摘要：SummaryOur understanding of miRNA activity at cellular resolution is thwarted by the inability of standard scRNA-seq protocols to capture miRNAs. We introduce a novel tool, miRSCAPE, to infer miRNA expression in a sample from its RNA-seq profile. We establish miRSCAPE’s accuracy in 10 tumor and normal cohorts demonstrating its superiority over alternatives. miRSCAPE accurately infers cell type-specific miRNA activities (predicted versus observed fold-difference correlation ∼0.81) in two independent scRNA-seq datasets. We apply miRSCAPE to infer miRNA activities in scRNA clusters in pancreatic and lung adenocarcinomas, as well as in 56 cell types in the human cell landscape (HCL). In pancreatic and breast cancer scRNA-seq data, miRSCAPE recapitulates miRNAs associated with stemness and epithelial-mesenchymal transition (EMT) cell states, respectively. Overall, miRSCAPE recapitulates and refines miRNA biology at cellular resolution. miRSCAPE is freely available and is easily applicable to scRNA-seq data to infer miRNA activities at cellular resolution.Graphical abstractDisplay OmittedHighlights•Novel machine learning-based tool to infer miRNA expression at single cell level•Predicts miRNA activity with high accuracy in various contexts•Recaps miRNAs associated with specific cellular states and suggests novel candidates•Provides a general framework to predict other types of molecular data at a single cellBiocomputational method; Cancer systems biology; Transcriptomics
