Microbiome Statistics
One-Sentence Definition
Microbiome statistics are the methods for analyzing sparse, compositional count data describing microbial community structure and its association with host or environmental variables.
Simple Explanation
Microbiome tables are mostly zeros and only give proportions — normal statistics mislead unless you account for that.
Detailed Scientific Explanation
| Concept | Meaning |
|---|---|
| Alpha diversity | Within-sample richness/evenness (Shannon, Simpson, observed features) |
| Beta diversity | Between-sample dissimilarity (Bray–Curtis, UniFrac — phylogeny-aware) |
| Ordination | PCoA/NMDS visualization of beta diversity |
| PERMANOVA | Testing group differences in community composition |
| Compositionality | Only relative abundances observed → CLR transforms, ALDEx2, ANCOM-BC |
| Differential abundance | Which taxa differ; high method disagreement — report method explicitly |
| Rarefaction vs normalization | Depth control; contested |
Confounders that dominate real studies: batch effects, DNA extraction kit, sequencing depth, diet, antibiotics, and low-biomass contamination (“kitome”).
Mechanism
Model counts as compositions on a simplex; use log-ratio transforms or distribution-aware models rather than raw proportions.
Clinical Importance
- Underlies claims about dysbiosis in disease, Clostridioides difficile recurrence, and FMT outcomes
Research Importance
- Reproducibility of microbiome findings hinges on these choices
Diagnostic Relevance
- Microbiome-based biomarkers must survive independent-cohort validation (Model Evaluation in Clinical Microbiology)
AMR Relevance
- Resistome burden comparisons across populations use the same statistical framework
Related MOCs
- MOC - Bioinformatics in Microbiology · MOC - Fundamentals of Microbiology · MOC - Clinical Microbiology
Active Recall Questions
- Alpha vs beta diversity?
- Why is compositional data a statistical problem?
- Name two major confounders in microbiome studies.