Circular Visualization Implementation using pyCirclize

Key Features and Customization Options for Circular Plots

Installation pyCirclize in Python

To install pyCirclize, use the following pip command:

pip install pycirclize

Example 1: Circular Visualization Layout

Circular data visualization simplifies understanding complex relationships in data, like networks and flows.
It useful for analyzing data on different sectors, each representing a category. Within each sector, we can have multiple tracks for plotting various data points

Python

from pycirclize import Circos

# Define sectors
sectors = {"X": 8, "Y": 14, "Z": 10, "W": 18, "V": 13}
circos = Circos(sectors, space=4)

for sector in circos.sectors:
    # Plot sector axis and name
    sector.axis(fc="none", ls="solid", lw=1.5, ec="grey", alpha=0.6)
    sector.text(f"Sector: {sector.name}={sector.size}", size=14)
    # Track 1 (Radius: 70 - 100)
    track1 = sector.add_track((70, 100))
    track1.axis(fc="orange", alpha=0.6)
    track1.text(track1.name)
    # Track 2 (Radius: 40 - 65)
    track2 = sector.add_track((40, 65))
    track2.axis(fc="lightblue", alpha=0.6)
    track2.text(track2.name)
    # Track 3 (Radius: 10 - 35)
    track3 = sector.add_track((10, 35))
    track3.axis(fc="lightgreen", alpha=0.6)
    track3.text(track3.name)

circos.savefig("example_custom01.png")

Output:

Circular Visualization Layout

Example 2: Visualizing Data on Circular Track

Tracks come with diverse plotting functionalities. Users can plot lines, points, and bars using track.line(), track.scatter(), and track.bar() methods respectively.
Additionally, x-ticks can be plotted using track.xticks_by_interval().

Python

from pycirclize import Circos
import numpy as np

np.random.seed(2)

sectors = {"Alpha": 8, "Beta": 14, "Gamma": 10, "Delta": 18, "Epsilon": 13}
circos = Circos(sectors, space=4)
for sector in circos.sectors:
    # Plot sector name
    sector.text(f"Sector: {sector.name}", r=105, size=14)
    # Generate x positions and random y values for plotting
    x = np.arange(sector.start, sector.end) + 0.5
    y = np.random.randint(0, 100, len(x))
    # Plot line
    line_track = sector.add_track((70, 100), r_pad_ratio=0.1)
    line_track.axis()
    line_track.xticks_by_interval(1)
    line_track.line(x, y)
    # Plot points
    points_track = sector.add_track((40, 65), r_pad_ratio=0.1)
    points_track.axis()
    points_track.scatter(x, y)
    # Plot bar
    bar_track = sector.add_track((10, 35), r_pad_ratio=0.1)
    bar_track.axis()
    bar_track.bar(x, y)

circos.savefig("custom_data_plotting.png")

Output:

Plot Data on Track

Example 3: Link Visualization Between Circular Plot

pyCirclize also facilitates plotting links within or between sectors, allowing visualization of relationships such as networks and flows. Users can customize the appearance of each link.

Python

from pycirclize import Circos

sectors = {"Alpha": 8, "Beta": 18, "Gamma": 13}
name2color = {"Alpha": "purple", "Beta": "green", "Gamma": "orange"}
circos = Circos(sectors, space=4)
for sector in circos.sectors:
    track = sector.add_track((90, 100))
    track.axis(fc=name2color[sector.name])
    track.text(sector.name, color="black", size=11)
    track.xticks_by_interval(1)

# Plot various styles of links
circos.link(("Alpha", 0, 1), ("Alpha", 5, 6))
circos.link(("Alpha", 2, 3), ("Alpha", 6, 5), color="yellow")
circos.link(("Alpha", 7, 8), ("Beta", 2, 1), direction=1, color="magenta")
circos.link(("Beta", 3, 5), ("Gamma", 4, 6), direction=1, ec="blue", lw=1, hatch="\\\\")
circos.link(("Beta", 15, 13), ("Beta", 10, 12), r1=85, r2=85, color="pink", ec="black", lw=2, ls="dotted")
circos.link(("Gamma", 0, 2), ("Beta", 1, 0), direction=1, color="lightblue")
circos.link(("Gamma", 10, 12), ("Alpha", 3, 2), direction=2, color="brown", ec="grey", lw=1, ls="dashed")

circos.savefig("custom_link_visualization.png")

Output:

Link Visualization

Example 4: Chord Diagram from Matrix

Chord diagrams visualize pairwise relationships between objects.
Here, a chord diagram is created from a matrix, where each row and column represents an object, and the matrix cells represent the strength of relationships between objects.

Python

from pycirclize import Circos
import pandas as pd

# Create matrix data (8 x 8)
row_names = list("ABCDEFGH")
col_names = row_names
matrix_data = [
    [45, 110, 50, 15, 115, 120, 110, 170],
    [100, 130, 150, 160, 80, 210, 70, 100],
    [100, 50, 70, 120, 80, 110, 100, 110],
    [60, 130, 25, 180, 190, 170, 70, 90],
    [200, 110, 45, 50, 190, 90, 10, 50],
    [80, 5, 95, 110, 115, 160, 105, 10],
    [160, 90, 100, 140, 95, 40, 90, 150],
    [85, 80, 130, 70, 115, 20, 35, 250],
]
matrix_df = pd.DataFrame(matrix_data, index=row_names, columns=col_names)

# Initialize from matrix
circos = Circos.initialize_from_matrix(
    matrix_df,
    space=2,
    r_lim=(90, 100),
    cmap="tab20",
    ticks_interval=400,
    label_kws=dict(r=92, size=11, color="black"),
)

circos.savefig("custom_chord_matrix.png")

Output:

Chord Diagram from Matrix

Example 5: Chord Diagram from From-To Table

Similar to above example, a chord diagram is created, but this time from a from-to table.
The table contains pairs of objects and the strength of relationships between them.

Python

from pycirclize import Circos
from pycirclize.parser import Matrix
import pandas as pd

# Create from-to table dataframe & convert to matrix
fromto_table_df = pd.DataFrame(
    [
        ["Alpha", "Beta", 12],
        ["Alpha", "Gamma", 6],
        ["Alpha", "Delta", 18],
        ["Alpha", "Epsilon", 22],
        ["Alpha", "Zeta", 4],
        ["Beta", "Alpha", 4],
        ["Beta", "Eta", 18],
        ["Zeta", "Delta", 15],
        ["Zeta", "Epsilon", 3],
        ["Epsilon", "Alpha", 22],
        ["Epsilon", "Delta", 8],
    ],
    columns=["from", "to", "value"],
)
matrix = Matrix.parse_fromto_table(fromto_table_df)

circos = Circos.initialize_from_matrix(
    matrix,
    space=2,
    cmap="plasma",
    ticks_interval=4,
    label_kws=dict(size=11, r=105),
    link_kws=dict(direction=1, ec="grey", lw=0.5),
)

circos.savefig("custom_chord_fromto.png")

Output:

Chord Diagram from From-To Table

Example 6: Human Genome Circos Plot

Similar to above Viral Genome Circos Plot example , but focusing on the human genome.
This Circos plot showcases the structure of the human genome, including chromosomes, genes, and other genomic features, providing a comprehensive view of the genome organization.

Python

from pycirclize import Circos
from pycirclize.utils import ColorCycler, load_eukaryote_example_dataset

# Load hg38 dataset
chr_bed_file, cytoband_file, chr_links = load_eukaryote_example_dataset("hg38")

# Initialize Circos from BED chromosomes
circos = Circos.initialize_from_bed(chr_bed_file, space=2)
circos.text("Human Genome\n(hg38)", deg=300, r=140, size=11)

# Add cytoband tracks from cytoband file
circos.add_cytoband_tracks((90, 95), cytoband_file)

# Create chromosome color mapping
ColorCycler.set_cmap("rainbow") 
chr_names = [s.name for s in circos.sectors]
colors = ColorCycler.get_color_list(len(chr_names))
chr_name2color = {name: color for name, color in zip(chr_names, colors)}

# Plot chromosome names & xticks
for sector in circos.sectors:
    sector.text(sector.name, r=110, size=9, color=chr_name2color[sector.name])
    sector.get_track("cytoband").xticks_by_interval(
        30000000,
        label_size=7,
        label_orientation="vertical",
        label_formatter=lambda v: f"{v / 1000000:.0f} Mb",
    )

# Plot chromosome links
for link in chr_links:
    region1 = (link.query_chr, link.query_start, link.query_end)
    region2 = (link.ref_chr, link.ref_start, link.ref_end)
    color = chr_name2color[link.query_chr]
    if link.query_chr in ("chr1", "chr7", "chr15") and link.query_chr != link.ref_chr:
        circos.link(region1, region2, color=color)

circos.savefig("genome_example02.png")

Output:

Human Genome Circos Plot

Example 7: Circular Histogram Visualization

A circular histogram is an effective way to display frequency data or distributions in a circular format. This method can be particularly useful for visualizing periodic data or cyclical events.
In this example, we will create a circular histogram that represents the frequency of certain events occurring within different sectors. Each sector represents a different category, and within each sector, the frequency of events is displayed as bars.

Python

from pycirclize import Circos
import numpy as np
import matplotlib.pyplot as plt

# Define sectors with different sizes
sectors = {"Category A": 12, "Category B": 8, "Category C": 10, "Category D": 15, "Category E": 5}

# Initialize Circos with defined sectors
circos = Circos(sectors, space=5)

# Generate synthetic frequency data for each sector
np.random.seed(42)
frequencies = {
    "Category A": np.random.randint(1, 10, sectors["Category A"]),
    "Category B": np.random.randint(1, 10, sectors["Category B"]),
    "Category C": np.random.randint(1, 10, sectors["Category C"]),
    "Category D": np.random.randint(1, 10, sectors["Category D"]),
    "Category E": np.random.randint(1, 10, sectors["Category E"])
}

# Add tracks and plot histograms
for sector in circos.sectors:
    sector_name = sector.name
    freq_data = frequencies[sector_name]
    track = sector.add_track((50, 100))
    track.axis(fc="lightgrey")
    track.bar(np.arange(len(freq_data)) + 0.5, freq_data, color="skyblue")

# Save and display the plot
circos.savefig("circular_histogram.png")
plt.show()

Output:

Circular Histogram Visualization

Example 8: Circular Heatmap in Python

A circular heatmap visualizes data intensity in a circular layout, which can be useful for representing data distributions over time or across different categories.
Here, we will create a circular heatmap to represent the intensity of data points within different sectors. Each sector represents a different category, and the intensity of data points is displayed using a heatmap color scale.

Python

from pycirclize import Circos
import numpy as np
import matplotlib.pyplot as plt

# Define sectors
sectors = {"Sector 1": 10, "Sector 2": 15, "Sector 3": 20, "Sector 4": 10, "Sector 5": 15}

# Initialize Circos plot
circos = Circos(sectors, space=5)

# Generate synthetic heatmap data for each sector
np.random.seed(0)
heatmap_data = {
    "Sector 1": np.random.rand(sectors["Sector 1"]),
    "Sector 2": np.random.rand(sectors["Sector 2"]),
    "Sector 3": np.random.rand(sectors["Sector 3"]),
    "Sector 4": np.random.rand(sectors["Sector 4"]),
    "Sector 5": np.random.rand(sectors["Sector 5"])
}

# Add heatmap tracks
for sector in circos.sectors:
    sector_name = sector.name
    data = heatmap_data[sector_name]
    heatmap_track = sector.add_track((75, 100))
    heatmap_track.axis(fc="none")
    heatmap_track.heatmap(data, cmap="viridis")

# Save and display the plot
circos.savefig("circular_heatmap.png")
plt.show()

Output:

Circular Heatmap Visualization

Visualizing Data with pyCirclize: A Guide to Circular Plots

PyCirclize is a versatile Python package designed for creating eye-catching circular visualizations. Inspired by the R package “circlize”, it leverages the capabilities of matplotlib to generate various circular plots, including Circos Plots, Chord Diagrams, and Radar Charts.

In this article, we will implement examples using pyCirclize to demonstrate its capabilities in creating circular visualizations. We’ll cover everything from the basics of installing pyCirclize to advanced use cases like human genome Circos plots and circular heatmaps.

Table of Content

Understanding Circular Visualization
Circular Visualization Implementation using pyCirclize

Installation pyCirclize in Python
Example 1: Circular Visualization Layout
Example 2: Visualizing Data on Circular Track
Example 3: Link Visualization Between Circular Plot
Example 4: Chord Diagram from Matrix
Example 5: Chord Diagram from From-To Table
Example 6: Human Genome Circos Plot
Example 7: Circular Histogram Visualization
Example 8: Circular Heatmap in Python

Key Features and Customization Options for Circular Plots

Circular Visualization Implementation using pyCirclize

Installation pyCirclize in Python

Example 1: Circular Visualization Layout

Example 2: Visualizing Data on Circular Track

Example 3: Link Visualization Between Circular Plot

Example 4: Chord Diagram from Matrix

Example 5: Chord Diagram from From-To Table

Example 6: Human Genome Circos Plot

Example 7: Circular Histogram Visualization

Example 8: Circular Heatmap in Python

Visualizing Data with pyCirclize: A Guide to Circular Plots

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