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treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses


Huang, Ruizhu; Soneson, Charlotte; Germain, Pierre-Luc; Schmidt, Thomas S B; von Mering, Christian; Robinson, Mark D (2021). treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses. Genome Biology, 22:157.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Molecular Life Sciences
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Life Sciences > Ecology, Evolution, Behavior and Systematics
Life Sciences > Genetics
Life Sciences > Cell Biology
Language:English
Date:1 May 2021
Deposited On:08 Jun 2021 14:57
Last Modified:11 Jun 2021 06:59
Publisher:BioMed Central
ISSN:1474-7596
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1186/s13059-021-02368-1
Project Information:
  • : FunderSNSF
  • : Grant ID310030_175841
  • : Project TitleBeyond the average: computational tools for discovery in high-throughput single cell datasets
  • : FunderSNSF
  • : Grant IDCRSII5_177208
  • : Project TitleDefining the identity and differentiation pathways of the immune-stimulating fibroblastic tumor stroma

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