在 python 中使用 FlowSOM 进行高效细胞测量分析，提高了与其他单细胞工具的互操作性。

IF 4.4 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Bioinformatics Pub Date : 2024-04-17 DOI:10.1093/bioinformatics/btae179

Artuur Couckuyt, Benjamin Rombaut, Yvan Saeys, S. van Gassen

{"title":"在 python 中使用 FlowSOM 进行高效细胞测量分析，提高了与其他单细胞工具的互操作性。","authors":"Artuur Couckuyt, Benjamin Rombaut, Yvan Saeys, S. van Gassen","doi":"10.1093/bioinformatics/btae179","DOIUrl":null,"url":null,"abstract":"MOTIVATION\nWe describe a new Python implementation of FlowSOM, a clustering method for cytometry data.\n\n\nRESULTS\nThis implementation is faster than the original version in R, better adapted to work with single-cell omics data including integration with current single-cell data structures and includes all the original visualizations, such as the star and pie plot.\n\n\nAVAILABILITY\nThe FlowSOM Python implementation is freely available on GitHub: https://github.com/saeyslab/FlowSOM_Python.\n\n\nSUPPLEMENTARY INFORMATION\nSupplementary data are available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficient cytometry analysis with FlowSOM in python boosts interoperability with other single-cell tools.\",\"authors\":\"Artuur Couckuyt, Benjamin Rombaut, Yvan Saeys, S. van Gassen\",\"doi\":\"10.1093/bioinformatics/btae179\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"MOTIVATION\\nWe describe a new Python implementation of FlowSOM, a clustering method for cytometry data.\\n\\n\\nRESULTS\\nThis implementation is faster than the original version in R, better adapted to work with single-cell omics data including integration with current single-cell data structures and includes all the original visualizations, such as the star and pie plot.\\n\\n\\nAVAILABILITY\\nThe FlowSOM Python implementation is freely available on GitHub: https://github.com/saeyslab/FlowSOM_Python.\\n\\n\\nSUPPLEMENTARY INFORMATION\\nSupplementary data are available at Bioinformatics online.\",\"PeriodicalId\":8903,\"journal\":{\"name\":\"Bioinformatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/bioinformatics/btae179\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btae179","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}

引用次数: 0

摘要

MOTIVATION We describe a new Python implementation of FlowSOM, a clustering method for cytometry data.ResultThis implementation is faster than the original version in R, better adapted to work with single-cell omics data including integration with current single-cell data structures and includes all the original visualizations, such as the star and pie plot.AVAILABILITYThe FlowSOM Python implementation is free available on GitHub: https://github.com/saeyslab/FlowSOM_Python.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online.

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Efficient cytometry analysis with FlowSOM in python boosts interoperability with other single-cell tools.

MOTIVATION We describe a new Python implementation of FlowSOM, a clustering method for cytometry data. RESULTS This implementation is faster than the original version in R, better adapted to work with single-cell omics data including integration with current single-cell data structures and includes all the original visualizations, such as the star and pie plot. AVAILABILITY The FlowSOM Python implementation is freely available on GitHub: https://github.com/saeyslab/FlowSOM_Python. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Bioinformatics 生物-生化研究方法

CiteScore

11.20

自引率

5.20%

发文量

753

审稿时长

2.1 months

期刊介绍： The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.