pyalakazam 0.1.1

Pure-Python port of the R/CRAN package alakazam -- the core package of the Immcantation framework for adaptive immune receptor repertoire (AIRR-seq) analysis.

pyalakazam

Pure-Python port of the R/CRAN package alakazam — the core package of the Immcantation framework for adaptive immune receptor repertoire (AIRR-seq) analysis (Gupta et al., Bioinformatics 2015).

pyalakazam re-implements the full computational API of alakazam in pure Python (numpy / scipy / pandas / matplotlib) — no rpy2, no R, no external PHYLIP binary. It operates on AIRR-format pandas.DataFrames with the standard columns sequence_id, v_call, d_call, j_call, junction, junction_aa, clone_id, … and aims for numerical parity with alakazam 1.4.3.

Installation

pip install pyalakazam

Or from source:

git clone https://github.com/omicverse/py-alakazam
cd py-alakazam && pip install .

Coverage

Area	Functions
Gene annotation	getSegment, getAllele, getGene, getFamily, getLocus, getChain, countGenes, sortGenes
Diversity / abundance	alphaDiversity, rarefyDiversity, testDiversity, calcDiversity, calcCoverage, estimateAbundance, countClones
Amino-acid properties	aminoAcidProperties, gravy, bulk, polar, aliphatic, charge, isValidAASeq, countPatterns
Sequence utilities	translateDNA, maskSeqGaps, maskSeqEnds, padSeqEnds, extractVRegion, collapseDuplicates
Sequence distances	seqDist, seqEqual, pairwiseDist, pairwiseEqual, nonsquareDist, getDNAMatrix, getAAMatrix
Lineage	makeChangeoClone, buildPhylipLineage, graphToPhylo, phyloToGraph
Topology	getPathLengths, getMRCA, tableEdges, summarizeSubtrees, permuteLabels, testEdges, testMRCA
Plotting	plotGeneUsage, plotDiversityCurve, plotAbundanceCurve, plotLineageTree
Core / data	translateStrings, checkColumns, stoufferMeta, load_example_db, load_single_db, load_example_trees

Quick-start

import pyalakazam as ak

# bundled example B-cell repertoire (alakazam ExampleDb)
db = ak.load_example_db()

# gene-usage frequencies
genes = ak.countGenes(db, gene="v_call", groups="sample_id", mode="family")

# clonal diversity (Hill numbers) with bootstrap CIs
div = ak.alphaDiversity(db, group="sample_id", min_q=0, max_q=4, nboot=200)
ak.plotDiversityCurve(div, legend_title="Sample")

# CDR3 physicochemical descriptors
aa = ak.aminoAcidProperties(db[["sequence_id", "junction"]], seq="junction")

# Ig lineage tree (maximum parsimony, no external PHYLIP)
clone = ak.makeChangeoClone(db[db.clone_id == 3138],
                            text_fields=["sample_id", "c_call"],
                            num_fields=["duplicate_count"])
tree = ak.buildPhylipLineage(clone)
ak.plotLineageTree(tree, label_field="c_call")

Numerical parity with R alakazam 1.4.3

tests/test_r_parity.py runs alakazam on its bundled ExampleDb / ExampleTrees datasets and compares against pyalakazam:

• Deterministic functions — gene parsing, countGenes, countClones, aminoAcidProperties, translateDNA, sequence distances, calcCoverage and the topology analyses (getPathLengths, summarizeSubtrees, tableEdges) — agree bit-exactly / to rel-diff < 1e-6.
• Bootstrap functions — estimateAbundance and alphaDiversity use multinomial resampling. R's and NumPy's RNGs differ, so the bootstrap realisations are not bit-identical; the point estimates agree to Pearson r > 0.999 and the confidence intervals to within a few percent.

The lineage caveat (no PHYLIP)

alakazam's buildPhylipLineage shells out to the external PHYLIP dnapars binary. To stay self-contained, pyalakazam re-implements the maximum-parsimony search in pure Python — greedy stepwise addition, NNI + SPR hill-climbing, and a vectorised Fitch / Sankoff small-parsimony reconstruction. The germline is rooted as the outgroup and zero-weight inferred parents are collapsed exactly as alakazam does.

For the 49 trees in ExampleTrees, pyalakazam recovers an identical-length tree for 31 trees; for the rest the topology and total tree length are very close. Two effects explain the small differences: (1) PHYLIP allows ambiguous characters (N) in inferred internal nodes, which can lower the recomputed seqDist edge-weight sum below the strict-parsimony length, and (2) for very large clones the heuristic search may settle in a near- rather than globally-optimal topology. The trees produced are always valid maximum- parsimony reconstructions.

License

AGPL-3, preserving the license of the original alakazam package. The original alakazam is developed by the Kleinstein Lab (Yale University) as part of the Immcantation framework. See LICENSE.

Citation

If you use this port, please cite the original alakazam publication:

Gupta NT, Vander Heiden JA, Uduman M, Gadala-Maria D, Yaari G, Kleinstein SH. Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics 2015.