PairedFB: a full hierarchical Bayesian model for paired RNA-seq data with heterogeneous treatment effects.

ABSTRACT:

Motivation

Several methods have been proposed for the paired RNA-seq analysis. However, many of them do not consider the heterogeneity in treatment effect among pairs that can naturally arise in real data. In addition, it has been reported in literature that the false discovery rate (FDR) control of some popular methods has been problematic. In this paper, we present a full hierarchical Bayesian model for the paired RNA-seq count data that accounts for variation of treatment effects among pairs and controls the FDR through the posterior expected FDR.

Results

Our simulation studies show that most competing methods can have highly inflated FDR for small to moderate sample sizes while PairedFB is able to control FDR close to the nominal levels. Furthermore, PairedFB has overall

SUBMITTER: Bian Y 

PROVIDER: S-EPMC7963077 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature