Evidence map›Paper›PMID 41403052›Full record

ArticleBriefings in bioinformatics2025

scHLens: a web server for hierarchically and interactively exploring single cell RNA-seq data.

Jiazhi Xia, Zhiwei Deng, Chen He, Min Li, Ruiqing Zheng

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Jiazhi XiaSchool of Computer Science and Engineering, Central South University, 932 Lushan South Road, Yuelu District, Changsha, Hunan 410083, China.
Zhiwei DengSchool of Computer Science and Engineering, Central South University, 932 Lushan South Road, Yuelu District, Changsha, Hunan 410083, China.
Chen HeSchool of Computer Science and Engineering, Central South University, 932 Lushan South Road, Yuelu District, Changsha, Hunan 410083, China.
Min LiSchool of Computer Science and Engineering, Central South University, 932 Lushan South Road, Yuelu District, Changsha, Hunan 410083, China.ORCID 0000-0002-0188-1394
Ruiqing ZhengSchool of Computer Science and Engineering, Central South University, 932 Lushan South Road, Yuelu District, Changsha, Hunan 410083, China.ORCID 0000-0001-6372-6798

Funding

Hunan Provincial Natural Science Foundation of China 2023JJ40780National Natural Science Foundation of China 62372471National Natural Science Foundation of China U23A20313Science Foundation for Distinguished Young Scholars of Hunan Province 2023JJ10080
6 · The paper itself

Abstract

With the great advancement of single-cell transcriptome technologies, the identification of cellular heterogeneity from scRNA-seq data has become an important task in biomedical research. There are several challenges associated with the existing analysis methods: (i) The reliance on command-line interfaces creates a substantial technical barrier for researchers lacking computational expertise; (ii) existing methods or platforms usually lack flexibility in workflow customization, forcing users into rigid analytical pipelines; (iii) hierarchical cellular subtypes challenge conventional clustering, as fixed-resolution analyses prevent the detection of biologically subtype cells. Here, we develop a hierarchical and interactive web server named scHLens. scHLens supports a user-defined analysis pipeline and hierarchical exploration mode, providing various visualization views and interaction operations. The three case studies demonstrate scHLens's ability to identify cellular heterogeneity. The online web server version is freely available at http://schlens.csuligroup.com, while the Docker version is available at https://hub.docker.com/r/zhiweideng975/schlens, and the source code can be obtained at https://github.com/ZhiweiDeng459/scHLens.

Indexed as

InternetRNA-SeqSequence Analysis, RNASingle-Cell AnalysisSoftwareComputational BiologyHumanscell type identificationcustom pipelinehierarchical explorationscRNA-seqweb app

Identifiers

PMID41403052
PMCPMC12708058

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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.