Core Concepts in Microbiome and Omics Research
The ideas that separate good omics projects from great ones. Compositionality. Confounding. Replication. Effect size. Each concept explained once, clearly, with real consequences.
The concepts that cause the most problems in microbiome projects are not the hard ones. They are the familiar ones that teams assume they understand. Compositionality. Confounding. Replication. Effect size. Each concept explained once, clearly, with real consequences.
Complexity by default. Clarity by design.
Compositionality
Microbiome data is compositional — you measure relative, not absolute, abundances. When one taxon increases, others appear to decrease even if nothing changed. Standard statistical tests assume independence between features. Apply them to compositional data and you get spurious correlations that look real. Use log-ratio transformations or tools designed for compositional data such as ALDEx2 or ANCOM.
Alpha and beta diversity
Alpha diversity describes richness and evenness within a single sample — a single number summarising how complex one community is. Beta diversity describes how different communities are from each other across samples. They answer different questions and require different statistical approaches. Conflating them is one of the most common mistakes in microbiome reporting.
Confounding
A confounding variable correlates with both your treatment and your outcome, making it impossible to separate their effects. Age, diet, BMI, medication use, and antibiotic history confound almost every human microbiome study. Geography and seasonality confound environmental studies. Design for confounders explicitly — matching, stratification, or covariate adjustment — or acknowledge them as limitations. Unnamed confounders become reviewer comments.
Sample size and statistical power
For most microbiome studies, fewer than ten samples per group will not give you reliable effect size estimates for differential abundance. Run a power calculation before sequencing — it is far cheaper than repeating the study. Microbiome effect sizes are typically small and variance is high, so studies that feel adequately powered for other outcomes often aren't for community-level analysis.
Common questions
- What is compositionality and why does it matter?
- Microbiome data is compositional — you measure relative, not absolute, abundances. Standard statistical tests assume independence between features. Apply them to compositional data and you get spurious correlations that look real.
- What is the difference between alpha and beta diversity?
- Alpha diversity describes richness and evenness within a single sample. Beta diversity describes how different communities are from each other across samples. They answer different questions and require different statistical approaches.
- What is confounding in a microbiome study?
- A confounding variable correlates with both your treatment and your outcome. Age, diet, BMI, and antibiotic history confound almost every human microbiome study. Design for it, adjust for it, or acknowledge it — but name it explicitly.
- How do I know if my sample size is sufficient?
- For most microbiome studies, fewer than ten samples per group will not give you reliable effect size estimates. Run a power calculation before sequencing. It is far cheaper than repeating the study.
Related pages
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- AnswersDirect answers to the questions microbiome projects run into most. What platform to use. How much sequencing depth is enough. When to stop troubleshooting and redesign.
- GlossaryPlain definitions for the terms that matter in microbiome and omics work. No inflated jargon. Each term connects to where it appears in practice.
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