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Differential expression analysis for RNAseq using Poisson mixed models.

Shiquan Sun1,2, Michelle Hood2, Laura Scott2,3

  • 1Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P.R. China.

Nucleic Acids Research
|April 4, 2017
PubMed
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This summary is machine-generated.

This study introduces a new Poisson mixed model for analyzing gene expression data from RNA sequencing (RNAseq) studies. The method improves the accuracy of identifying differentially expressed genes, especially when samples are related or have hidden confounders.

Area of Science:

  • Genomics
  • Statistical genetics
  • Bioinformatics

Background:

  • Identifying differentially expressed (DE) genes using RNA sequencing (RNAseq) is a common genomic analysis.
  • Existing methods struggle with over-dispersed read counts and sample non-independence due to relatedness or population structure.
  • Simple hierarchical Poisson models do not adequately address sample non-independence.

Purpose of the Study:

  • To develop a novel statistical model for RNAseq DE analysis that accounts for both over-dispersion and sample non-independence.
  • To create an efficient algorithm for scalable inference in complex genomic datasets.
  • To improve the statistical power and accuracy of DE gene identification in challenging datasets.

Main Methods:

  • A Poisson mixed model incorporating two random effects terms was developed.

Related Experiment Videos

  • The model addresses independent over-dispersion and sample non-independence.
  • A scalable sampling-based inference algorithm using a latent variable representation of the Poisson distribution was implemented.

    • Main Results:

    • The proposed method controls type I error rates effectively in simulations.
    • It demonstrates superior statistical power compared to existing methods, particularly in datasets with related individuals or hidden confounders.
    • Application to real datasets confirmed increased power, with the most significant gains in larger samples.

    Conclusions:

    • The new Poisson mixed model offers a robust approach for RNAseq DE analysis.
    • It enhances power and controls error rates in the presence of sample non-independence.
    • The method is implemented in the MACAU software, available for broader use.