Length Bias Correction in Gene Ontology Enrichment Analysis Using Logistic Regression

• Published in: PloS one Vol. 7; no. 10; p. e46128
• Main Authors: Mi, Gu, Di, Yanming, Emerson, Sarah, Cumbie, Jason S., Chang, Jeff H.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.10.2012; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Arabidopsis - genetics; Bias; Bioinformatics; Biological effects; Biology; Cellular biology; Computer Simulation; Contingency tables; Enrichment; Gene expression; Gene Expression Profiling - statistics & numerical data; Gene sequencing; Genes; Genomes; Humans; Logistic Models; Male; Mathematical models; Mathematics; Oligonucleotide Array Sequence Analysis - statistics & numerical data; Ontology; Plant pathology; Power; Prostate cancer; Prostatic Neoplasms - genetics; Regression analysis; Regression models; Ribonucleic acid; RNA; RNA - genetics; RNA sequencing; Statistical analysis; Statistical tests; Transcription; Transcription, Genetic; Vocabulary, Controlled; Oregon; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0046128

When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.