DiSC: a statistical tool for fast differential expression analysis of individual-level single-cell RNA-seq data.

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) enables detailed characterization of cellular heterogeneity.
• Increasing scRNA-seq data necessitates efficient methods for differential expression (DE) analysis that account for individual variability.

Purpose of the Study:

• To introduce DiSC, a novel method for individual-level DE analysis in scRNA-seq data.
• To develop a statistically powerful and computationally efficient tool for analyzing large-scale scRNA-seq datasets.

Main Methods:

• DiSC extracts multiple distributional characteristics for joint testing.
• A flexible permutation testing framework is employed to control the false discovery rate (FDR).
• The method is implemented in the R software package 'SingleCellStat'.

Main Results:

• DiSC effectively controls FDR and demonstrates high statistical power in simulations.
• The method is computationally efficient, approximately 100 times faster than existing state-of-the-art approaches.
• DiSC identified DE genes associated with COVID-19 severity and Alzheimer's disease, with findings supported by literature.
• DiSC successfully identified more DE markers than traditional methods in cytometry by time-of-flight data.

Conclusions:

• DiSC provides an efficient and powerful solution for individual-level DE analysis in scRNA-seq.
• The DiSC framework is robust and adaptable to various single-cell data types.
• The DiSC R package and replication code are publicly available for broader scientific use.