bioconductor-phyloseq

name	bioconductor-phyloseq
description	phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data.
when_to_use	Use when: Importing and storing complex phylogenetic sequencing data (OTU tables, sample metadata, taxonomy, trees) into a single object using import().; Estimating and visualizing alpha diversity using plot_richness() with measures like "Chao1" or "Shannon".; Performing beta diversity ordination (e.g., PCoA, NMDS, CCA) using ordinate() and visualizing the results with plot_ordination().; Creating explorato. Not for: For advanced network analysis beyond basic ecological similarity, use igraph directly because plot_net() provides only a default Jaccard co-occurrence network.; For complex custom plotting beyond standard wrappers, use ggplot2 directly because phylos
user-invocable	false

phyloseq

Dependencies & Environment

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

• Version: 1.56.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Imports: ade4, ape, Biobase, BiocGenerics, biomformat, Biostrings, cluster, data.table, foreach, ggplot2, igraph, multtest, plyr, reshape2, scales, vegan
• Install: BiocManager::install("phyloseq")

When to Use

• Importing and storing complex phylogenetic sequencing data (OTU tables, sample metadata, taxonomy, trees) into a single object using import().
• Estimating and visualizing alpha diversity using plot_richness() with measures like "Chao1" or "Shannon".
• Performing beta diversity ordination (e.g., PCoA, NMDS, CCA) using ordinate() and visualizing the results with plot_ordination().
• Creating exploratory bar plots of taxonomic abundance across samples using plot_bar().

When NOT to Use

• For advanced network analysis beyond basic ecological similarity, use igraph directly because plot_net() provides only a default Jaccard co-occurrence network.
• For complex custom plotting beyond standard wrappers, use ggplot2 directly because phyloseq wrappers are built on it but may restrict advanced geoms.

Data Requirements

• Pre-clustered phylogenetic sequencing data (OTU/ASV table).
• Associated sample data (metadata), a phylogenetic tree, and/or taxonomic assignments.
• Raw (untrimmed) OTU-clustered data is strictly required when performing richness estimates.

Key Parameters

• measures: Alpha diversity estimators to calculate in plot_richness() (e.g., c("Observed", "Chao1", "Shannon")).
• method: Ordination method for ordinate() (e.g., "PCoA", "NMDS", "CCA").
• distance: Distance metric for ordinate() (e.g., "bray", "unifrac").
• maxdist: Maximum distance required to create an edge in plot_net().
• type: What to plot in plot_ordination() (e.g., "samples", "species", "biplot", "split").
• color / shape / fill: Variables from sample_data or tax_table to map to aesthetics in plotting functions.

Best Practices

• Use raw (untrimmed) OTU-clustered data when performing richness estimates with plot_richness(), as estimates depend heavily on singletons.
• Prune absent taxa using prune_taxa() and taxa_sums() > 0 to remove artifacts before downstream analysis.
• Subset large datasets using subset_taxa() before rendering computationally heavy plots like plot_heatmap() or plot_tree().
• Use scree plots (plot_scree()) to evaluate the relative importance of axes after performing ordination.

Common Pitfalls

• Richness estimates are skewed or incorrect: Using normalized or trimmed data removes singletons necessary for accurate alpha diversity estimation. Fix: Ensure you use raw, untrimmed counts in 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.

Alternatives

• vegan: For underlying ecological distance and ordination calculations without the phyloseq S4 object overhead.
• ape: For direct phylogenetic tree manipulation and plotting outside of microbiome contexts.
• ggplot2: For building custom graphics from scratch when phyloseq plot wrappers are too constrained.

Citations

• McMurdie PJ, Holmes S (2013). "phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data." PLoS ONE, 8(4): e61217.
• Caporaso JG, et al. (2011). "Global Patterns of 16S rRNA Diversity at a Depth of Millions of Sequences Per Sample." PNAS, 108: 4516-22.

References

• Homepage: https://bioconductor.org/packages/phyloseq
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/phyloseq/inst/doc/phyloseq-analysis.html

---

Run this on BioMate

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.

▶ Open phyloseq on BioMate →