bio-tcr-bcr-analysis-repertoire-visualization
Create publication-quality visualizations of immune repertoire data including circos plots, clone tracking, diversity plots, and network graphs. Use when generating figures for repertoire comparisons, clonal dynamics, or V(D)J gene usage.
Best use case
bio-tcr-bcr-analysis-repertoire-visualization is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Create publication-quality visualizations of immune repertoire data including circos plots, clone tracking, diversity plots, and network graphs. Use when generating figures for repertoire comparisons, clonal dynamics, or V(D)J gene usage.
Teams using bio-tcr-bcr-analysis-repertoire-visualization 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-repertoire-visualization/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bio-tcr-bcr-analysis-repertoire-visualization Compares
| Feature / Agent | bio-tcr-bcr-analysis-repertoire-visualization | 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?
Create publication-quality visualizations of immune repertoire data including circos plots, clone tracking, diversity plots, and network graphs. Use when generating figures for repertoire comparisons, clonal dynamics, or V(D)J gene usage.
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+, ggplot2 3.5+, matplotlib 3.8+, pandas 2.2+, scanpy 1.10+, seaborn 0.13+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- 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.
# Repertoire Visualization
**"Visualize my immune repertoire data"** → Create publication-quality figures for TCR/BCR repertoires including circos plots, V(D)J gene usage heatmaps, diversity plots, and clonal tracking across samples.
- CLI: `vdjtools PlotFancyVJUsage` for circos-style V-J plots
- Python: `matplotlib`/`seaborn` for custom repertoire visualizations
## Circos Plots (V-J Gene Usage)
### VDJtools
```bash
# Generate V-J usage circos plot
vdjtools PlotFancyVJUsage \
-m metadata.txt \
output_dir/
# Generates PDF circos plots showing V-J pairing frequencies
```
### Python with pyCircos
```python
import pandas as pd
import matplotlib.pyplot as plt
from pycircos import Gcircle
def plot_vj_circos(clone_df):
'''Create circos plot of V-J usage'''
# Count V-J pairs
vj_counts = clone_df.groupby(['v_gene', 'j_gene']).size().reset_index(name='count')
# Create circos
circle = Gcircle()
# Add arcs for each V and J gene
v_genes = vj_counts['v_gene'].unique()
j_genes = vj_counts['j_gene'].unique()
# Add sectors and links
# ... (complex setup)
circle.save('vj_circos.pdf')
```
### R with circlize
```r
library(circlize)
plot_vj_circos <- function(clone_df) {
# Prepare adjacency matrix
vj_matrix <- table(clone_df$v_gene, clone_df$j_gene)
# Create circos plot
chordDiagram(
vj_matrix,
transparency = 0.5,
annotationTrack = c("grid", "name")
)
}
```
## Clone Tracking Over Time
```python
import pandas as pd
import matplotlib.pyplot as plt
def plot_clone_tracking(clones_by_time, top_n=10):
'''Track top clones across timepoints'''
# Get top clones by total frequency
total_freq = clones_by_time.groupby('cdr3_aa')['frequency'].sum()
top_clones = total_freq.nlargest(top_n).index
fig, ax = plt.subplots(figsize=(10, 6))
for clone in top_clones:
clone_data = clones_by_time[clones_by_time['cdr3_aa'] == clone]
ax.plot(clone_data['timepoint'], clone_data['frequency'],
marker='o', label=clone[:20])
ax.set_xlabel('Timepoint')
ax.set_ylabel('Clone Frequency')
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.savefig('clone_tracking.pdf')
```
## Diversity Plots
```python
import matplotlib.pyplot as plt
import seaborn as sns
def plot_diversity_comparison(diversity_df, metric='shannon'):
'''Compare diversity between groups'''
fig, ax = plt.subplots(figsize=(8, 6))
sns.boxplot(
data=diversity_df,
x='condition',
y=metric,
ax=ax
)
sns.stripplot(
data=diversity_df,
x='condition',
y=metric,
color='black',
alpha=0.5,
ax=ax
)
ax.set_ylabel(f'{metric.capitalize()} Diversity')
plt.savefig('diversity_comparison.pdf')
```
## Overlap Heatmap
```python
def plot_overlap_heatmap(overlap_matrix):
'''Plot pairwise repertoire overlap'''
import seaborn as sns
fig, ax = plt.subplots(figsize=(10, 8))
sns.heatmap(
overlap_matrix,
annot=True,
fmt='.2f',
cmap='YlOrRd',
ax=ax
)
ax.set_title('Repertoire Overlap (Jaccard Index)')
plt.tight_layout()
plt.savefig('overlap_heatmap.pdf')
```
## Spectratype Plot
```python
def plot_spectratype(clone_df, group_col=None):
'''Plot CDR3 length distribution'''
fig, ax = plt.subplots(figsize=(10, 6))
clone_df['cdr3_length'] = clone_df['cdr3_nt'].str.len()
if group_col:
for group, data in clone_df.groupby(group_col):
ax.hist(data['cdr3_length'], bins=range(20, 80, 3),
alpha=0.5, label=group, density=True)
ax.legend()
else:
ax.hist(clone_df['cdr3_length'], bins=range(20, 80, 3))
ax.set_xlabel('CDR3 Length (nt)')
ax.set_ylabel('Density')
ax.set_title('CDR3 Length Distribution (Spectratype)')
plt.savefig('spectratype.pdf')
```
## Clonotype Network
```python
import networkx as nx
def plot_clone_network(clone_df, similarity_threshold=0.8):
'''Create network of similar clonotypes'''
from Levenshtein import ratio
G = nx.Graph()
clones = clone_df['cdr3_aa'].unique()
# Add nodes
for clone in clones:
freq = clone_df[clone_df['cdr3_aa'] == clone]['frequency'].sum()
G.add_node(clone, size=freq)
# Add edges for similar clones
for i, c1 in enumerate(clones):
for c2 in clones[i+1:]:
sim = ratio(c1, c2)
if sim >= similarity_threshold:
G.add_edge(c1, c2, weight=sim)
# Draw network
fig, ax = plt.subplots(figsize=(12, 12))
pos = nx.spring_layout(G)
sizes = [G.nodes[n]['size'] * 1000 for n in G.nodes()]
nx.draw(G, pos, node_size=sizes, with_labels=False, ax=ax)
plt.savefig('clone_network.pdf')
```
## Related Skills
- vdjtools-analysis - Generate input data
- mixcr-analysis - Generate clonotype tables
- data-visualization/ggplot2-fundamentals - General plotting conceptsRelated Skills
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