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GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge
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 differe...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6134144/ https://www.ncbi.nlm.nih.gov/pubmed/30206223 http://dx.doi.org/10.1038/s41467-018-05988-7 |
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author | Costa, Fabrizio Grün, Dominic Backofen, Rolf |
author_facet | Costa, Fabrizio Grün, Dominic Backofen, Rolf |
author_sort | Costa, Fabrizio |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6134144 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61341442018-09-14 GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge Costa, Fabrizio Grün, Dominic Backofen, Rolf Nat Commun Article 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. Nature Publishing Group UK 2018-09-11 /pmc/articles/PMC6134144/ /pubmed/30206223 http://dx.doi.org/10.1038/s41467-018-05988-7 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Costa, Fabrizio Grün, Dominic Backofen, Rolf GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge |
title | GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge |
title_full | GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge |
title_fullStr | GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge |
title_full_unstemmed | GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge |
title_short | GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge |
title_sort | graphddp: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6134144/ https://www.ncbi.nlm.nih.gov/pubmed/30206223 http://dx.doi.org/10.1038/s41467-018-05988-7 |
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