pyCirclize 1.4.0

Circular visualization in Python

pyCirclize: Circular visualization in Python

Table of contents

• Overview
• Installation
• API Usage
• Code Example
• Not Implemented Features
• Star History

Overview

pyCirclize is a circular visualization python package implemented based on matplotlib. This package is developed for the purpose of easily and beautifully plotting circular figure such as Circos Plot and Chord Diagram in Python. In addition, useful genome and phylogenetic tree visualization methods for the bioinformatics field are also implemented. pyCirclize was inspired by circlize and pyCircos. More detailed documentation is available here.

Fig.1 pyCirclize example plot gallery

Installation

Python 3.8 or later is required for installation.

Install PyPI package:

pip install pycirclize

Install conda-forge package:

conda install -c conda-forge pycirclize

API Usage

API usage is described in each of the following sections in the document.

• Getting Started
• Plot API Example
• Chord Diagram
• Radar Chart
• Circos Plot (Genomics)
• Phylogenetic Tree
• Plot Tips

Code Example

1. Circos Plot

from pycirclize import Circos
import numpy as np
np.random.seed(0)

# Initialize Circos sectors
sectors = {"A": 10, "B": 15, "C": 12, "D": 20, "E": 15}
circos = Circos(sectors, space=5)

for sector in circos.sectors:
    # Plot sector name
    sector.text(f"Sector: {sector.name}", r=110, size=15)
    # Create x positions & random y values
    x = np.arange(sector.start, sector.end) + 0.5
    y = np.random.randint(0, 100, len(x))
    # Plot lines
    track1 = sector.add_track((80, 100), r_pad_ratio=0.1)
    track1.xticks_by_interval(interval=1)
    track1.axis()
    track1.line(x, y)
    # Plot points 
    track2 = sector.add_track((55, 75), r_pad_ratio=0.1)
    track2.axis()
    track2.scatter(x, y)
    # Plot bars
    track3 = sector.add_track((30, 50), r_pad_ratio=0.1)
    track3.axis()
    track3.bar(x, y)

# Plot links 
circos.link(("A", 0, 3), ("B", 15, 12))
circos.link(("B", 0, 3), ("C", 7, 11), color="skyblue")
circos.link(("C", 2, 5), ("E", 15, 12), color="chocolate", direction=1)
circos.link(("D", 3, 5), ("D", 18, 15), color="lime", ec="black", lw=0.5, hatch="//", direction=2)
circos.link(("D", 8, 10), ("E", 2, 8), color="violet", ec="red", lw=1.0, ls="dashed")

circos.savefig("example01.png")

2. Circos Plot (Genomics)

from pycirclize import Circos
from pycirclize.utils import fetch_genbank_by_accid
from pycirclize.parser import Genbank

# Download `NC_002483` E.coli plasmid genbank
gbk_fetch_data = fetch_genbank_by_accid("NC_002483")
gbk = Genbank(gbk_fetch_data)

# Initialize Circos instance with genome size
circos = Circos(sectors={gbk.name: gbk.range_size})
circos.text(f"Escherichia coli K-12 plasmid F\n\n{gbk.name}", size=14)
circos.rect(r_lim=(90, 100), fc="lightgrey", ec="none", alpha=0.5)
sector = circos.sectors[0]

# Plot forward strand CDS
f_cds_track = sector.add_track((95, 100))
f_cds_feats = gbk.extract_features("CDS", target_strand=1)
f_cds_track.genomic_features(f_cds_feats, plotstyle="arrow", fc="salmon", lw=0.5)

# Plot reverse strand CDS
r_cds_track = sector.add_track((90, 95))
r_cds_feats = gbk.extract_features("CDS", target_strand=-1)
r_cds_track.genomic_features(r_cds_feats, plotstyle="arrow", fc="skyblue", lw=0.5)

# Plot 'gene' qualifier label if exists
labels, label_pos_list = [], []
for feat in gbk.extract_features("CDS"):
    start = int(feat.location.start)
    end = int(feat.location.end)
    label_pos = (start + end) / 2
    gene_name = feat.qualifiers.get("gene", [None])[0]
    if gene_name is not None:
        labels.append(gene_name)
        label_pos_list.append(label_pos)
f_cds_track.xticks(label_pos_list, labels, label_size=6, label_orientation="vertical")

# Plot xticks (interval = 10 Kb)
r_cds_track.xticks_by_interval(
    10000, outer=False, label_formatter=lambda v: f"{v/1000:.1f} Kb"
)

circos.savefig("example02.png")

3. Chord Diagram

from pycirclize import Circos
import pandas as pd

# Create matrix dataframe (3 x 6)
row_names = ["F1", "F2", "F3"]
col_names = ["T1", "T2", "T3", "T4", "T5", "T6"]
matrix_data = [
    [10, 16, 7, 7, 10, 8],
    [4, 9, 10, 12, 12, 7],
    [17, 13, 7, 4, 20, 4],
]
matrix_df = pd.DataFrame(matrix_data, index=row_names, columns=col_names)

# Initialize Circos from matrix for plotting Chord Diagram
circos = Circos.initialize_from_matrix(
    matrix_df,
    space=5,
    cmap="tab10",
    label_kws=dict(size=12),
    link_kws=dict(ec="black", lw=0.5, direction=1),
)

circos.savefig("example03.png")

4. Phylogenetic Tree

from pycirclize import Circos
from pycirclize.utils import load_example_tree_file, ColorCycler
from matplotlib.lines import Line2D

# Initialize Circos from phylogenetic tree
tree_file = load_example_tree_file("large_example.nwk")
circos, tv = Circos.initialize_from_tree(
    tree_file,
    r_lim=(30, 100),
    leaf_label_size=5,
    line_kws=dict(color="lightgrey", lw=1.0),
)

# Define group-species dict for tree annotation
# In this example, set minimum species list to specify group's MRCA node
group_name2species_list = dict(
    Monotremata=["Tachyglossus_aculeatus", "Ornithorhynchus_anatinus"],
    Marsupialia=["Monodelphis_domestica", "Vombatus_ursinus"],
    Xenarthra=["Choloepus_didactylus", "Dasypus_novemcinctus"],
    Afrotheria=["Trichechus_manatus", "Chrysochloris_asiatica"],
    Euarchontes=["Galeopterus_variegatus", "Theropithecus_gelada"],
    Glires=["Oryctolagus_cuniculus", "Microtus_oregoni"],
    Laurasiatheria=["Talpa_occidentalis", "Mirounga_leonina"],
)

# Set tree line color & label color
ColorCycler.set_cmap("tab10")
group_name2color = {name: ColorCycler() for name in group_name2species_list.keys()}
for group_name, species_list in group_name2species_list.items():
    color = group_name2color[group_name]
    tv.set_node_line_props(species_list, color=color, apply_label_color=True)

# Plot figure & set legend on center
fig = circos.plotfig()
_ = circos.ax.legend(
    handles=[Line2D([], [], label=n, color=c) for n, c in group_name2color.items()],
    labelcolor=group_name2color.values(),
    fontsize=6,
    loc="center",
    bbox_to_anchor=(0.5, 0.5),
)
fig.savefig("example04.png")

5. Radar Chart

from pycirclize import Circos
import pandas as pd

# Create RPG jobs parameter dataframe (3 jobs, 7 parameters)
df = pd.DataFrame(
    data=[
        [80, 80, 80, 80, 80, 80, 80],
        [90, 20, 95, 95, 30, 30, 80],
        [60, 90, 20, 20, 100, 90, 50],
    ],
    index=["Hero", "Warrior", "Wizard"],
    columns=["HP", "MP", "ATK", "DEF", "SP.ATK", "SP.DEF", "SPD"],
)

# Initialize Circos instance for radar chart plot
circos = Circos.radar_chart(
    df,
    vmax=100,
    marker_size=6,
    grid_interval_ratio=0.2,
)

# Plot figure & set legend on upper right
fig = circos.plotfig()
_ = circos.ax.legend(loc="upper right", fontsize=10)
fig.savefig("example05.png")

Not Implemented Features

List of features implemented in other Circos plotting tools but not yet implemented in pyCirclize. I may implement them when I feel like it.

• Plot histogram
• Plot boxplot
• Plot violin
• Plot curved text
• Adjust overlap label position

Star History