2011 | Nicola Segata, Jacques Izard, Levi Waldron, Dirk Gevers, Larisa Miropolsky, Wendy S Garrett and Curtis Huttenhower
This study introduces LEfSe, a method for metagenomic biomarker discovery and explanation. LEfSe addresses the challenge of identifying organisms, genes, or pathways that consistently explain differences between microbial communities. The method combines statistical significance with biological consistency and effect size estimation to identify biologically informative features. Extensive validation on human microbiomes, a mouse model of ulcerative colitis, and environmental samples demonstrates LEfSe's effectiveness in detecting differentially abundant features. LEfSe also performs well on synthetic data, achieving a low false positive rate. The method is implemented as a user-friendly web application, providing a convenient interface for researchers to explore metagenomic data and discover novel biomarkers.This study introduces LEfSe, a method for metagenomic biomarker discovery and explanation. LEfSe addresses the challenge of identifying organisms, genes, or pathways that consistently explain differences between microbial communities. The method combines statistical significance with biological consistency and effect size estimation to identify biologically informative features. Extensive validation on human microbiomes, a mouse model of ulcerative colitis, and environmental samples demonstrates LEfSe's effectiveness in detecting differentially abundant features. LEfSe also performs well on synthetic data, achieving a low false positive rate. The method is implemented as a user-friendly web application, providing a convenient interface for researchers to explore metagenomic data and discover novel biomarkers.