Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany.
Department of Haematology and Wellcome and Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
Genome Biol. 2019 Mar 19;20(1):59. 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.
单细胞 RNA 测序定量分析了离散细胞类型和连续细胞转变过程中的生物学异质性。基于流形分区连接性估计的基于分区的图抽象（PAGA）提供了一个可解释的类似图的数据流形图谱，（https://github.com/theislab/paga）。PAGA 图谱保留了数据的全局拓扑结构，允许在不同的分辨率下分析数据，并显著提高了典型探索性数据分析工作流程的计算效率。我们通过在四个造血数据集、成年扁形动物和斑马鱼胚胎中推断具有一致拓扑结构的富含结构的细胞图谱，并在 100 万个神经元上对计算性能进行基准测试，来证明该方法。
