scRegulocity: Detection of local RNA velocity patterns in embeddings of single cell RNA-Seq data

Abstract

Single cell RNA-sequencing has revolutionized transcriptome analysis. ScRNA-seq provides a massive resource for studying biological phenomena at single cell level. One of the most important applications of scRNA-seq is the inference of dynamic cell states through modeling of transcriptional dynamics. Understanding the full transcriptional dynamics using the concept named RNA Velocity enables us to identify cell states, regimes of regulatory changes in cell states, and putative drivers within these states. We present scRegulocity that integrates RNA-velocity estimates with locality information from cell embedding coordinates. scRegulocity focuses on velocity switching patterns, local patterns where velocity of nearby cells change abruptly. These different transcriptional dynamics patterns can be indicative of transitioning cell states. scRegulocity annotates these patterns with genes and enriched pathways and also analyzes and visualizes the velocity switching patterns at the regulatory network level. scRegulocity also combines velocity estimation, pattern detection and visualization steps.