Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding...

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) generates vast gene expression datasets.
• Differential expression analysis is crucial for identifying cell markers and downstream analyses.

Purpose of the Study:

• To critically discuss statistical principles of differential expression analysis methods for scRNA-seq data.
• To categorize existing approaches and identify limitations and future challenges.

Main Methods:

• Categorization of differential expression analysis methods into six classes: generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric.
• Critical discussion of the statistical principles and limitations specific to each class.

Main Results:

• Identification of six major classes of statistical approaches for scRNA-seq differential expression analysis.
• Discussion of how limitations in one class are addressed by others.
• Highlighting of current challenges in scRNA-seq data analysis.

Conclusions:

• Provides a guide for researchers to select appropriate differential expression analysis methods for scRNA-seq data.
• Emphasizes methodological challenges to maximize the benefits of single-cell technology.
• Suggests areas for developing innovative future approaches.