bio-tcr-bcr-analysis-vdjtools-analysis
Calculate immune repertoire diversity metrics, compare samples, and track clonal dynamics using VDJtools. Use when analyzing repertoire diversity, finding shared clonotypes, or comparing immune profiles between conditions.
Best use case
bio-tcr-bcr-analysis-vdjtools-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Calculate immune repertoire diversity metrics, compare samples, and track clonal dynamics using VDJtools. Use when analyzing repertoire diversity, finding shared clonotypes, or comparing immune profiles between conditions.
Teams using bio-tcr-bcr-analysis-vdjtools-analysis should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/bio-tcr-bcr-analysis-vdjtools-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bio-tcr-bcr-analysis-vdjtools-analysis Compares
| Feature / Agent | bio-tcr-bcr-analysis-vdjtools-analysis | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Calculate immune repertoire diversity metrics, compare samples, and track clonal dynamics using VDJtools. Use when analyzing repertoire diversity, finding shared clonotypes, or comparing immune profiles between conditions.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
## 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.
```bash
# 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.
```bash
# 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.
```bash
# 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.
```bash
# 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.
```bash
# 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.
```bash
# 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.
```python
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
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