A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data.

Area of Science:

• Computational biology
• Genomics
• Bioinformatics

Background:

• Cell populations dynamically change in response to environmental conditions.
• Understanding these cellular dynamics requires analyzing gene expression at the single-cell level.

Purpose of the Study:

• To develop a computational method for identifying and tracking cell type dynamics using single-cell RNA sequencing (scRNA-seq) data.
• To simultaneously identify marker genes associated with cell type changes across different conditions.

Main Methods:

• Proposed SparseDC, a novel algorithm that solves a unified optimization problem.
• Utilized single-cell RNA sequencing data measuring gene expression before and after a condition change.
• Employed computational efficiency for simultaneous identification of cell types, their changes, and marker genes.

Main Results:

• SparseDC accurately identifies cell types and traces their evolution across conditions.
• The algorithm successfully pinpoints marker genes crucial for adaptive changes.
• Demonstrated high computational efficiency and accuracy on both simulated and real-world scRNA-seq datasets.

Conclusions:

• SparseDC offers a powerful and efficient tool for dissecting cell population dynamics.
• This method advances the analysis of scRNA-seq data for understanding cellular adaptation and evolution.
• Enables simultaneous discovery of cell types, their alterations, and associated genetic markers.