pyimmunarch 0.1.0

Pure-Python port of the R package immunarch — bulk and single-cell immune-repertoire (TCR/BCR AIRR-seq) analytics.

py-immunarch

Pure-Python port of the R/CRAN package immunarch — bulk and single-cell immune-repertoire (TCR/BCR AIRR-seq) analytics, by Vadim I. Nazarov and the ImmunoMind team.

pyimmunarch is a standalone, dependency-light re-implementation of immunarch's repertoire-analysis core: data import, exploratory statistics, clonality, diversity estimators, repertoire overlap, gene usage, public clonotypes, clonotype tracking, k-mer analysis, filtering and the standard repertoire plots. It does not require R or rpy2.

PyPI / import name	pyimmunarch
License	Apache 2.0 (same as upstream immunarch)
Upstream	immunarch 0.10.3
Numerical parity	bit-exact vs immunarch (max rel diff ~1e-13)

Install

pip install pyimmunarch          # once published
# or, from a checkout:
pip install -e .

Dependencies: numpy, scipy, pandas, matplotlib, scikit-learn.

Data model

pyimmunarch follows immunarch's data model: an immune dataset is a list of per-sample repertoire DataFrames plus a sample-metadata table. The container class is ImmunData (mirroring R's immdata list, with .data and .meta). Repertoire columns use the immunarch standard (Clones, Proportion, CDR3.nt, CDR3.aa, V.name, D.name, J.name, ...).

Quick start

import pyimmunarch as pim

# 1. load repertoire files (AIRR / immunarch / MiXCR / VDJtools / 10x,
#    auto-detected) or the bundled example dataset
imm = pim.repLoad("my_repertoires/")        # directory with metadata.txt
imm = pim.load_example_immdata()            # bundled example TCR cohort

# 2. exploratory statistics
pim.repExplore(imm, method="volume")        # unique clonotypes per sample
pim.repExplore(imm, method="len", col="aa") # CDR3 length distribution

# 3. clonal-space analysis
pim.repClonality(imm, method="homeo")       # clonal space homeostasis
pim.repClonality(imm, method="top")         # top-N clonal proportion

# 4. diversity estimators
pim.repDiversity(imm, method="chao1")       # Chao1 richness
pim.repDiversity(imm, method="hill")        # Hill numbers
pim.repDiversity(imm, method="div", q=5)    # true diversity
pim.repDiversity(imm, method="gini")        # Gini coefficient
pim.repDiversity(imm, method="raref")       # rarefaction curve

# 5. repertoire overlap
ov = pim.repOverlap(imm, method="jaccard")  # public/overlap/jaccard/...
pim.repOverlapAnalysis(ov, method="mds+hclust")

# 6. gene usage
gu = pim.geneUsage(imm, gene="hs.trbv", norm=True)
pim.geneUsageAnalysis(gu, method="js")

# 7. public clonotypes & tracking
pr = pim.pubRep(imm, col="aa+v")
tc = pim.trackClonotypes(imm, which=(1, 15), col="aa")

# 8. k-mers, filtering, plotting
km  = pim.getKmers(imm[0], 3)
sub = pim.repFilter(imm, "by.meta", {"Status": pim.include("MS")})
pim.vis_overlap_heatmap(ov)

Ported API

immunarch family	pyimmunarch
I/O	repLoad, load_example_immdata, ImmunData, IMMCOL
Exploratory	repExplore (volume / count / len / clones)
Clonality	repClonality (clonal.prop / homeo / top / rare)
Diversity	repDiversity (chao1, hill, div, gini.simp, inv.simp, gini, d50, dxx, raref)
Overlap	repOverlap (public / overlap / jaccard / tversky / cosine / morisita), repOverlapAnalysis
Gene usage	geneUsage, geneUsageAnalysis
Public clonotypes	pubRep, public_matrix, pubRepStatistics, pubRepFilter, pubRepApply
Dynamics	trackClonotypes
K-mers	getKmers, split_to_kmers, kmer_profile, spectratype
Filtering	repFilter, include, exclude, lessthan, morethan, interval
Information theory	entropy, kl_div, js_div, cross_entropy
CDR3 analysis	cdr3_aa_profile
Preprocessing	coding, noncoding, inframes, outofframes, top, bunch_translate
Plotting	vis_diversity, vis_overlap_heatmap, vis_gene_usage, vis_clonal_space, vis_tracking, vis_spectratype, vis_explore

R parity

pyimmunarch is validated against immunarch 0.10.3 on immunarch's own bundled immdata example dataset (a 12-sample TCR cohort). The diversity, overlap and gene-usage numbers are deterministic closed-form formulas, so agreement is bit-exact (maximum relative difference ~1e-13). The test suite (tests/test_r_parity.py) re-runs immunarch via Rscript and asserts rel-diff < 1e-6 for every function family; it skips gracefully when R / immunarch is unavailable.

On the example dataset the Python pipeline runs ~60x faster than the R pipeline (which includes Rscript startup).

python examples/benchmark.py --runs 3
pytest tests/ -q

See examples/compare_R_vs_Python.ipynb for a full R-vs-Python comparison (timing, accuracy table, scatter plots, diversity bar and overlap heatmap).

License

Apache License 2.0 — the same license as the upstream immunarch package. See LICENSE.