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 This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed fu
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Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("DirichletMultinomial")dmn().laplace(), AIC(), or BIC() scores.dmngroup().predict().dmngroup() is designed for discrete phenotypic categories (e.g., 'Lean', 'Obese').dmn() or dmngroup().dmn() or dmngroup().TRUE in predict() or mixture() to return the component/class with the maximum value.TRUE in fitted() to scale the posterior mean difference by theta.k=1:7) and select the best fit using which.min(lplc) on the Laplace scores.cvdmngroup() to perform cross-validation and assess classifier performance via ROC curves.heatmapdmn() to visualize samples arranged by their dominant Dirichlet component.cvdmngroup() is computationally expensive; fix this by using mc.preschedule=FALSE and running it on multiple cores via the parallel package.dmn() violates the multinomial assumption; fix this by ensuring the input is a raw count matrix.vegan: For standard distance-based clustering and ordination rather than generative mixture modeling.phyloseq: For general microbiome data manipulation and visualization.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 — free to start.
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概要と使いどころ
The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
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This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
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apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
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ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
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AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
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