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

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Comparison and evaluation of statistical error models for scRNA-seq.

Saket Choudhary¹, Rahul Satija^2,3

¹New York Genome Center, 101 Avenue of the Americas, New York, 100013, USA.

|January 19, 2022

Summary

This study evaluates statistical models for single-cell RNA sequencing (scRNA-seq) data. A negative binomial model is recommended over Poisson for genes with sufficient sequencing depth due to observed overdispersion in scRNA-seq data.

Keywords:

Differential expression Dimension reduction Feature selection Normalization Single-cell RNA-seq Variable genes

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) data exhibit heterogeneity from biological and technical sources.
Accurate preprocessing requires effective statistical modeling of count data.
Current consensus is lacking on appropriate count distributions and parameter settings for scRNA-seq analysis.

Purpose of the Study:

To evaluate the performance of different error models for scRNA-seq data.
To determine appropriate statistical distributions for modeling variation in scRNA-seq data.
To provide recommendations for scRNA-seq data preprocessing and analysis.

Main Methods:

Analysis of 59 scRNA-seq datasets covering diverse technologies, systems, and sequencing depths.
Evaluation of Poisson and negative binomial error models.
Assessment of overdispersion across genes, datasets, and biological systems.

Main Results:

A Poisson error model is suitable for sparse scRNA-seq datasets.
Evidence of overdispersion is observed for genes with sufficient sequencing depth across all biological systems.
The degree of overdispersion varies significantly with datasets, systems, and gene abundance.

Conclusions:

A negative binomial model is necessary for genes with adequate sequencing depth in scRNA-seq data.
A data-driven approach is recommended for estimating model parameters.
Recommendations are provided for modeling variation in scRNA-seq data using generalized linear models or likelihood-based methods.