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. 2017

230 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 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.

Subfields: Biophysics, Molecular Biology

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Citations in the corpus

The specious art of single-cell genomics (2021, background): “Similarly, higher dimensional inference of differentiation trajectories [50, 51], and incorporation of probabilistic inference methods [52–54], offer meaningful and interpretable analysis approaches with possible, targeted visualization alternatives.”
Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming (2019, unknown): “: This program (Wolf et al., 2017) connects clusters to identify a graphical representation of trajectories.”
The single cell transcriptional landscape of mammalian organogenesis (2019, unknown): “It then groups mutually similar cells using the Louvain community detection algorithm, and merges adjacent groups into ‘supergroups’ 44 .”
Challenges in unsupervised clustering of single-cell RNA-seq data (2019, methodology): “A novel method by Wolf et al. 44 provides a coarse grained graph representation in which cells are assigned either to nodes (which represent stable groups) or to connecting edges.”
SOM-based embedding improves efficiency of high-dimensional cytometry data analysis (2019, background): “[28] to improve the trajectory depiction in EmbedSOM.”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, unknown): “This unification was proposed by the partition-based graph abstraction tool (PAGA; Fig 7D; Wolf et al , 2019).”
Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis (2020, methodology): “PAGA connectivity analysis of fibroblast and myofibroblast To assess the most likely trajectories of cell progression toward IPF-enriched fibrosis among fibroblast and myofibroblast, we used unsupervised Louvain clustering to generate eight subpopulations, which were then subjected to PAGA analysis ”
From Louvain to Leiden: guaranteeing well-connected communities (2018, background): “The problem of disconnected communities has been observed before 19,20 , also in the context of the label propagation algorithm 21 .”