Determining gene specificity from multivariate single-cell RNA sequencing data

Abstract

An important application of single-cell genomics experiments is to identify genes specific to biological categories or experimental conditions. We develop ember (Entropy Metrics for Biological ExploRation), an entropy-based framework that uses Shannon's chain rule across nested biological partitions. Four axioms characterize its Psi score within a class of normalized entropy-decomposition scores. Applying ember to eight tissues from eight founder mouse strains, we find that gene specificity is often unintuitive: canonical markers can be supplanted, housekeeping genes are context-dependent, and mouse strain can drive unexpected cell type switching. Unsupervised learning on entropy metrics uncovers shared genes specialized to male gonads and kidney, as well as genes specific to non-consecutive developmental stages in the kidney. To facilitate further exploration of gene specificity in mice, we have also developed a comprehensive specificity database, along with a web interface, API and MCP server. Extending ember to a human PBMC dataset collected from 255 diverse individuals, we find that variation in PBMCs is largely localized to classical monocytes. We also find genes with unique specificity by sex, age and ancestral background. Together, these applications establish ember as a powerful tool and provide a roadmap for elucidating the impact of human genetic variation using the murine model.