biostudies-literatureGan DU.S. Department of DefenseNCI NIH HHSNational Institutes of HealthNIH HHS119https://www.ebi.ac.uk/biostudies/studies/S-EPMC11089691biostudies-literatureUnknown25(1)Numerous algorithms have been proposed to identify cell types in single-cell RNA sequencing data, yet a fundamental problem remains: determining associations between cells and phenotypes such as cancer. We develop SCIPAC, the first algorithm that quantitatively estimates the association between each cell in single-cell data and a phenotype. SCIPAC also provides a p-value for each association and applies to data with virtually any type of phenotype. We demonstrate SCIPAC's accuracy in simulated data. On four real cancerous or noncancerous datasets, insights from SCIPAC help interpret the data and generate new hypotheses. SCIPAC requires minimum tuning and is computationally very fast.Genome biologySCIPAC: quantitative estimation of cell-phenotype associations.PMC11089691W81XWH2110432R01CA252878R01CA280097R01 CA248033R01 CA252878R01 CA280097Li JZhu YLu XGan DfalseSCIPAC: quantitative estimation of cell-phenotype associations.Numerous algorithms have been proposed to identify cell types in single-cell RNA sequencing data, yet a fundamental problem remains: determining associations between cells and phenotypes such as cancer. We develop SCIPAC, the first algorithm that quantitatively estimates the association between each cell in single-cell data and a phenotype. SCIPAC also provides a p-value for each association and applies to data with virtually any type of phenotype. We demonstrate SCIPAC's accuracy in simulated data. On four real cancerous or noncancerous datasets, insights from SCIPAC help interpret the data and generate new hypotheses. SCIPAC requires minimum tuning and is computationally very fast.2024-01-01T00:00:00Z2024 May2026-07-02T03:13:33.255Z2026-07-02T03:09:01.773ZS-EPMC110896913874118310.1186/s13059-024-03263-1