SAREV: A review on statistical analytics of single-cell RNA sequencing data

Area of Science:

• Genomics
• Transcriptomics
• Epigenomics

Background:

• Bulk RNA sequencing (RNA-seq) provides averaged cellular data.
• Single-cell RNA sequencing (scRNA-seq) offers transcriptomic information at single-cell resolution.
• scRNA-seq data requires advanced statistical analysis methods.

Purpose of the Study:

• To review recently developed statistical methods for scRNA-seq data analysis.
• To guide researchers in scRNA-seq computational and statistical research.
• To highlight available free software tools for scRNA-seq data.

Main Methods:

• Quality control of scRNA-seq data.
• Differential gene expression analysis.
• Network analysis of scRNA-seq data.

Main Results:

• Identification of popular statistical methods for scRNA-seq analysis.
• Overview of computational tools for scRNA-seq data.
• Discussion of methods from data processing to network analysis.

Conclusions:

• Effective analysis of scRNA-seq data necessitates specialized statistical approaches.
• This review provides a resource for researchers navigating scRNA-seq data analysis.
• The field benefits from ongoing development of statistical methods and software.