Bioconductor Dirichletmultinomial

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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