Open Access
December 2019 Empirical Bayes analysis of RNA sequencing experiments with auxiliary information
Kun Liang
Ann. Appl. Stat. 13(4): 2452-2482 (December 2019). DOI: 10.1214/19-AOAS1270


Finding differentially expressed genes is a common task in high-throughput transcriptome studies. While traditional statistical methods rank the genes by their test statistics alone, we analyze an RNA sequencing dataset using the auxiliary information of gene length and the test statistics from a related microarray study. Given the auxiliary information, we propose a novel nonparametric empirical Bayes procedure to estimate the posterior probability of differential expression for each gene. We demonstrate the advantage of our procedure in extensive simulation studies and a psoriasis RNA sequencing study. The companion R package calm is available at Bioconductor.


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Kun Liang. "Empirical Bayes analysis of RNA sequencing experiments with auxiliary information." Ann. Appl. Stat. 13 (4) 2452 - 2482, December 2019.


Received: 1 June 2018; Revised: 1 January 2019; Published: December 2019
First available in Project Euclid: 28 November 2019

zbMATH: 07160946
MathSciNet: MR4037437
Digital Object Identifier: 10.1214/19-AOAS1270

Keywords: Conditional density , multiple comparison , Nonparametric regression , simultaneous inference

Rights: Copyright © 2019 Institute of Mathematical Statistics

Vol.13 • No. 4 • December 2019
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