Single-cell sequencing yields novel discoveries by distinguishing cell types, states, and lineages within the context of heterogeneous tissues. However, interpreting complex single-cell data from highly heterogeneous cell populations remains challenging. Currently, most existing single-cell data analyses focus on cell type clusters defined by unsupervised clustering methods that cannot directly link cell clusters with specific biological and clinical phenotypes. Here we present Scissor, a novel approach that utilizes disease phenotypes to identify cell subpopulations from single-cell data that are most highly associated with the given phenotype. This “phenotype-to-cell” within a single-step strategy utilizes clinical information collected from bulk assays to identify the most highly phenotype-associated cell subpopulations. When applied to a lung cancer single-cell RNA-seq (scRNA-seq) dataset, Scissor identified a subset of cells associated with worse survival as well as another cell subpopulation associated with TP53 mutation. Furthermore, in a melanoma scRNA-seq dataset, Scissor discerned a T cell subpopulation with low PDCD1/CTLA4 and high TCF7 expressions, which is associated with a favorable immunotherapy response. Scissor also demonstrated its broad applicability and effectiveness in interpreting Facioscapulohumeral muscular dystrophy (FSHD) and Alzheimer’s disease scRNA-seq datasets. Thus, Scissor provides a novel framework to identify biologically and clinically relevant cell subpopulations from single-cell assays by leveraging the wealth of phenotype and bulk-omics datasets.