| 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
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