Existing single-cell (SC) datasets are very limited by their number of donors (individuals). As a result, most of the current research in SC genomics focuses on studying biological processes that are broadly conserved across individuals, such as cellular organization and tissue development. While studying such biology from a limited number of donors is possible in principle due to the expected high consistency across donors, advancing our understanding of heterogeneous conditions that demonstrate molecular variation across individuals requires population-level data. Particularly, probing the etiology of complex and heterogeneous variation that may be inconsistent across individuals owing to molecular variation is expected to require population-level information. We developed ``kernel of integrated single cells'' (Keris), a novel framework to inform the analysis of SC gene expression data with population-level variation. By inferring cell-type-specific moments and their variation with conditions using large tissue-level bulk data representing a population, Keris allows us to generate testable hypotheses at the SC level that would normally require collecting SC data from a large number of donors. Here, we demonstrate how such integration of low-resolution but large bulk data with small but high-resolution SC data enables the identification and study of systematic gradients of variation in gene-gene interactions across cells.