RT Journal Article SR Electronic T1 Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells JF bioRxiv FD Cold Spring Harbor Laboratory SP 208819 DO 10.1101/208819 A1 F. Alexander Wolf A1 Fiona Hamey A1 Mireya Plass A1 Jordi Solana A1 Joakim S. Dahlin A1 Berthold Göttgens A1 Nikolaus Rajewsky A1 Lukas Simon A1 Fabian J. Theis YR 2018 UL http://biorxiv.org/content/early/2018/11/04/208819.abstract AB Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. Partition-based graph abstraction (PAGA) provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions (https://github.com/theislab/paga). PAGA maps provide interpretable discrete and continuous latent coordinates for both disconnected and continuous structure in data, preserve the global topology of data, allow analyzing data at different resolutions and result in much higher computational efficiency of the typical exploratory data analysis workflow — one million cells take on the order of a minute, a speedup of 130 times compared to UMAP. We demonstrate the method by inferring structure-rich cell maps with consistent topology across four hematopoietic datasets, confirm the reconstruction of lineage relations of adult planaria and the zebrafish embryo, benchmark computational performance on a neuronal dataset and detect a biological trajectory in one deep-learning processed image dataset.