Figure 6.

Schematic representation of the statistical and computational steps implemented in LEfSe. Input data consist of a collection of m samples (columns) each made up of n numerical features (rows, typically normalized per-sample, red representing high values and green low). These samples are labeled with a class (taking two or more possible values) that represents the main biological comparison under investigation; they may also have one or more subclass labels reflecting within-class groupings. (a) Step 1 analyzes all features, testing whether values in different classes are differentially distributed. (b) Features violating the null hypothesis are further analyzed in step 2, which tests whether all pairwise comparisons between subclasses in different classes significantly agree with the class level trend. (c) The resulting subset of vectors is used to build a LDA model from which the relative difference among classes is used to rank the features. The final output thus consists of a list of features that are discriminative with respect to the classes, consistent with the subclass grouping within classes, and ranked according to the effect size with which they differentiate classes.