Unlocking Complex Metabolomics Data: A Guide to Statistical Analysis of FBMN Results
As the fields of metabolomics and cheminformatics continue to evolve, researchers are increasingly turning to advanced analytical methods to parse through complex datasets. One such method, feature-based molecular networking (FBMN), has emerged as a powerful tool for analyzing liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based non-targeted metabolomics data. While FBMN facilitates the efficient processing of these data, the real challenge often lies in the subsequent steps: handling, analyzing, and deriving meaningful insights from the intricate data output.
This guide shines a light on the statistical analysis of FBMN results, with a primary focus on the downstream analysis of the output table generated by this approach. Aimed at demystifying the process for both novices and experienced users, this resource delves into the nuances of data structure and leads you through the essential protocols for data cleanup, normalization, and statistical analysis.
Understanding the array of statistical techniques available for analyzing FBMN results is crucial. This guide lays the groundwork by discussing both uni- and multivariate statistical analysis methods and how they can be applied to interpret FBMN data effectively. To accommodate users of varying backgrounds, we provide comprehensive explanations, alongside practical code in R and Python scripting languages, as well as within the QIIME2 framework. These are made accessible through meticulously prepared Jupyter Notebooks, available for free on GitHub at our dedicated repository.
Recognizing the steep learning curve that statistical analysis can present, especially to newcomers in the field, this guide introduces a user-friendly web application. Accessible at fbmn-statsguide.gnps2.org, this application boasts a graphical user interface designed to simplify the journey for educational uses or for those making their initial foray into metabolomics data analysis.
The guide does not stop at analysis; it also instructs users on how to visually integrate their statistical findings back into the original molecular network using Cytoscape, an open-source software tool. This holistic approach ensures that researchers can not only conduct comprehensive statistical analyses but also visualize their results in a manner that elucidates the intricate relationships within their data.
To ensure that the protocols are practical and clearly understood, we utilize a previously published environmental metabolomics dataset throughout the guide. This real-world example helps to ground the abstract concepts in tangible analysis tasks, making it easier for users to apply the knowledge to their own data sets.
This comprehensive guide, complete with an accompanying web application and shared code, aims to arm researchers with the tools necessary to conduct robust statistical analyses of FBMN results. Tailored to datasets analyzed through the Global Natural Products Social Molecular Networking (GNPS) platform, the principles and procedures outlined here are also applicable to a broader range of mass spectrometry feature detection, annotation, and networking tools.
In summary, this guide offers a detailed roadmap for navigating the often-daunting task of statistical analysis in non-targeted metabolomics studies. By providing easy-to-follow protocols, practical examples, and accessible tools, it aims to empower researchers to uncover the molecular insights hidden within their complex datasets.
