RepertoireMetrics.jl

A Julia package for computing diversity and clonality metrics for B cell repertoire sequencing data from MIAIRR-formatted files.

Features

• Flexible lineage definition: Use lineage_id, V-J-CDR3 combinations (LineageCollapse.jl compatible), or custom strategies
• Comprehensive metrics: Shannon, Simpson, Gini, Hill numbers, Chao1, D50, and more
• Composable metric selection: Choose which metrics to compute with + operator
• Type-stable design: Proper Julia abstractions for performance
• Multi-donor support: Process single files or entire directories

Installation

using Pkg
Pkg.add("RepertoireMetrics")

Quick Start

using RepertoireMetrics

# Read a repertoire file (supports .tsv and .tsv.gz)
rep = read_repertoire("sample-001.tsv.gz", VJCdr3Definition(); 
                      length_column=:cdr3)

Repertoire{Int64}:
  Donor:      sample-001
  Lineages:   15420
  Total count: 198350
  Top 5 lineages:
    IGHV3-23*01|IGHJ4*02|GCGAGAGATCTTGACTACTGGGGCCAGGGAACC: 856 (0.43%)
    IGHV4-39*01|IGHJ5*02|TGTGCGAGAGTCGATTACTATGATAGTAGTGGT: 724 (0.37%)
    IGHV1-69*01|IGHJ3*02|GCGAGAGATAGTGGCTACGATTTTGACTACTGG: 512 (0.26%)
    IGHV5-51*01|IGHJ4*02|TGTGCGAGACATATTGTGGTGGTAACTGCCCC: 398 (0.2%)
    IGHV3-48*01|IGHJ6*02|GCGAGAGGGGATAGCAGCAGCTGGTACTTTGAC: 287 (0.14%)
    ... and 15415 more

# Compute selected metrics using the composable + operator
metrics = compute_metrics(rep, Depth() + SimpsonDiversity() + Clonality() + GiniCoefficient())

Metrics (4 computed):
  depth: 198350.0
  simpson_diversity: 0.9997
  clonality: 0.0612
  gini_coefficient: 0.548

# Or compute all available metrics (default)
metrics = compute_metrics(rep)