pyloseq 1.0.0

A native Python port of R/Bioconductor phyloseq — PyData-native microbiome analysis

pyloseq

Documentation →

A Python port of the R/Bioconductor phyloseq package, built on the PyData stack. pyloseq represents microbiome data as a single object that bundles an OTU/feature table with sample metadata, taxonomic annotations, a phylogenetic tree, and reference sequences. All analysis functions operate on that object directly.

Designed for researchers migrating 16S/ITS workflows from R to Python. Every public function includes an R reference: block in its docstring.

Installation

pip install pyloseq

Requires Python 3.10+.

Quick start

from pyloseq import (
    Phyloseq, OtuTable, SampleData, TaxTable, PhyTree,
    filter_taxa, kOverA, transform_sample_counts,
    estimate_richness, distance, ordinate,
    plot_richness, plot_ordination,
)
from pyloseq.datasets import load_global_patterns_reference

ref = load_global_patterns_reference()

ps = Phyloseq(
    otu=OtuTable(ref["otu_table"], taxa_are_rows=True),
    sam=SampleData(ref["sample_data"]),
    tax=TaxTable(ref["tax_table"]),
    tree=PhyTree.from_newick(ref["phy_tree_newick"]),
)

# Filter rare taxa, normalize to relative abundance
ps = filter_taxa(ps, kOverA(5, 0))
ps_rel = transform_sample_counts(ps, lambda x: x / x.sum())

# Alpha diversity
alpha = estimate_richness(ps, measures=["Shannon", "Simpson"])

# Bray-Curtis PCoA
dm = distance(ps, "bray")
ord_result = ordinate(ps, method="PCoA", distance=dm)

plot_richness(ps, x="SampleType", color="SampleType").draw()
plot_ordination(ps, ord_result, color="SampleType").draw()

Features

Data containers

• Phyloseq — top-level object bundling OTU table, sample metadata, taxonomy, tree, and reference sequences
• Automatic pruning to the intersection of taxa/sample names across components on construction
• Sparse OTU table storage (auto-detected below 50% density)

Input / Output

• BIOM v1 (JSON) and v2 (HDF5) — read and write
• QIIME 2 .qza artifacts — no qiime2 package required
• QIIME 1 legacy OTU tables and mapping files
• mothur .shared, .list + .group, .cons.taxonomy
• Plain CSV/TSV

Manipulation (all functions return new objects; inputs are never modified)

• prune_taxa, prune_samples — subset by explicit name list
• subset_taxa, subset_samples — filter by callable or pandas query string
• filter_taxa, kOverA — abundance-based filtering
• transform_sample_counts — apply any per-sample function (normalization, log transform, etc.)
• rarefy_even_depth — random subsampling to uniform depth
• tax_glom — collapse taxa to a given rank
• tip_glom — collapse by phylogenetic distance
• merge_phyloseq, merge_samples, merge_taxa
• psmelt — wide to long (tidy) format

Diversity

• Alpha: Observed, Chao1, ACE, Shannon, Simpson, InvSimpson, Fisher
• Beta: Bray-Curtis, Jaccard, UniFrac, weighted UniFrac, JSD, DPCoA, and all scipy distance metrics
• distance dispatcher accepts method strings or pre-computed skbio.DistanceMatrix

Ordination

• PCoA / MDS, NMDS, CA, CCA, RDA, CAP, DPCoA
• Constrained methods accept a formula string referencing sample metadata columns
• Returns skbio.OrdinationResults

Plotting (all return plotnine.ggplot objects)

• plot_bar, plot_richness, plot_ordination, plot_heatmap, plot_tree
• make_network / plot_network — sample similarity networks

Hypothesis testing

• multi_tax_test — per-taxon t-test or Wilcoxon rank-sum, with BH, BY, Holm, Bonferroni, or Westfall-Young correction

Dependencies

Package	Min version
numpy	1.24
pandas	2.0
scipy	1.11
scikit-bio	0.7
plotnine	0.13
biom-format	2.1
h5py	3.9
pyarrow	12
pyyaml	6

Development

git clone https://github.com/alittleb3ar/pyloseq
cd pyloseq
pip install -e ".[dev]"
pytest

Tests use golden files generated from R's phyloseq as numerical ground truth. See docs/golden_files.md for details on regenerating them.

License

BSD 3-Clause. See LICENSE.