Abstract
Here, we introduce UniPath, for representing single-cells using pathway and gene-set enrichment scores by transformation of their open-chromatin or expression profiles. Besides being robust to variability in drop-out, UniPath also provides consistency and scalability in estimating gene-set enrichment scores for every cell. UniPath also enables exploiting pathway continuum and dropping known covariate gene-sets for predicting temporal order of single-cells. Analyzing mouse cell atlas using pathway enrichment-scores revealed surprising but biologically-meaningful co-clustering of cell-types from distant organs and helped in annotating many unlabeled cells. By enabling unconventional analysis, UniPath also prooves to be useful in inferring context-specific regulation in cancer cells.