bio-tcr-bcr-analysis-vdjtools-analysis

Version Compatibility

Reference examples tested with: MiXCR 4.6+, VDJtools 1.2.1+, matplotlib 3.8+, pandas 2.2+, scanpy 1.10+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

VDJtools Analysis

"Compute diversity and overlap for my TCR repertoires" → Calculate repertoire diversity metrics, sample overlap, and perform statistical comparisons between immune repertoire samples.

• CLI: vdjtools CalcDiversityStats, vdjtools OverlapPair, vdjtools PlotFancySpectratype

Basic Usage

Goal: Run VDJtools commands for immune repertoire analysis.

Approach: Invoke VDJtools via Java JAR or wrapper script with appropriate subcommand and options.

# VDJtools requires Java
java -jar vdjtools.jar <command> [options]

# Or with wrapper script
vdjtools <command> [options]

Calculate Diversity Metrics

Goal: Compute repertoire diversity indices (Shannon, Simpson, Chao1, Gini) across samples.

Approach: Run CalcDiversityStats with a metadata file linking sample files to sample IDs and conditions.

# Basic diversity (Shannon, Simpson, Chao1, etc.)
vdjtools CalcDiversityStats \
    -m metadata.txt \
    output_dir/

# Metadata format (tab-separated):
# #file.name    sample.id    condition
# sample1.txt   S1           control
# sample2.txt   S2           treated

Diversity Metrics Explained

Metric	Description	Interpretation
Shannon	Entropy-based diversity	Higher = more diverse
Simpson	Probability two random clones differ	0-1, higher = diverse
InverseSimpson	1/Simpson	Effective number of clones
Chao1	Richness estimator	Total estimated clonotypes
Gini	Inequality coefficient	0=equal, 1=dominated by one
d50	Clones comprising 50% of repertoire	Lower = more oligoclonal

Sample Comparison

Goal: Quantify clonotype sharing and repertoire overlap between samples or conditions.

Approach: Compute pairwise overlap metrics (Jaccard, Morisita-Horn, F2) on amino acid clonotype identities.

# Find overlapping clonotypes
vdjtools OverlapPair \
    -p sample1.txt sample2.txt \
    output_dir/

# Calculate overlap for all pairs
vdjtools CalcPairwiseDistances \
    -m metadata.txt \
    -i aa \
    output_dir/

# Overlap metrics: F2 (frequency-weighted Jaccard), Jaccard, MorisitaHorn

Spectratype Analysis

Goal: Analyze CDR3 length distributions and V/J gene segment usage patterns across samples.

Approach: Generate spectratype (CDR3 length histogram) and segment usage tables via VDJtools commands.

# CDR3 length distribution (spectratype)
vdjtools CalcSpectratype \
    -m metadata.txt \
    output_dir/

# V/J gene usage
vdjtools CalcSegmentUsage \
    -m metadata.txt \
    output_dir/

Clonal Tracking

Goal: Track individual clonotype frequencies across longitudinal timepoints and identify public clones shared across individuals.

Approach: Use TrackClonotypes for temporal tracking and JoinSamples to find public (cross-individual) clonotypes.

# Track clones across timepoints
vdjtools TrackClonotypes \
    -m metadata_timecourse.txt \
    -x time \
    output_dir/

# Identify public clones (shared across individuals)
vdjtools JoinSamples \
    -m metadata.txt \
    -p \
    output_dir/

Input Format

VDJtools accepts MiXCR output or standard format:

# Required columns (tab-separated):
count   frequency   CDR3nt  CDR3aa  V   D   J

# Example:
1500    0.15    TGTGCCAGC...    CASSF...    TRBV5-1*01  TRBD2*01    TRBJ2-7*01

Convert from MiXCR

Goal: Convert MiXCR clonotype output into VDJtools-compatible format.

Approach: Use VDJtools Convert command specifying MiXCR as the source software format.

# Convert MiXCR output to VDJtools format
vdjtools Convert \
    -S mixcr \
    mixcr_clones.txt \
    output.txt

Parse VDJtools Output in Python

Goal: Load VDJtools diversity statistics and overlap matrices into Python for custom analysis and plotting.

Approach: Read tab-delimited VDJtools output files into pandas DataFrames and visualize diversity comparisons.

import pandas as pd

def load_diversity_stats(filepath):
    '''Load VDJtools diversity statistics'''
    df = pd.read_csv(filepath, sep='\t')
    return df

def load_overlap_matrix(filepath):
    '''Load pairwise overlap matrix'''
    df = pd.read_csv(filepath, sep='\t', index_col=0)
    return df

# Plot diversity across samples
def plot_diversity(stats_df, metric='shannon_wiener_index_mean'):
    import matplotlib.pyplot as plt

    plt.figure(figsize=(10, 6))
    plt.bar(stats_df['sample_id'], stats_df[metric])
    plt.xlabel('Sample')
    plt.ylabel(metric)
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.savefig('diversity_plot.png')

Related Skills

• mixcr-analysis - Generate input clonotype tables
• repertoire-visualization - Visualize VDJtools output
• immcantation-analysis - BCR-specific phylogenetics