The prevalence of single-cell multi-omics datasets calls for automated cell type annotation methods that can characterize novel cell states. We developed \(\Phi\) -Space, a computational framework for the continuous phenotyping of single-cell multi-omics data. We adopt a highly versatile modeling strategy to characterize query cell identity in a low-dimensional phenotype space, defined by reference phenotypes. The phenotype space embedding enables various downstream analyses, including insightful visualizations, clustering, and cell type labeling. \(\Phi\) -Space is applicable to a wide range analytical tasks beyond cell type transfer. Its ability to model complex phenotypic variations will facilitate biological discoveries from different omics types.
