The Annals of Applied Statistics

Bayesian latent hierarchical model for transcriptomic meta-analysis to detect biomarkers with clustered meta-patterns of differential expression signals

Zhiguang Huo, Chi Song, and George Tseng

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Abstract

Due to the rapid development of high-throughput experimental techniques and fast-dropping prices, many transcriptomic datasets have been generated and accumulated in the public domain. Meta-analysis combining multiple transcriptomic studies can increase the statistical power to detect disease-related biomarkers. In this paper we introduce a Bayesian latent hierarchical model to perform transcriptomic meta-analysis. This method is capable of detecting genes that are differentially expressed (DE) in only a subset of the combined studies, and the latent variables help quantify homogeneous and heterogeneous differential expression signals across studies. A tight clustering algorithm is applied to detected biomarkers to capture differential meta-patterns that are informative to guide further biological investigation. Simulations and three examples, including a microarray dataset from metabolism-related knockout mice, an RNA-seq dataset from HIV transgenic rats and cross-platform datasets from human breast cancer are used to demonstrate the performance of the proposed method.

Article information

Source
Ann. Appl. Stat., Volume 13, Number 1 (2019), 340-366.

Dates
Received: September 2016
Revised: February 2018
First available in Project Euclid: 10 April 2019

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1554861652

Digital Object Identifier
doi:10.1214/18-AOAS1188

Mathematical Reviews number (MathSciNet)
MR3937432

Keywords
Transcriptomic differential analysis meta-analysis Bayesian hierarchical model Dirichlet process

Citation

Huo, Zhiguang; Song, Chi; Tseng, George. Bayesian latent hierarchical model for transcriptomic meta-analysis to detect biomarkers with clustered meta-patterns of differential expression signals. Ann. Appl. Stat. 13 (2019), no. 1, 340--366. doi:10.1214/18-AOAS1188. https://projecteuclid.org/euclid.aoas/1554861652


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Supplemental materials

  • Supplementary information. Additional tables, figures, and text.
  • Supplementary Excel file 1. Pathway information for the mouse metabolism application.
  • Supplementary Excel file 2. Pathway information for the HIV transgenic rat application.