A Novel Method to Identify the Differences Between Two Single Cell Groups at Single Gene, Gene Pair, and Gene Module...

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Single-cell sequencing reveals cellular heterogeneity and enables new cell type discovery.
• Existing methods effectively address dropout imputation, cell clustering, and lineage reconstruction.
• A systemic pipeline for molecular-level comparison of single-cell clusters is lacking.

Purpose of the Study:

• To present a novel computational pipeline for comparing single-cell clusters at the molecular level.
• To enable the identification of biological mechanisms underlying differences between cell clusters and types.
• To facilitate the discovery of cell type-specific molecular mechanisms.

Main Methods:

• Development of a novel bioinformatics pipeline for single-cell cluster comparison.
• Inclusion of differential gene expression analysis.
• Incorporation of coexpression network module identification.

Main Results:

• The pipeline was applied to single-cell RNA sequencing (RNA-seq) datasets from mouse brain (Usoskin) and human pancreas (Xin).
• Significant differential genes, differential gene coexpression, and network modules were identified between cell clusters.
• The findings confirmed functional distinctions among different cell clusters.

Conclusions:

• The developed pipeline effectively reveals molecular mechanisms driving biological differences between cell clusters.
• It aids in identifying cell type-specific molecular mechanisms.
• The approach validates the functional divergence of distinct cell clusters.