Bayesian Joint Analysis of Gene Expression Data and Gene Functional Annotations

Xinlei Wang, Min Chen, Arkady B. Khodursky, Guanghua Xiao

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Identifying which genes and which gene sets are differentially expressed (DE) under two experimental conditions are both key questions in microarray analysis. Although closely related and seemingly similar, they cannot replace each other, due to their own importance and merits in scientific discoveries. Existing approaches have been developed to address only one of the two questions. Further, most of the methods for detecting DE genes purely rely on gene expression analysis, without using the information about gene functional grouping. Methods for detecting altered gene sets often use a two-step procedure, of which the first step conducts differential expression analysis using expression data only, and the second step takes results from the first step and tries to examine whether each predefined gene set is overrepresented by DE genes through some testing procedure. Such a sequential manner in analysis might cause information loss by just focusing on summary results without using the entire expression data in the second step. Here, we propose a Bayesian joint modeling approach to address the two key questions in parallel, which incorporates the information of functional annotations into expression data analysis and meanwhile infer the enrichment of functional groups. Simulation results and analysis of experimental data obtained for E. coli show improved statistical power of our integrated approach in both identifying DE genes and altered gene sets, when compared to conventional methods.

Original languageEnglish (US)
Pages (from-to)300-318
Number of pages19
JournalStatistics in Biosciences
Volume4
Issue number2
DOIs
StatePublished - Nov 1 2012

Keywords

  • Bayesian hierarchical models
  • Co-expression
  • Differentially expressed genes
  • Down-regulated
  • Functional categories
  • Functional groups
  • Gene expression
  • Gene set enrichment
  • Joint modeling
  • Pathway analysis
  • Up-regulated

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