easySCF: a tool for enhancing interoperability between R and Python for efficient single-cell data analysis.

Haoyun Zhang, Wentao Zhang, Shuai Zhao, Guangyu Xu, Yi Shen, Feng Jiang, An Qin, Lei Cui

Abstract read

In one paragraph

Article in Bioinformatics (Oxford, England), 2024. 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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1 · What the graph read from it

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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.

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2 · The registry

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Haoyun ZhangSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.

Wentao ZhangSchool of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China.

Shuai ZhaoCollaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-constructed by the Province and Ministry, Guangxi Medical University, Nanning, 530021, China.

Guangyu XuCollaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-constructed by the Province and Ministry, Guangxi Medical University, Nanning, 530021, China.

Yi ShenJiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China.

Feng JiangShanghai Research and Development Center, UxBioInfo, Shanghai, 201100, China.

An QinShanghai Key Laboratory of Orthopaedic Implants, Department of Orthopaedics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200125, China.

Lei CuiCollaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-constructed by the Province and Ministry, Guangxi Medical University, Nanning, 530021, China.ORCID 0009-0006-7868-8441

Funding

National Natural Science Foundation of China 82372430

6 · The paper itself

Abstract

summaryThis study introduces easySCF, a tool designed to enhance the interoperability of single-cell data between the two major bioinformatics platforms, R and Python. By supporting seamless data exchange, easySCF improves the efficiency and accuracy of single-cell data analysis. AVAILABILITY AND IMPLEMENTATION: easySCF utilizes a unified data format (.h5 format) to facilitate data transfer between R and Python platforms. The tool has been evaluated for data processing speed, memory efficiency, and disk usage, as well as its capability to handle large-scale single-cell datasets. easySCF is available as an open-source package, with implementation details and documentation accessible at https://github.com/xleizi/easySCF.

Indexed as

Computational BiologySingle-Cell AnalysisSoftwareHumansProgramming Languages

Identifiers

PMID39585309

DOI10.1093/bioinformatics/btae710

PMCPMC11634540

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LicenceCC BY

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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.