# Bioconductor Dirichletmultinomial

> Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed fu

- Skill: `biomate-ai/bioconductor-dirichletmultinomial` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/bioconductor-dirichletmultinomial`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/bioconductor-dirichletmultinomial/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: biomate-ai (https://skillmd.com/u/biomate-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/biomate-ai/bioconductor-dirichletmultinomial

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# DirichletMultinomial

## Dependencies & Environment

> Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

- **Version:** 1.54.0 · **Bioconductor:** 3.23 · **R:** ≥ 4.6
- **Depends:** S4Vectors, IRanges
- **Imports:** BiocGenerics
- **System requirements:** gsl
- **Install:** `BiocManager::install("DirichletMultinomial")`

## When to Use
- Fitting Dirichlet-Multinomial models to microbial metagenomic count data using `dmn()`.
- Identifying the optimal number of Dirichlet components (clusters) by comparing `laplace()`, `AIC()`, or `BIC()` scores.
- Building generative classifiers for phenotypic groups using `dmngroup()`.
- Assigning new samples to classes using `predict()`.

## When NOT to Use
- For continuous covariate modeling, use alternative regression frameworks because `dmngroup()` is designed for discrete phenotypic categories (e.g., 'Lean', 'Obese').

## Data Requirements
- A matrix of raw integer counts (samples in rows, taxa in columns).
- Phenotypic information as a factor for classification.

## Key Parameters
- **count** (default): The matrix of samples by taxa counts provided to `dmn()` or `dmngroup()`.
- **k** (default): The number of Dirichlet components to model in `dmn()` or `dmngroup()`.
- **assign** (FALSE): Set to `TRUE` in `predict()` or `mixture()` to return the component/class with the maximum value.
- **scale** (FALSE): Set to `TRUE` in `fitted()` to scale the posterior mean difference by theta.

## Best Practices
- Fit models across a range of components (e.g., `k=1:7`) and select the best fit using `which.min(lplc)` on the Laplace scores.
- Use `cvdmngroup()` to perform cross-validation and assess classifier performance via ROC curves.
- Use `heatmapdmn()` to visualize samples arranged by their dominant Dirichlet component.

## Common Pitfalls
- Cross-validation with `cvdmngroup()` is computationally expensive; fix this by using `mc.preschedule=FALSE` and running it on multiple cores via the `parallel` package.
- Passing non-integer or normalized data to `dmn()` violates the multinomial assumption; fix this by ensuring the input is a raw count matrix.

## Alternatives
- `vegan`: For standard distance-based clustering and ordination rather than generative mixture modeling.
- `phyloseq`: For general microbiome data manipulation and visualization.

## Citations
- Holmes et al., 2012, PLoS ONE 7(2): 1-15. https://doi.org/10.1371/journal.pone.0030126

## References
- Homepage: https://bioconductor.org/packages/DirichletMultinomial
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.pdf

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## Run this on BioMate

This skill is the **knowledge layer** — when, why, and how to use `dirichletmultinomial`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=dirichletmultinomial)** — free to start.

▶ **[Open `dirichletmultinomial` on BioMate →](https://www.biomate.ai?ref=kb&pkg=dirichletmultinomial)**