PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells.

Abstract

Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. Partition-based graph abstraction (PAGA) provides an interpretable graphlike map of the arising data manifold, based on estimating connectivity of manifold partitions (https://github.com/theislab/paga ). PAGA maps preserve the global topology of data, allow analyzing data at di erent resolutions and result in much higher computational e ciency of the typical exploratory data analysis workflow. We demonstrate the method by inferring structure-rich cell maps with consistent topology across four hematopoietic datasets, adult planaria and the zebrafish embryo and benchmark computational performance on one million neurons.