Non-ribbon Chord Diagram
Chart overview
Non-ribbon chord diagrams arrange nodes in a circle and connect them with simple lines instead of width-varying ribbons.
Key points
- This variant works well when connection strength is less important than the existence of connections, common in biological and social network analysis.
Python Tutorial
How to create a non-ribbon chord diagram in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
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"Create a circular network diagram (non-ribbon chord diagram) showing 'Gene Interaction Network' for 15 genes involved in a biological pathway. Generate realistic gene interaction data: each gene (TP53, BRCA1, EGFR, MYC, KRAS, etc.) with 2-5 connections representing protein-protein interactions. Position genes in a circle using their pathway order. Draw straight lines between interacting genes. Line color by interaction type: activation (green), inhibition (red), binding (blue). Line thickness by confidence score (0.5-1.0). Node size by number of connections (degree). Label each gene node. Highlight hub genes (>4 connections) with a larger marker. Include a legend for interaction types."
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Python Code Example
# === IMPORTS ===
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
from matplotlib.patches import Arc, FancyArrowPatch
import matplotlib.patheffects as pe
# === USER-EDITABLE PARAMETERS ===
title = "Gene Regulatory Network — Circular Layout"
figsize = (14, 14)
# === EXAMPLE DATASET ===
# Gene names and pathway involvement
genes = [
('TP53', 'Tumor Suppressor'),
('BRCA1', 'DNA Repair'),
('EGFR', 'Growth Factor'),
('MYC', 'Transcription Factor'),
('KRAS', 'Signal Transduction'),
('AKT1', 'Cell Survival'),
('PIK3CA', 'Signal Transduction'),
('PTEN', 'Tumor Suppressor'),
('RB1', 'Cell Cycle'),
('CDK4', 'Cell Cycle'),
('CDKN2A', 'Tumor Suppressor'),
('MDM2', 'P53 Regulation'),
]
# Interactions: (gene1, gene2, type, confidence)
interactions = [
('TP53', 'MDM2', 'inhibition', 0.97),
('MDM2', 'TP53', 'inhibition', 0.95),
('TP53', 'CDKN2A', 'activation', 0.88),
('BRCA1', 'TP53', 'binding', 0.92),
('BRCA1', 'RB1', 'binding', 0.78),
('EGFR', 'PIK3CA', 'activation', 0.94),
('EGFR', 'KRAS', 'activation', 0.91),
('MYC', 'CDK4', 'activation', 0.87),
('KRAS', 'PIK3CA', 'activation', 0.89),
('AKT1', 'PTEN', 'inhibition', 0.93),
('PIK3CA', 'AKT1', 'activation', 0.96),
('PTEN', 'PIK3CA', 'inhibition', 0.91),
('RB1', 'CDK4', 'inhibition', 0.90),
('CDK4', 'RB1', 'inhibition', 0.88),
('CDKN2A', 'CDK4', 'inhibition', 0.92),
]
# Create graph
G = nx.DiGraph()
for gene, pathway in genes:
G.add_node(gene, pathway=pathway)
for gene1, gene2, int_type, conf in interactions:
G.add_edge(gene1, gene2, type=int_type, confidence=conf)
# Print summary
print("=== Gene Regulatory Network ===")
print(f"\nGenes: {len(genes)}")
print(f"Interactions: {len(interactions)}")
print(f"\nHub genes (degree > 3):")
degrees = dict(G.degree())
for gene, degree in sorted(degrees.items(), key=lambda x: x[1], reverse=True):
if degree > 3:
print(f" {gene}: {degree} connections")
# === CREATE CIRCULAR NETWORK DIAGRAM ===
fig, ax = plt.subplots(figsize=figsize, facecolor='#0d1117')
ax.set_facecolor('#0d1117')
# Circular layout
pos = nx.circular_layout(G)
# Pathway colors
pathway_colors = {
'Tumor Suppressor': '#FF6B6B',
'DNA Repair': '#4ECDC4',
'Growth Factor': '#45B7D1',
'Transcription Factor': '#96CEB4',
'Signal Transduction': '#FFEAA7',
'Cell Survival': '#DDA0DD',
'Cell Cycle': '#98D8C8',
'P53 Regulation': '#F7DC6F'
}
# Edge colors by interaction type
edge_color_map = {
'activation': '#00E676',
'inhibition': '#FF5252',
'binding': '#448AFF'
}
# Draw curved edges
for gene1, gene2, data in G.edges(data=True):
x1, y1 = pos[gene1]
x2, y2 = pos[gene2]
color = edge_color_map[data['type']]
width = data['confidence'] * 3
# Create curved arrow
style = "arc3,rad=0.2"
arrow = FancyArrowPatch(
(x1, y1), (x2, y2),
connectionstyle=style,
arrowstyle='-|>',
mutation_scale=15,
color=color,
linewidth=width,
alpha=0.7
)
ax.add_patch(arrow)
# Draw nodes with glow
for gene, pathway in genes:
x, y = pos[gene]
color = pathway_colors[pathway]
size = degrees[gene] * 80 + 200
# Glow effect
for i in range(4, 0, -1):
circle = plt.Circle((x, y), 0.08 * i, color=color, alpha=0.05)
ax.add_patch(circle)
# Main node
ax.scatter(x, y, s=size, c=color, edgecolors='white',
linewidths=2, zorder=10, alpha=0.95)
# Draw labels
label_offset = 0.15
for gene, _ in genes:
x, y = pos[gene]
# Position label outside the circle
angle = np.arctan2(y, x)
lx = x + label_offset * np.cos(angle)
ly = y + label_offset * np.sin(angle)
rotation = np.degrees(angle)
if rotation > 90:
rotation -= 180
elif rotation < -90:
rotation += 180
ax.text(lx, ly, gene, fontsize=11, fontweight='bold',
ha='center', va='center', color='white',
rotation=rotation,
path_effects=[pe.withStroke(linewidth=3, foreground='#0d1117')])
# Legend for edge types
from matplotlib.lines import Line2D
edge_legend = [
Line2D([0], [0], color='#00E676', linewidth=3, label='Activation →'),
Line2D([0], [0], color='#FF5252', linewidth=3, label='Inhibition ⊣'),
Line2D([0], [0], color='#448AFF', linewidth=3, label='Binding â—‹'),
]
legend1 = ax.legend(handles=edge_legend, loc='upper left',
facecolor='#161b22', edgecolor='#30363d',
labelcolor='white', fontsize=10, title='Interaction Type',
title_fontsize=11)
legend1.get_title().set_color('white')
# Legend for pathways
from matplotlib.patches import Patch
pathway_legend = [
Patch(facecolor=color, label=pathway, edgecolor='white')
for pathway, color in list(pathway_colors.items())[:6]
]
legend2 = ax.legend(handles=pathway_legend, loc='upper right',
facecolor='#161b22', edgecolor='#30363d',
labelcolor='white', fontsize=9, title='Pathway',
title_fontsize=10)
legend2.get_title().set_color('white')
ax.add_artist(legend1)
# Title
ax.set_title(title, fontsize=22, fontweight='bold', color='white', pad=20,
path_effects=[pe.withStroke(linewidth=3, foreground='#238636')])
ax.set_xlim(-1.5, 1.5)
ax.set_ylim(-1.5, 1.5)
ax.set_aspect('equal')
ax.axis('off')
plt.tight_layout()
plt.savefig('chart.png', dpi=150, bbox_inches='tight', facecolor='#0d1117')
print("Saved: chart.png")
plt.show()
# END-OF-CODE
Opens the Analyze page with this code pre-loaded and ready to execute
Console Output
=== Gene Regulatory Network === Genes: 12 Interactions: 15 Hub genes (degree > 3): TP53: 4 connections PIK3CA: 4 connections CDK4: 4 connections Saved: chart.png
Common Use Cases
- 1Gene interaction networks
- 2Protein-protein interactions
- 3Simple relationship mapping
- 4Communication patterns
Pro Tips
Order nodes to minimize crossings
Use edge bundling for many connections
Color edges by relationship type
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
networkx
Useful in specialized workflows that complement core Python plotting libraries for non-ribbon-chord-diagram analysis tasks.
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