""" Chromosome plotting functions.
Notes
-----
Adapted from Ryan Dale's GitHub Gist for plotting chromosome features. [#Dale]_
References
----------
.. [#Dale] Ryan Dale, GitHub Gist,
https://gist.github.com/daler/c98fc410282d7570efc3#file-ideograms-py
"""
"""
The MIT License (MIT)
Copyright (c) 2016 Ryan Dale
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
"""
MIT License
Copyright (c) 2017 Andrew Riha
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import logging
import os
from atomicwrites import atomic_write
import pandas as pd
import numpy as np
import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot as plt
from matplotlib.collections import BrokenBarHCollection
from matplotlib import patches
logger = logging.getLogger(__name__)
[docs]
def plot_chromosomes(one_chrom_match, two_chrom_match, cytobands, path, title, build):
"""Plots chromosomes with designated markers.
Parameters
----------
one_chrom_match : pandas.DataFrame
segments to highlight on the chromosomes representing one shared chromosome
two_chrom_match : pandas.DataFrame
segments to highlight on the chromosomes representing two shared chromosomes
cytobands : pandas.DataFrame
cytobands table loaded with Resources
path : str
path to destination `.png` file
title : str
title for plot
build : {37}
human genome build
"""
# Height of each chromosome
chrom_height = 1.25
# Spacing between consecutive chromosomes
chrom_spacing = 1
# Decide which chromosomes to use
chromosome_list = ["chr%s" % i for i in range(1, 23)]
chromosome_list.append("chrY")
chromosome_list.append("chrX")
# Keep track of the y positions for chromosomes, and the center of each chromosome
# (which is where we'll put the ytick labels)
ybase = 0
chrom_ybase = {}
chrom_centers = {}
# Iterate in reverse so that items in the beginning of `chromosome_list` will
# appear at the top of the plot
for chrom in chromosome_list[::-1]:
chrom_ybase[chrom] = ybase
chrom_centers[chrom] = ybase + chrom_height / 2.0
ybase += chrom_height + chrom_spacing
# Colors for different chromosome stains
color_lookup = {
"gneg": (202 / 255, 202 / 255, 202 / 255), # background
"one_chrom": (0 / 255, 176 / 255, 240 / 255),
"two_chrom": (66 / 255, 69 / 255, 121 / 255),
"centromere": (1, 1, 1, 0.6),
}
df = _patch_chromosomal_features(cytobands, one_chrom_match, two_chrom_match)
# Add a new column for colors
df["colors"] = df["gie_stain"].apply(lambda x: color_lookup[x])
# Width, height (in inches)
figsize = (6.5, 9)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
# Now all we have to do is call our function for the chromosome data...
for collection in _chromosome_collections(df, chrom_ybase, chrom_height):
ax.add_collection(collection)
# Axes tweaking
ax.set_yticks([chrom_centers[i] for i in chromosome_list])
ax.set_yticklabels(chromosome_list)
ax.margins(0.01)
ax.axis("tight")
handles = []
# setup legend
if len(one_chrom_match) > 0:
one_chrom_patch = patches.Patch(
color=color_lookup["one_chrom"], label="One chromosome shared"
)
handles.append(one_chrom_patch)
if len(two_chrom_match) > 0:
two_chrom_patch = patches.Patch(
color=color_lookup["two_chrom"], label="Two chromosomes shared"
)
handles.append(two_chrom_patch)
no_match_patch = patches.Patch(color=color_lookup["gneg"], label="No shared DNA")
handles.append(no_match_patch)
centromere_patch = patches.Patch(
color=(234 / 255, 234 / 255, 234 / 255), label="Centromere"
)
handles.append(centromere_patch)
plt.legend(handles=handles, loc="lower right", bbox_to_anchor=(0.95, 0.05))
ax.set_title(title, fontsize=14, fontweight="bold")
plt.xlabel("Build " + str(build) + " Chromosome Position", fontsize=10)
logger.info("Saving {}".format(os.path.relpath(path)))
plt.tight_layout()
with atomic_write(path, mode="wb", overwrite=True) as f:
plt.savefig(f)
def _chromosome_collections(df, y_positions, height, **kwargs):
"""
Yields BrokenBarHCollection of features that can be added to an Axes
object.
Parameters
----------
df : pandas.DataFrame
Must at least have columns ['chrom', 'start', 'end', 'color']. If no
column 'width', it will be calculated from start/end.
y_positions : dict
Keys are chromosomes, values are y-value at which to anchor the
BrokenBarHCollection
height : float
Height of each BrokenBarHCollection
Additional kwargs are passed to BrokenBarHCollection
"""
del_width = False
if "width" not in df.columns:
del_width = True
df["width"] = df["end"] - df["start"]
for chrom, group in df.groupby("chrom"):
yrange = (y_positions["chr" + chrom], height)
xranges = group[["start", "width"]].values
yield BrokenBarHCollection(
xranges, yrange, facecolors=group["colors"], **kwargs
)
if del_width:
del df["width"]
def _patch_chromosomal_features(cytobands, one_chrom_match, two_chrom_match):
"""Highlight positions for each chromosome segment / feature.
Parameters
----------
cytobands : pandas.DataFrame
cytoband table from UCSC
one_chrom_match : pandas.DataFrame
segments to highlight on the chromosomes representing one shared chromosome
two_chrom_match : pandas.DataFrame
segments to highlight on the chromosomes representing two shared chromosomes
Returns
-------
df : pandas.DataFrame
the start and stop positions of particular features on each
chromosome
"""
def concat(df, chrom, start, end, gie_stain):
return pd.concat(
[
df,
pd.DataFrame(
{
"chrom": [chrom],
"start": [start],
"end": [end],
"gie_stain": [gie_stain],
}
),
],
ignore_index=True,
)
chromosomes = cytobands["chrom"].unique()
df = pd.DataFrame()
for chromosome in chromosomes:
chromosome_length = np.max(
cytobands[cytobands["chrom"] == chromosome]["end"].values
)
# get all markers for this chromosome
one_chrom_match_markers = one_chrom_match.loc[
one_chrom_match["chrom"] == chromosome
]
two_chrom_match_markers = two_chrom_match.loc[
two_chrom_match["chrom"] == chromosome
]
# background of chromosome
df = concat(df, chromosome, 0, chromosome_length, "gneg")
# add markers for shared DNA on one chromosome
for marker in one_chrom_match_markers.itertuples():
df = concat(df, chromosome, marker.start, marker.end, "one_chrom")
# add markers for shared DNA on both chromosomes
for marker in two_chrom_match_markers.itertuples():
df = concat(df, chromosome, marker.start, marker.end, "two_chrom")
# add centromeres
for item in cytobands.loc[
(cytobands["chrom"] == chromosome) & (cytobands["gie_stain"] == "acen")
].itertuples():
df = concat(df, chromosome, item.start, item.end, "centromere")
return df