Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data
Andrew E. Teschendorff, Alok K. Maity, Hu Xue, Chen Wei-yan, Matthias Lechner
Abstract
MOTIVATION: An important task in the analysis of single-cell RNA-Seq data is the estimation of differentiation potency, as this can help identify stem-or-multipotent cells in non-temporal studies or in tissues where differentiation hierarchies are not well established. A key challenge in the estimation of single-cell potency is the need for a fast and accurate algorithm, scalable to large scRNA-Seq studies profiling millions of cells. RESULTS: Here, we present a single-cell potency measure, called Correlation of Connectome and Transcriptome (CCAT), which can return accurate single-cell potency estimates of a million cells in minutes, a 100-fold improvement over current state-of-the-art methods. We benchmark CCAT against 8 other single-cell potency models and across 28 scRNA-Seq studies, encompassing over 2 million cells, demonstrating comparable accuracy than the current state-of-the-art, at a significantly reduced computational cost, and with increased robustness to dropouts. AVAILABILITY AND IMPLEMENTATION: CCAT is part of the SCENT R-package, freely available from https://github.com/aet21/SCENT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.