Chord Diagram

In [1]:

# %pip install pycirclize

# %pip install pycirclize

1. Chord Diagram From Matrix¶

pyCirclize can plot a Chord Diagram(wiki) from matrix data like circlize or Circos Table Viewer.

1-1. Example1 (3 x 6)¶

This example uses the same matrix data as documented in circlize

In [2]:

from pycirclize import Circos
import pandas as pd

# Create matrix dataframe (3 x 6)
row_names = ["S1", "S2", "S3"]
col_names = ["E1", "E2", "E3", "E4", "E5", "E6"]
matrix_data = [
    [4, 14, 13, 17, 5, 2],
    [7, 1, 6, 8, 12, 15],
    [9, 10, 3, 16, 11, 18],
]
matrix_df = pd.DataFrame(matrix_data, index=row_names, columns=col_names)

# Initialize from matrix (Can also directly load tsv matrix file)
circos = Circos.initialize_from_matrix(
    matrix_df,
    start=-265,
    end=95,
    space=5,
    r_lim=(93, 100),
    cmap="tab10",
    label_kws=dict(r=94, size=12, color="white"),
    link_kws=dict(ec="black", lw=0.5),
)

print(matrix_df)
fig = circos.plotfig()

from pycirclize import Circos import pandas as pd # Create matrix dataframe (3 x 6) row_names = ["S1", "S2", "S3"] col_names = ["E1", "E2", "E3", "E4", "E5", "E6"] matrix_data = [ [4, 14, 13, 17, 5, 2], [7, 1, 6, 8, 12, 15], [9, 10, 3, 16, 11, 18], ] matrix_df = pd.DataFrame(matrix_data, index=row_names, columns=col_names) # Initialize from matrix (Can also directly load tsv matrix file) circos = Circos.initialize_from_matrix( matrix_df, start=-265, end=95, space=5, r_lim=(93, 100), cmap="tab10", label_kws=dict(r=94, size=12, color="white"), link_kws=dict(ec="black", lw=0.5), ) print(matrix_df) fig = circos.plotfig()

    E1  E2  E3  E4  E5  E6
S1   4  14  13  17   5   2
S2   7   1   6   8  12  15
S3   9  10   3  16  11  18

1-2. Example2 (10 x 10)¶

This example uses the 10 x 10 matrix data randomly generated by Circos Table Viewer

In [3]:

from pycirclize import Circos
import pandas as pd

# Create matrix data (10 x 10)
row_names = list("ABCDEFGHIJ")
col_names = row_names
matrix_data = [
    [51, 115, 60, 17, 120, 126, 115, 179, 127, 114],
    [108, 138, 165, 170, 85, 221, 75, 107, 203, 79],
    [108, 54, 72, 123, 84, 117, 106, 114, 50, 27],
    [62, 134, 28, 185, 199, 179, 74, 94, 116, 108],
    [211, 114, 49, 55, 202, 97, 10, 52, 99, 111],
    [87, 6, 101, 117, 124, 171, 110, 14, 175, 164],
    [167, 99, 109, 143, 98, 42, 95, 163, 134, 78],
    [88, 83, 136, 71, 122, 20, 38, 264, 225, 115],
    [145, 82, 87, 123, 121, 55, 80, 32, 50, 12],
    [122, 109, 84, 94, 133, 75, 71, 115, 60, 210],
]
matrix_df = pd.DataFrame(matrix_data, index=row_names, columns=col_names)

# Initialize from matrix (Can also directly load tsv matrix file)
circos = Circos.initialize_from_matrix(
    matrix_df,
    space=3,
    r_lim=(93, 100),
    cmap="tab10",
    ticks_interval=500,
    label_kws=dict(r=94, size=12, color="white"),
)

print(matrix_df)
fig = circos.plotfig()

from pycirclize import Circos import pandas as pd # Create matrix data (10 x 10) row_names = list("ABCDEFGHIJ") col_names = row_names matrix_data = [ [51, 115, 60, 17, 120, 126, 115, 179, 127, 114], [108, 138, 165, 170, 85, 221, 75, 107, 203, 79], [108, 54, 72, 123, 84, 117, 106, 114, 50, 27], [62, 134, 28, 185, 199, 179, 74, 94, 116, 108], [211, 114, 49, 55, 202, 97, 10, 52, 99, 111], [87, 6, 101, 117, 124, 171, 110, 14, 175, 164], [167, 99, 109, 143, 98, 42, 95, 163, 134, 78], [88, 83, 136, 71, 122, 20, 38, 264, 225, 115], [145, 82, 87, 123, 121, 55, 80, 32, 50, 12], [122, 109, 84, 94, 133, 75, 71, 115, 60, 210], ] matrix_df = pd.DataFrame(matrix_data, index=row_names, columns=col_names) # Initialize from matrix (Can also directly load tsv matrix file) circos = Circos.initialize_from_matrix( matrix_df, space=3, r_lim=(93, 100), cmap="tab10", ticks_interval=500, label_kws=dict(r=94, size=12, color="white"), ) print(matrix_df) fig = circos.plotfig()

     A    B    C    D    E    F    G    H    I    J
A   51  115   60   17  120  126  115  179  127  114
B  108  138  165  170   85  221   75  107  203   79
C  108   54   72  123   84  117  106  114   50   27
D   62  134   28  185  199  179   74   94  116  108
E  211  114   49   55  202   97   10   52   99  111
F   87    6  101  117  124  171  110   14  175  164
G  167   99  109  143   98   42   95  163  134   78
H   88   83  136   71  122   20   38  264  225  115
I  145   82   87  123  121   55   80   32   50   12
J  122  109   84   94  133   75   71  115   60  210

1-3. Example3 (10 x 2)¶

This example uses the 10 x 2 matrix data randomly generated by Circos Table Viewer

In [4]:

from pycirclize import Circos
import pandas as pd

# Create matrix data (10 x 2)
row_names = list("ABCDEFGHIJ")
col_names = list("KL")
matrix_data = [
    [83, 79],
    [90, 118],
    [165, 81],
    [121, 77],
    [187, 197],
    [177, 8],
    [141, 127],
    [29, 27],
    [95, 82],
    [107, 39],
]
matrix_df = pd.DataFrame(matrix_data, index=row_names, columns=col_names)

# Define link_kws handler function to customize each link property
def link_kws_handler(from_label: str, to_label: str):
    if from_label in ("C", "G"):
        # Set alpha, zorder values higher than other links for highlighting
        return dict(alpha=0.5, zorder=1.0)
    else:
        return dict(alpha=0.1, zorder=0)

# Initialize from matrix (Can also directly load tsv matrix file)
circos = Circos.initialize_from_matrix(
    matrix_df,
    space=2,
    cmap="Set3",
    label_kws=dict(size=12),
    link_kws=dict(direction=1, ec="black", lw=0.5),
    link_kws_handler=link_kws_handler,
)

print(matrix_df)
fig = circos.plotfig()

from pycirclize import Circos import pandas as pd # Create matrix data (10 x 2) row_names = list("ABCDEFGHIJ") col_names = list("KL") matrix_data = [ [83, 79], [90, 118], [165, 81], [121, 77], [187, 197], [177, 8], [141, 127], [29, 27], [95, 82], [107, 39], ] matrix_df = pd.DataFrame(matrix_data, index=row_names, columns=col_names) # Define link_kws handler function to customize each link property def link_kws_handler(from_label: str, to_label: str): if from_label in ("C", "G"): # Set alpha, zorder values higher than other links for highlighting return dict(alpha=0.5, zorder=1.0) else: return dict(alpha=0.1, zorder=0) # Initialize from matrix (Can also directly load tsv matrix file) circos = Circos.initialize_from_matrix( matrix_df, space=2, cmap="Set3", label_kws=dict(size=12), link_kws=dict(direction=1, ec="black", lw=0.5), link_kws_handler=link_kws_handler, ) print(matrix_df) fig = circos.plotfig()

     K    L
A   83   79
B   90  118
C  165   81
D  121   77
E  187  197
F  177    8
G  141  127
H   29   27
I   95   82
J  107   39

2. Chord Diagram From Table¶

pyCirclize can plot a Chord Diagram from from-to table data.

2-1. Example 1¶

In [5]:

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(
    [
        ["A", "B", 10],
        ["A", "C", 5],
        ["A", "D", 15],
        ["B", "D", 8],
        ["C", "D", 6],
    ],
    columns=["from", "to", "value"], # Column name is optional
)
matrix = Matrix.parse_fromto_table(fromto_table_df)

circos = Circos.initialize_from_matrix(
    matrix,
    space=3,
    cmap=dict(A="royalblue", B="orange", C="green", D="red"),
    label_kws=dict(size=12),
)

print(fromto_table_df.to_string(index=False))
fig = circos.plotfig()

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( [ ["A", "B", 10], ["A", "C", 5], ["A", "D", 15], ["B", "D", 8], ["C", "D", 6], ], columns=["from", "to", "value"], # Column name is optional ) matrix = Matrix.parse_fromto_table(fromto_table_df) circos = Circos.initialize_from_matrix( matrix, space=3, cmap=dict(A="royalblue", B="orange", C="green", D="red"), label_kws=dict(size=12), ) print(fromto_table_df.to_string(index=False)) fig = circos.plotfig()

from to  value
   A  B     10
   A  C      5
   A  D     15
   B  D      8
   C  D      6

2-2. Example 2¶

In [6]:

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(
    [
        ["A", "B", 10],
        ["A", "C", 5],
        ["A", "D", 15],
        ["A", "E", 20],
        ["A", "F", 3],
        ["B", "A", 3],
        ["B", "G", 15],
        ["F", "D", 13],
        ["F", "E", 2],
        ["E", "A", 20],
        ["E", "D", 6],
    ],
    columns=["from", "to", "value"], # Column name is optional
)
matrix = Matrix.parse_fromto_table(fromto_table_df)

circos = Circos.initialize_from_matrix(
    matrix,
    space=3,
    cmap="viridis",
    ticks_interval=5,
    label_kws=dict(size=12, r=110),
    link_kws=dict(direction=1, ec="black", lw=0.5),
)

print(fromto_table_df.to_string(index=False))
fig = circos.plotfig()

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( [ ["A", "B", 10], ["A", "C", 5], ["A", "D", 15], ["A", "E", 20], ["A", "F", 3], ["B", "A", 3], ["B", "G", 15], ["F", "D", 13], ["F", "E", 2], ["E", "A", 20], ["E", "D", 6], ], columns=["from", "to", "value"], # Column name is optional ) matrix = Matrix.parse_fromto_table(fromto_table_df) circos = Circos.initialize_from_matrix( matrix, space=3, cmap="viridis", ticks_interval=5, label_kws=dict(size=12, r=110), link_kws=dict(direction=1, ec="black", lw=0.5), ) print(fromto_table_df.to_string(index=False)) fig = circos.plotfig()

from to  value
   A  B     10
   A  C      5
   A  D     15
   A  E     20
   A  F      3
   B  A      3
   B  G     15
   F  D     13
   F  E      2
   E  A     20
   E  D      6