lefser

lefser implements the LEfSe algorithm in R for microbiome biomarker discovery from two-level classes with optional sub-classes.

Key Features:

• Statistical Testing: Uses the Kruskal-Wallis test and Wilcoxon-Rank Sum test for biomarker identification.
• Linear Discriminant Analysis: Applies LDA to rank and identify discriminating biomarkers.
• Two-Level Class Support: Analyzes biomarkers from two-level classes with optional sub-classes.
• R/Bioconductor Integration: Provides LEfSe functionality within the R/Bioconductor ecosystem for compatibility with existing statistical analysis workflows.
• Performance Improvements: Offers improvements to the original algorithm for performance, accuracy, and reproducibility.

Scientific Applications:

• Metagenomic Biomarker Discovery: Identifies microbial biomarkers in metagenomic datasets from human and mouse studies.
• Microbiome Differential Analysis: Finds features that distinguish between two-level classes in microbiome data.

Methodology:

lefser applies the Kruskal-Wallis test, Wilcoxon-Rank Sum test, and Linear Discriminant Analysis to identify biomarkers from two-level classes with optional sub-classes.