lefser: implementation of metagenomic biomarker discovery tool, LEfSe, in R.

Autor:	Khleborodova A; Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, NY 10027, United States.; Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY 10027, United States., Gamboa-Tuz SD; Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, NY 10027, United States.; Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY 10027, United States., Ramos M; Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, NY 10027, United States.; Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY 10027, United States., Segata N; Cellular, Computational and Integrative Biology, University of Trento, Trento, Provo 38123, Italy.; European Institute of Oncology, Istituto di Ricovero e Cura a Carattere Scientifico, Milan 20139, Italy., Waldron L; Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, NY 10027, United States.; Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY 10027, United States., Oh S; Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, NY 10027, United States.; Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY 10027, United States.
Jazyk:	angličtina
Zdroj:	Bioinformatics (Oxford, England) [Bioinformatics] 2024 Nov 28; Vol. 40 (12).
DOI:	10.1093/bioinformatics/btae707
Abstrakt:	Summary: LEfSe is a widely used Python package and Galaxy module for metagenomic biomarker discovery and visualization, utilizing the Kruskal-Wallis test, Wilcoxon Rank-Sum test, and Linear Discriminant Analysis. R/Bioconductor provides a large collection of tools for metagenomic data analysis but has lacked an implementation of this widely used algorithm, hindering benchmarking against other tools and incorporation into R workflows. We present the lefser package to provide comparable functionality within the R/Bioconductor ecosystem of statistical analysis tools, with improvements to the original algorithm for performance, accuracy, and reproducibility. We benchmark the performance of lefser against the original algorithm using human and mouse metagenomic datasets. Availability and Implementation: Our software, lefser, is distributed through the Bioconductor project (https://www.bioconductor.org/packages/release/bioc/html/lefser.html), and all the source code is available in the GitHub repository https://github.com/waldronlab/lefser. (© The Author(s) 2024. Published by Oxford University Press.)
Databáze:	MEDLINE
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