications) / Statistics as Topic