PLNseq: a multivariate Poisson lognormal distribution for high-throughput matched RNA-sequencing read count data

• Published in: Statistics in medicine Vol. 34; no. 9; pp. 1577 - 1589
• Main Authors: Zhang, Hong, Xu, Jinfeng, Jiang, Ning, Hu, Xiaohua, Luo, Zewei
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 30.04.2015; Wiley Subscription Services, Inc
• Subjects: Algorithms; Analysis of Variance; Base Sequence; Biostatistics; Biotechnology; Computer Simulation; differential expression analysis; Gene expression; Gene Expression Profiling; High-Throughput Nucleotide Sequencing - methods; Humans; Likelihood Functions; Lung Neoplasms - genetics; matched samples; Multivariate Analysis; Normal distribution; Parameter estimation; Poisson Distribution; Poisson lognormal model; Ribonucleic acid; RNA; RNA-seq; ROC Curve; Sequence Analysis, RNA - methods; RNA-seq; Poisson lognormal model; differential expression analysis; matched samples
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.6449

High‐throughput RNA‐sequencing (RNA‐seq) technology provides an attractive platform for gene expression analysis. In many experimental settings, RNA‐seq read counts are measured from matched samples or taken from the same subject under multiple treatment conditions. The induced correlation therefore should be evaluated and taken into account in deriving tests of differential expression. We proposed a novel method ‘PLNseq’, which uses a multivariate Poisson lognormal distribution to model matched read count data. The correlation is directly modeled through Gaussian random effects, and inferences are made by likelihood methods. A three‐stage numerical algorithm is developed to estimate unknown parameters and conduct differential expression analysis. Results using simulated data demonstrate that our method performs reasonably well in terms of parameter estimation, DE analysis power, and robustness. PLNseq also has better control of FDRs than the benchmarks edgeR and DESeq2 in the situations where the correlation is different across the genes but can still be accurately estimated. Furthermore, direct evaluation of correlation through PLNseq enables us to develop a new and more powerful test for DE analysis. Application to a lung cancer study is provided to illustrate the practical utilities of our method. An R package implementing the method is also publicly available. Copyright © 2015 John Wiley & Sons, Ltd.