Preface

This guide focuses on interpretation.

We do not only generate plots. We explain what they represent, what assumptions they carry, and what conclusions are justified.

Welcome

Microbiome studies frequently present:

  • composition bar plots
  • alpha diversity summaries
  • ordination maps
  • heatmaps and clustering patterns

These figures are familiar.

Interpreting them correctly is less common.

A plot can look structured and convincing. That does not automatically make it biologically meaningful.

This guide provides a structured pathway from raw feature tables to careful, defensible visual interpretation.

What This Guide Does

This is a structured, applied guide to microbiome data visualization using R.

We begin with a validated phyloseq object derived from publicly available QIIME2 outputs and build forward from there.

The emphasis is not on generating every possible figure. It is on understanding:

  • what each visualization encodes
  • how preprocessing decisions shape what you see
  • how filtering alters apparent patterns
  • how compositional constraints affect interpretation

Clarity in microbiome analysis begins with structural awareness.

How This Guide Is Organized

The chapters follow a logical progression:

  • Datasets
    Understanding the internal structure of the phyloseq object

  • Data Structure and Prevalence
    Sparsity, sequencing depth variation, and filtering logic

  • Composition Visualization
    Relative abundance, aggregation choices, and compositional limits

  • Diversity Analysis
    What alpha diversity measures — and what it does not

  • Ordination Plots
    What visual separation implies — and what it does not

  • Heatmaps and Patterns
    Recognizing structure without over-claiming

  • Summary and Next Steps
    Extending analysis responsibly

Each chapter combines reproducible code with interpretive framing.

Who This Guide Is For

This guide is written for:

  • Researchers working with 16S microbiome outputs from QIIME2, mothur, or similar pipelines
  • Learners comfortable running R code who want stronger interpretive reasoning
  • Analysts preparing figures for manuscripts, theses, or reports

No advanced statistics are required.

What is required is careful thinking.

Relationship to iMAP

The iMAP repositories provide modular steps for microbiome data processing and exploratory workflows.

This guide does not replace those pipelines.

It begins after feature tables and taxonomy have been generated.

It strengthens the interpretive layer by connecting visualization choices to the questions they are intended to answer.

Together, iMAP and this guide form a coherent path from sequencing output to biological insight.

A figure is not a conclusion.

Interpretation requires understanding structure before pattern.