Repertoire Visualization
Circos Plots (V-J Gene Usage)
VDJtools
# 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
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
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
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
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
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
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
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 - General plotting concepts
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