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treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses
treeclimbR is for analyzing hierarchical trees of entities, such as phylogenies or cell types, at different resolutions. It proposes multiple candidates that capture the latent signal and pinpoints branches or leaves that contain features of interest, in a data-driven way. It outperforms currently a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8127214/ https://www.ncbi.nlm.nih.gov/pubmed/34001188 http://dx.doi.org/10.1186/s13059-021-02368-1 |
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author | Huang, Ruizhu Soneson, Charlotte Germain, Pierre-Luc Schmidt, Thomas S.B. Mering, Christian Von Robinson, Mark D. |
author_facet | Huang, Ruizhu Soneson, Charlotte Germain, Pierre-Luc Schmidt, Thomas S.B. Mering, Christian Von Robinson, Mark D. |
author_sort | Huang, Ruizhu |
collection | PubMed |
description | treeclimbR is for analyzing hierarchical trees of entities, such as phylogenies or cell types, at different resolutions. It proposes multiple candidates that capture the latent signal and pinpoints branches or leaves that contain features of interest, in a data-driven way. It outperforms currently available methods on synthetic data, and we highlight the approach on various applications, including microbiome and microRNA surveys as well as single-cell cytometry and RNA-seq datasets. With the emergence of various multi-resolution genomic datasets, treeclimbR provides a thorough inspection on entities across resolutions and gives additional flexibility to uncover biological associations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-021-02368-1). |
format | Online Article Text |
id | pubmed-8127214 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81272142021-05-17 treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses Huang, Ruizhu Soneson, Charlotte Germain, Pierre-Luc Schmidt, Thomas S.B. Mering, Christian Von Robinson, Mark D. Genome Biol Method treeclimbR is for analyzing hierarchical trees of entities, such as phylogenies or cell types, at different resolutions. It proposes multiple candidates that capture the latent signal and pinpoints branches or leaves that contain features of interest, in a data-driven way. It outperforms currently available methods on synthetic data, and we highlight the approach on various applications, including microbiome and microRNA surveys as well as single-cell cytometry and RNA-seq datasets. With the emergence of various multi-resolution genomic datasets, treeclimbR provides a thorough inspection on entities across resolutions and gives additional flexibility to uncover biological associations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-021-02368-1). BioMed Central 2021-05-17 /pmc/articles/PMC8127214/ /pubmed/34001188 http://dx.doi.org/10.1186/s13059-021-02368-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Method Huang, Ruizhu Soneson, Charlotte Germain, Pierre-Luc Schmidt, Thomas S.B. Mering, Christian Von Robinson, Mark D. treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses |
title | treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses |
title_full | treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses |
title_fullStr | treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses |
title_full_unstemmed | treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses |
title_short | treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses |
title_sort | treeclimbr pinpoints the data-dependent resolution of hierarchical hypotheses |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8127214/ https://www.ncbi.nlm.nih.gov/pubmed/34001188 http://dx.doi.org/10.1186/s13059-021-02368-1 |
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