Read the LEfSe paper here: Segata et al, 2011, Metagenomic biomarker discovery and explanation, Genome Biology.
LEfSe (Linear discriminant analysis effect size) is an algorithm for High-Dimensional biomarker discovery that identifies genomic features (genes, pathways, or taxa) characterizing the differences between two or more biological conditions. It emphasizes both statistical significance and biological relevance, allowing researchers to identify discriminative features that are that are statistically different among biological classes.
Specifically, the non-parametric factorial Kruskal-Wallis (KW) sum-rank test is used to detect features with significant differential abundance with respect to the class of interest. As a last step, LEfSe uses Linear Discriminant Analysis to estimate the effect size of each differentially abundant feature and rank the feature accordingly.
In oder to run LEfSe biomarker discovery analysis, at-least 2 datasets and 2 cohorts are required for comparative analysis creation. 2 parameters are required for LEFSE biomarker discovery analysis.
LEFSE Table comprises of 4 columns. The column description is given below.
LEfSe Barchart is a visual representation of discriminative features/biomarkers found by LEfSe tool ranking them accordingly to their LDA Score/Effect Size. The barchart is dynamic and can be filtered using both LDA score and P-value.