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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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Single-gene negative binomial regression models for RNA-Seq data with higher-order asymptotic inference.

¹Department of Statistics, Oregon State University, Corvallis, OR 97331, USA.

Statistics and Its Interface

|January 3, 2017

Summary

This study introduces a single-gene negative binomial (NB) regression approach for RNA-Seq data. It shows that accurate statistical inferences are possible even with small sample sizes and unknown dispersion parameters.

Keywords:

92D20 Extra-Poisson variation Higher-order asymptotics Negative binomial Overdispersion Power-robustness Primary 62P10 RNA-Seq Regression

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Area of Science:

Bioinformatics
Statistical Genetics
Computational Biology

Background:

RNA-Seq read counts are often analyzed using negative binomial (NB) regression models.
Current methods typically model dispersion parameters across many genes simultaneously.
This study explores an alternative, gene-by-gene analysis approach.

Purpose of the Study:

To investigate the validity and utility of fitting NB regression models to individual genes separately.
To assess the estimation of NB dispersion parameters on a per-gene basis.
To provide a reference for understanding dispersion modeling trade-offs and identify potential practical applications.

Main Methods:

Fitting negative binomial (NB) regression models to individual RNA-Seq genes.
Estimating the NB dispersion parameter for each gene independently.
Employing higher-order asymptotic techniques for statistical inference.

Main Results:

Accurate inferences regarding regression coefficients can be achieved with the single-gene NB model.
This holds true even when the dispersion parameter is unknown and sample sizes are small.
The approach offers a benchmark for evaluating dispersion-modeling strategies.

Conclusions:

Single-gene NB regression is a viable approach for RNA-Seq data analysis.
It provides a robust alternative or complement to multi-gene dispersion modeling.
This method can be particularly useful with moderate sample sizes or when dispersion models show issues.