A Bayesian methodology for detecting targeted genes under two related experiments

Naveen K. Bansal*, Hongmei Jiang, Prachi Pradeep

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Many gene expression data are based on two experiments where the gene expressions of the targeted genes under both experiments are correlated. We consider problems in which objectives are to find genes that are simultaneously upregulated/downregulated under both experiments. A Bayesian methodology is proposed based on directional multiple hypotheses testing. We propose a false discovery rate specific to the problem under consideration, and construct a Bayes rule satisfying a false discovery rate criterion. The proposed method is compared with a traditional rule through simulation studies. We apply our methodology to two real examples involving microRNAs; where in one example the targeted genes are simultaneously downregulated under both experiments, and in the other the targeted genes are downregulated in one experiment and upregulated in the other experiment. We also discuss how the proposed methodology can be extended to more than two experiments.

Original languageEnglish (US)
Pages (from-to)3362-3375
Number of pages14
JournalStatistics in Medicine
Volume34
Issue number25
DOIs
StatePublished - Jan 1 2015

Keywords

  • Bayes rule
  • EM algorithm
  • false discovery rate
  • gene expression
  • microRNA
  • multiple hypotheses

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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