Data Science, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne 1015, Switzerland.
Department of Gastrointestinal Health, Nestlé Institute of Health Sciences, Nestlé Research, Société des Produits Nestlé S.A, Lausanne 1000, Switzerland.
Bioinformatics. 2023 Jan 1;39(1). doi: 10.1093/bioinformatics/btac781.
The 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 Toolbox. We implemented several methods for analysis and exploration to provide interactive visualizations for easy comprehension and reporting of longitudinal microbiome data.
Based on the abundance of microbiome features such as taxa as well as functional capacity modules, and with the corresponding metadata per sample, the Microbiome Toolbox includes methods for (i) data analysis and exploration, (ii) data preparation including dataset-specific preprocessing and transformation, (iii) best feature selection for log-ratio denominators, (iv) two-group analysis, (v) microbiome trajectory prediction with feature importance over time, (vi) spline and linear regression statistical analysis for testing universality across different groups and differentiation of two trajectories, (vii) longitudinal anomaly detection on the microbiome trajectory and (viii) simulated intervention to return anomaly back to a reference trajectory.
The software tools are open source and implemented in Python. For developers interested in additional functionality of the Microbiome Toolbox, it is modular allowing for further extension with custom methods and analysis. The code, python package and the link to the interactive dashboard of Microbiome Toolbox are available on GitHub https://github.com/JelenaBanjac/microbiome-toolbox.
Supplementary data are available at Bioinformatics online.
肠道微生物组在年龄、饮食变化或药物等多种因素的影响下迅速变化。为了分析和理解这种变化,我们提出了微生物组工具箱。我们实现了几种分析和探索方法,提供了交互式可视化,以便轻松理解和报告纵向微生物组数据。
基于微生物组特征(如分类群和功能能力模块)的丰度,以及每个样本的相应元数据,微生物组工具箱包括以下方法:(i)数据分析和探索,(ii)数据准备,包括特定于数据集的预处理和转换,(iii)用于对数比分母的最佳特征选择,(iv)两组分析,(v)随着时间推移的微生物组轨迹预测,以及特征重要性,(vi)用于测试不同组之间通用性和两个轨迹分化的样条和线性回归统计分析,(vii)微生物组轨迹上的纵向异常检测,以及(viii)模拟干预以使异常回归到参考轨迹。
软件工具是开源的,用 Python 实现。对于有兴趣在微生物组工具箱的更多功能上进行开发的开发人员,它是模块化的,允许使用自定义方法和分析进行进一步扩展。代码、Python 包以及微生物组工具箱的交互式仪表板的链接可在 GitHub https://github.com/JelenaBanjac/microbiome-toolbox 上获得。
补充数据可在《生物信息学》在线获得。