Multi-omics Analysis Workflow for gut microbiome

An example to integrate and analyze 16s and WGS sequences of gut microbiome, and non-targeted metabolomics. Statistical analysis and machine learning models were used for functional prediction and exploration.

The repository contains all scripts for paper Restructuring the Gut Microbiota by Intermittent Fasting Lowers Blood Pressure.
Please cite our paper if you use my workflow.
(See other repository for analysis of microbiome 16s rRNA sequencing)
If you are reading this, I assume that you have some basic understanding of using Python, R, and Shell. If you want to know more about packages I used here, please go to their websites. Some codes can be simplified. There are several repeated steps that can be combined if run this workflow in order.
Modify these code to best suit for your needs.

1. required package:
   • python: Numpy, scipy, seaborn, pandas, statsmodels, matplotlib, and scikit-learn
   • R: vegan, DADA2, biomformat,ComplexHeatmap, circlize, ggplot2, RColorBrewer, car, mixOmics, and WRS2.
   • Commandline tools: biobakery_workflow, HUMAnN3, MetaPhlAn3, biom-format
   • Others: LEfSe, GraphPad Prism 9
2. workflows (detailed version coming soon)
3. citations: