Gene set meta-analysis with Quantitative Set Analysis for Gene Expression (QuSAGE).

• Main Authors: Hailong Meng, Gur Yaari, Christopher R Bolen, Stefan Avey, Steven H Kleinstein
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2019-04-01
• Series: PLoS Computational Biology
• Online Access: https://doi.org/10.1371/journal.pcbi.1006899

Small sample sizes combined with high person-to-person variability can make it difficult to detect significant gene expression changes from transcriptional profiling studies. Subtle, but coordinated, gene expression changes may be detected using gene set analysis approaches. Meta-analysis is another approach to increase the power to detect biologically relevant changes by integrating information from multiple studies. Here, we present a framework that combines both approaches and allows for meta-analysis of gene sets. QuSAGE meta-analysis extends our previously published QuSAGE framework, which offers several advantages for gene set analysis, including fully accounting for gene-gene correlations and quantifying gene set activity as a full probability density function. Application of QuSAGE meta-analysis to influenza vaccination response shows it can detect significant activity that is not apparent in individual studies.