scPAS: single-cell phenotype-associated subpopulation identifier.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Single-cell sequencing reveals tissue heterogeneity, but linking cell subpopulations to disease phenotypes is challenging.
• Current methods struggle to effectively integrate diverse data types for robust association analysis.

Purpose of the Study:

• Introduce scPAS, a novel bioinformatics tool for identifying phenotype-associated cell subpopulations in single-cell data.
• Develop a method to integrate bulk and single-cell data for enhanced disease association discovery.

Main Methods:

• scPAS utilizes a network-regularized sparse regression model to quantify cell-phenotype associations.
• A permutation test is employed to assess the statistical significance of identified associations.
• The tool integrates single-cell RNA sequencing (scRNA-seq) with bulk data and spatial transcriptomics.

Main Results:

• scPAS accurately identifies phenotype-associated cell subpopulations across simulated and real-world datasets (breast carcinoma, ovarian cancer, atherosclerosis).
• Demonstrated broad applicability across various cancer types using spatial transcriptomics data.
• Evaluations show scPAS offers superior operational efficiency on large datasets compared to existing methods.

Conclusions:

• scPAS provides an accurate, flexible, and efficient solution for identifying disease-associated cell subpopulations.
• The tool facilitates deeper insights into tissue heterogeneity and its role in disease phenotypes.
• An open-source R package is available for widespread research adoption.