phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data.
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Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("phyloseq")import().plot_richness() with measures like "Chao1" or "Shannon".ordinate() and visualizing the results with plot_ordination().plot_bar().igraph directly because plot_net() provides only a default Jaccard co-occurrence network.ggplot2 directly because phyloseq wrappers are built on it but may restrict advanced geoms.plot_richness() (e.g., c("Observed", "Chao1", "Shannon")).ordinate() (e.g., "PCoA", "NMDS", "CCA").ordinate() (e.g., "bray", "unifrac").plot_net().plot_ordination() (e.g., "samples", "species", "biplot", "split").sample_data or tax_table to map to aesthetics in plotting functions.plot_richness(), as estimates depend heavily on singletons.prune_taxa() and taxa_sums() > 0 to remove artifacts before downstream analysis.subset_taxa() before rendering computationally heavy plots like plot_heatmap() or plot_tree().plot_scree()) to evaluate the relative importance of axes after performing ordination.plot_richness().plot_bar() is illegible: Plotting all taxa at once creates a solid block of color due to too many OTUs. Fix: Subset to the most abundant taxa or facet by taxonomic rank (e.g., facet_grid=~Genus).UniFrac() takes too long: Calculating UniFrac distances on large datasets (like Global Patterns) is computationally intensive. Fix: Use parallelization or subset the data before running the distance calculation.phyloseq S4 object overhead.phyloseq plot wrappers are too constrained.This skill is the knowledge layer — when, why, and how to use phyloseq. 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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