Microbiome Toolbox: Methodological approaches to derive and visualize microbiome trajectories

Abstract

SummaryThe gut microbiome changes rapidly under the influence of different factors such as age, dietary changes or medications to name just a few. To analyze and understand such changes we present a microbiome analysis toolbox. We implemented several methods for analysis and exploration to provide interactive visualizations for easy comprehension and reporting of longitudinal microbiome data. Based on abundance of microbiome features such as taxa as well as functional capacity modules, and with the corresponding metadata per sample, the toolbox includes methods for 1) data analysis and exploration, 2) data preparation including dataset-specific preprocessing and transformation, 3) best feature selection for log-ratio denominators, 4) two-group analysis, 5) microbiome trajectory prediction with feature importance over time, 6) spline and linear regression statistical analysis for testing universality across different groups and differentiation of two trajectories, 7) longitudinal anomaly detection on the microbiome trajectory, and 8) simulated intervention to return anomaly back to a reference trajectory. Availability and implementationThe software tools are open source and implemented in Python. The link to the interactive dashboard is https://microbiome-toolbox.herokuapp.com/. For developers interested in additional functionality of the toolbox, the Python package can be downloaded from https://pypi.org/project/microbiome-toolbox/. The toolbox is modular allowing for further extension with custom methods and analysis. The code is available on Github https://github.com/JelenaBanjac/microbiome-toolbox. ContactShaillayKumar.Dogra@rd.nestle.com Supplementary InformationSupplementary data are available at Bioinformatics online.