Evidence map›Paper›PMID 34001188›Full record

ArticleGenome biology2021

treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses.

Ruizhu Huang, Charlotte Soneson, Pierre-Luc Germain, Thomas S B Schmidt, Christian Von Mering, Mark D Robinson

Abstract read
In one paragraph

Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed.

  1. Trial
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  7. Marine drugs · 2025
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  12. Tributyrin (CoreBiomeFrontiers in nutrition · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ruizhu HuangDepartment of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, Switzerland.
Charlotte SonesonDepartment of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, Switzerland.
Pierre-Luc GermainDepartment of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, Switzerland.
Thomas S B SchmidtDepartment of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, Switzerland.
Christian Von MeringDepartment of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, Switzerland.
Mark D RobinsonDepartment of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, Switzerland. mark.robinson@imls.uzh.ch.ORCID 0000-0002-3048-5518

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AlgorithmsModels, GeneticAnimalsBacteriaBlood PressureCerebral CortexComputer SimulationDatabases, GeneticGene Expression RegulationHumansInfant, NewbornMiceMicroRNAsPhylogenySingle-Cell AnalysisMicroRNAs

Identifiers

PMID34001188
PMCPMC8127214

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.