Large-scale microbiome data integration enables robust biomarker identification

Abstract The close association between gut microbiota dysbiosis and human diseases is being increasingly recognized. However, contradictory results are frequently reported, as confounding effects exist. lack of unbiased data integration methods also impeding the discovery disease-associated microbial biomarkers from different cohorts. Here we propose an algorithm, NetMoss, for assessing shifts network modules to identify robust associated with various diseases. Compared previous approaches, NetMoss method shows better performance in removing batch effects. Through comprehensive evaluations on both simulated real datasets, demonstrate that has great advantages identification disease-related biomarkers. Based analysis pandisease studies, there a high prevalence multidisease-related bacteria global populations. We believe large-scale will help understanding role microbiome more perspective accurate biomarker greatly promote microbiome-based medical diagnosis.