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

Yuanyuan Bian | Zendy; Chong He | Zendy; Jie Hou | Zendy; Jianlin Cheng | Zendy; Jing Qiu | Zendy

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

Author(s) -

Yuanyuan Bian,

Chong He,

Jie Hou,

Jianlin Cheng,

Jing Qiu

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty731

Subject(s) - false discovery rate , bayesian probability , sample size determination , computer science , ranking (information retrieval) , multiple comparisons problem , sample (material) , bayesian hierarchical modeling , statistics , data mining , computational biology , bayesian inference , mathematics , artificial intelligence , biology , gene , genetics , chemistry , chromatography

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.

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