The SIAMCAT R package enables statistical and machine learning analyses for case-control microbiome datasets.

Alterations in microbiome composition have been linked to many human diseases, including colorectal cancer or precancerous liver diseases. Findings from microbiome-association studies are increasingly explored as promising avenues for clinical applications due to their diagnostic or therapeutic potential. However, microbiome data complexity and the lack of integrated computational tools make it difficult to arrive at robust associations and predictive disease models. Here, we present the SIAMCAT R package as a modular and user-friendly toolbox for machine learning workflows, statistical analysis, and confounder detection for case-control microbiome datasets. Previously, we conducted a machine learning meta-analysis of colorectal cancer metagenomics studies (Wirbel, Pyl et al., Nat Med 2019) using the functionalities of SIAMCAT, which is now available to the wider community via Bioconductor. We showcase how SIAMCAT is a versatile tool by applying it to a large set of metagenomic datasets that have been processed with a wide variety of taxonomic and functional profiling tools. We furthermore demonstrate how SIAMCAT can help to detect confounding factors in microbiome association studies. By making stringent machine learning workflows and analysis pipelines available through a user-friendly and flexible interface, SIAMCAT will facilitate microbiome data analyses, biomarker discovery, and thus the translation of microbiome research to clinical applications, while simultaneously improving statistical rigor and safeguarding against common machine learning pitfalls. Citation Format: Jakob Wirbel, Konrad Zych, Morgan Essex, Nicolai Karcher, Ece Kartal, Guillem Salazar, Peer Bork, Shinichi Sunagawa, Georg Zeller. The SIAMCAT R package enables statistical and machine learning analyses for case-control microbiome datasets [abstract]. In: Proceedings of the AACR Special Conference on the Microbiome, Viruses, and Cancer; 2020 Feb 21-24; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2020;80(8 Suppl):Abstract nr A40.