title: Differential expression analysis for RNAseq using Poisson mixed models.
abstract: Identifying differentially expressed (DE) genes from RNA sequencing (RNAseq) studies is among the most common analyses in genomics. However, RNAseq DE analysis presents several statistical and computational challenges, including over-dispersed read counts and, in some settings, sample non-independence. Previous count-based methods rely on simple hierarchical Poisson models (e.g. negative binomial) to model independent over-dispersion, but do not account for sample non-independence due to relatedness, population structure and/or hidden confounders. Here, we present a Poisson mixed model with two random effects terms that account for both independent over-dispersion and sample non-independence. We also develop a scalable sampling-based inference algorithm using a latent variable representation of the Poisson distribution. With simulations, we show that our method properly controls for type I error and is generally more powerful than other widely used approaches, except in small samples (n <15) with other unfavorable properties (e.g. small effect sizes). We also apply our method to three real datasets that contain related individuals, population stratification or hidden confounders. Our results show that our method increases power in all three data compared to other approaches, though the power gain is smallest in the smallest sample (n = 6). Our method is implemented in MACAU, freely available at www.xzlab.org/software.html.
authors.affiliation: Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P.R. China.|Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Shiquan
authors.initials: S
authors.lastname: Sun
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Michelle
authors.initials: M
authors.lastname: Hood
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.|Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Laura
authors.initials: L
authors.lastname: Scott
authors.affiliation: Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P.R. China.
authors.forename: Qinke
authors.initials: Q
authors.lastname: Peng
authors.affiliation: Departments of Statistical Science, Mathematics, and Computer Science, Duke University, Durham, NC 27708, USA.
authors.forename: Sayan
authors.initials: S
authors.lastname: Mukherjee
authors.affiliation: Departments of Evolutionary Anthropology and Biology, Duke University, Durham, NC 27708, USA.|Duke University Population Research Institute, Duke University, Durham, NC 27708, USA.
authors.forename: Jenny
authors.initials: J
authors.lastname: Tung
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.|Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016014:Linear Models; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D016012:Poisson Distribution; D017423:Sequence Analysis, RNA; D012984:Software