This repository provides a structured guide to microbiome data visualization, developed as a companion to the iMAP (Integrated Microbiome Analysis Pipeline) framework.
While the iMAP repositories focus on step-by-step microbiome data processing and analysis workflows, this guide concentrates on generating and reading common visualization outputs from processed microbiome data.
This guide builds primarily on outputs generated in:
- iMAP PART 08 – Exploratory Analysis
- Selected downstream analytical components of the iMAP workflow
If you are new to iMAP, we recommend starting with the full project overview:
→ iMAP Project Overview
https://github.com/tmbuza/imap-project-overview
Microbiome analyses commonly produce visual summaries such as:
- Taxonomic composition bar plots
- Alpha diversity measures
- Beta diversity ordination plots
- Heatmaps and clustering visualizations
This guide focuses on:
- Generating reproducible visualization workflows in R
- Understanding what each visualization represents
- Connecting plot structure to underlying data characteristics
- Recognizing basic assumptions behind common microbiome plots
The goal is to strengthen visualization literacy and ensure figures are generated and interpreted responsibly.
The guide is organized into thematic sections covering:
- Data structure and preprocessing considerations
- Taxonomic composition visualization
- Alpha diversity overview
- Beta diversity and ordination basics
- Pattern visualization and clustering summaries
Each section combines reproducible R code with foundational interpretation guidance.
For readers interested in deeper analytical reasoning, advanced interpretation layers, and case-based discussion of microbiome visualization outputs, a structured Visualization & Interpretation edition is available as an extension.
This extended guide expands on the foundations presented here and focuses on interpretive reasoning across multiple analytical outputs.
The iMAP repositories provide the technical workflow for microbiome data analysis, from raw sequence processing to statistical modeling.
This guide complements iMAP by:
- Demonstrating how to visualize processed microbiome outputs
- Reinforcing reproducible analytical practice
- Connecting visualization outputs to biological reasoning
Together, iMAP and this guide support a complete workflow from data processing to responsible interpretation.
All examples in this guide are based on reproducible R workflows.
Session information and software dependencies are documented within the guide.
If you use the iMAP workflow in your research, please consider citing:
Buza, T. M., Tonui, T., Stomeo, F., Tiambo, C., Katani, R., Schilling, M., … Kapur, V. (2019). iMAP: An integrated bioinformatics and visualization pipeline for microbiome data analysis. BMC Bioinformatics, 20. https://doi.org/10.1186/S12859-019-2965-4