Utilities for microbiome analysis.
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Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("microbiome")phyloseq-based exploration and analysis of taxonomic profiling data.miaverse project instead, as microbiome development has been discontinued.TreeSummarizedExperiment data container, use miaverse packages instead of microbiome.phyloseq data format.miaverse project and TreeSummarizedExperiment data container for added capabilities in multi-omics data analysis.phyloseq package and data structures for R-based microbiome analysis.microbiome package development is discontinued. Fix: Shift to miaverse tools based on TreeSummarizedExperiment.TreeSummarizedExperiment objects directly in microbiome. Fix: Use phyloseq format or migrate to miaverse.microbiome for all analyses. Fix: Integrate with the independent phyloseq package and its data structures.microbiome relies heavily upon.TreeSummarizedExperiment data container.This skill is the knowledge layer — when, why, and how to use microbiome. 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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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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