Improved Performance of Gene Set Analysis on Genome-Wide Transcriptomics Data When Using Gene Activity State Estimates

Abstract

Gene set analysis methods continue to be a popular and powerful method of evaluating genome-wide transcriptomics data. These approach require a priori grouping of genes into biologically meaningful sets, and then conducting downstream analyses at the set (instead of gene) level of analysis. Gene set analysis methods have been shown to yield more powerful statistical conclusions than single-gene analyses due to both reduced multiple testing penalties and potentially larger observed effects due to the aggregation of effects across multiple genes in the set. Traditionally, gene set analysis methods have been applied directly to normalized, log-transformed, transcriptomics data. Recently, efforts have been made to transform transcriptomics data to scales yielding more biologically interpretable results. For example, recently proposed models transform log-transformed transcriptomics data to a confidence metric (ranging between 0 and 100%) that a gene is active (roughly speaking, that the gene product is part of an active cellular mechanism). In this manuscript, we demonstrate, on both real and simulated transcriptomics data, that tests for differential expression between sets of genes using are typically more powerful when using gene activity state estimates as opposed to log-transformed gene expression data. Our analysis suggests further exploration of techniques to transform transcriptomics data to meaningful quantities for improved downstream inference.

Improvements to Bayesian Gene Activity State Estimation from Genome-Wide Transcriptomics Data

“…Furthermore, many additional refinements are possible including the incorporation of additional biological information into the Bayesian model (e.g., cross-species gene orthology, metabolic pathway information, etc.). Ultimately, these gene activity estimates can be used in multiple downstream applications including gene set analysis [6] and metabolic flux modeling, among others. Software for the methods illustrated here is available as supplemental files to this manuscript and found here: http://www.dordt.edu/statgen.…”

“…Unlike previous methods, the estimated mixture distribution parameters can be National Science Foundation Grant MCB-1330734 used to yield a posterior probability aij ∈ [0,1], that gene i is active in condition j. Recently, we further demonstrated that use of gene activity estimates outperformed the use of raw expression data when conducting gene-set analysis approaches to test for differential gene expression [6]. The promising results of the MultiMM method, however, are tempered somewhat by at least three significant limitations.…”