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

• Published in: Genome Biology 2019-03, Vol.20 (1), p.59-9, Article 59
• Main Authors: Wolf, F Alexander, Hamey, Fiona K, Plass, Mireya, Solana, Jordi, Dahlin, Joakim S, Göttgens, Berthold, Rajewsky, Nikolaus, Simon, Lukas, Theis, Fabian J
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Cell cycle; Clustering; Computational Biology - methods; Computer Graphics; Connectivity; Data analysis; Data processing; Datasets; Embryo, Nonmammalian - cytology; Embryo, Nonmammalian - metabolism; Gene expression; Gene Expression Regulation, Developmental; Graphs; Hematopoietic Stem Cells - cytology; Hematopoietic Stem Cells - metabolism; High-Throughput Nucleotide Sequencing - methods; Humans; Medicin och hälsovetenskap; Method; Neighborhoods; Neural networks; Planarians - cytology; Planarians - genetics; Reference Standards; Ribonucleic acid; RNA; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Software; Zebrafish - growth & development; Zebrafish - metabolism
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-019-1663-x

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 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. 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.