RT Journal Article
SR Electronic
T1 Differential Expression Analysis for RNAseq using Poisson Mixed Models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 073403
DO 10.1101/073403
A1 Shiquan Sun
A1 Michelle Hood
A1 Laura Scott
A1 Qinke Peng
A1 Sayan Mukherjee
A1 Jenny Tung
A1 Xiang Zhou
YR 2017
UL http://biorxiv.org/content/early/2017/03/14/073403.abstract
AB 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 data sets 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.