Figure 1.

LEfSe mines a wide range of high-throughput genetic data to find biologically relevant features characterizing one or more experimental conditions. The inputs to the system are the specifications of the biological hypothesis under investigation (conditions and inter-condition sample groupings), the high-dimensional data obtained experimentally, and, optionally, prior knowledge from literature or databases used to define known relationships between features (used for meaningful hierarchical organization of the discovered biomarkers) or samples (used for testing biological consistency of potential biomarkers). LEfSe is a three-step algorithm (detailed in Figure 6). (a) LEfSe first provides the list of features that are differential among conditions of interest with statistical and biological significance, ranking them according to the effect size. (b) For problems with known hierarchical structure, either phylogenetic or functional, we then provide a mapping of the differences to taxonomic or functional trees. (c) Finally, the system produces a histogram visualizing the raw data within the specified problem structure for each relevant feature. While LEfSe has been developed primarily for metagenomic data containing taxon or gene abundances, it can be used for biomarker discovery in any setting where prior biological knowledge regarding the structure of a comparison is coupled with statistically significant differences in high-dimensional genomic features. KEGG, Kyoto Encyclopedia of Genes and Genomes; WGS, whole genome shotgun.

Segata et al. Genome Biology 2011 12:R60   doi:10.1186/gb-2011-12-6-r60
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