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

By F. Alexander Wolf, Fiona Kathryn Hamey, Mireya Plass, Jordi Solana, Joakim S. Dahlin, Berthold Göttgens, Nikolaus Rajewsky, Lukas M. Simon, Fabian J Theis. 2019

1,506 citations recorded by the source corpus.

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.

Subfields: Biophysics, Molecular Biology

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Citations in the corpus

Visualizing structure and transitions in high-dimensional biological data (2019, background): “We note that ordering could also be based on other pseudotime-ordering software 14,30–33 .”
Generalizing RNA velocity to transient cell states through dynamical modeling (2019, background): “9a), which indicates that stochasticity adds valuable information.”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “This unification was proposed by the partition-based graph abstraction tool (PAGA; Fig 7D; Wolf et al , 2019).”
Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues (2023, positive): “In our TBI case study, we chose four main anatomical regions (i.e. hypothalamus [node 4], thalamus [node 2], hippocampus [node 3] and penumbra regionsofthedamagedsite[node1]),thereasonbeingthatroute4-2-3-1wasidenti ﬁ edthroughanunsupervisedprocessasoneoftheshortest path from the terminal state (clus”
Spatiotemporal analysis of human intestinal development at single-cell resolution (2021, positive): “In order to better understand the higher level connectivity structure of 101 single cell populations identified in our atlas, we employed a partition-based graph abstraction algorithm (Wolf et al., 2019) (scanpy accessed via reticulate (version 1.”
DeepST: identifying spatial domains in spatial transcriptomics by deep learning (2022, background): “The UMAP and the PAGA (25) (the partitionbased graph abstraction) results of DeepST indicated that the various cortical layers were well organized from L1 to L6 and WM, better than the result of Seurat (Figure 2D).”
Best practices for single-cell analysis across modalities (2023, methodology): “Univariate statistical models, which analyse change in abundance for each cell type individually, such as Poisson regression or Wilcoxon rank-sum tests, may perceive some cell-type population shifts as statistically sound effects, although they are purely a statistical artefact caused by the…”