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  GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge

Costa, F., Grün, D., & Backofen, R. (2018). GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge. Nature Communications, 9, 3685. doi: 10.1038/s41467-018-05988-7.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-6E33-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-E629-B
Genre: Journal Article

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 Creators:
Costa , Fabrizio1, Author
Grün, Dominic2, Author              
Backofen, Rolf1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Max Planck Institute of Immunobiology and Epigenetics, Max Planck Society, 79108 Freiburg, DE, ou_2243640              

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 Abstract: Cell types can be characterized by expression profiles derived from single-cell RNA-seq. Subpopulations are identified via clustering, yielding intuitive outcomes that can be validated by marker genes. Clustering, however, implies a discretization that cannot capture the continuous nature of differentiation processes. One could give up the detection of subpopulations and directly estimate the differentiation process from cell profiles. A combination of both types of information, however, is preferable. Crucially, clusters can serve as anchor points of differentiation trajectories. Here we present GraphDDP, which integrates both viewpoints in an intuitive visualization. GraphDDP starts from a user-defined cluster assignment and then uses a force-based graph layout approach on two types of carefully constructed edges: one emphasizing cluster membership, the other, based on density gradients, emphasizing differentiation trajectories. We show on intestinal epithelial cells and myeloid progenitor data that GraphDDP allows the identification of differentiation pathways that cannot be easily detected by other approaches.

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Language(s): eng - English
 Dates: 20182018
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41467-018-05988-7
 Degree: -

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Title: Nature Communications
  Abbreviation : Nat. Commun.
Source Genre: Journal
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Publ. Info: London : Nature Publishing Group
Pages: - Volume / Issue: 9 Sequence Number: - Start / End Page: 3685 Identifier: ISSN: 2041-1723
CoNE: https://pure.mpg.de/cone/journals/resource/2041-1723