scPAS

scPAS integrates bulk data to identify phenotype-associated cell subpopulations within single-cell data using a network-regularized sparse regression model and permutation tests.

Key Features:

• Network-regularized sparse regression model: Quantifies the association between each cell in single-cell data and a phenotype.
• Permutation test: Estimates the significance of cell-phenotype associations to identify phenotype-associated cell subpopulations.
• Operational efficiency: Exhibits superior computational performance on large datasets compared to other methods.

Scientific Applications:

• Cancer subpopulation identification: Identifies phenotype-associated cell subpopulations in breast carcinoma and ovarian cancer single-cell datasets.
• Cardiovascular disease analysis: Detects phenotype-associated cell subpopulations in atherosclerosis single-cell data.
• Spatial transcriptomics: Applies to spatial transcriptomics data from multiple cancers.

Methodology:

scPAS employs a network-regularized sparse regression model to quantify associations between each cell and a phenotype, followed by permutation tests to assess significance.