import cycler
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import prism.util as util
[docs]def scatter_1d(subclone_assignments, num_subclones, depths, fingerprint_fractions, width, height, dpi):
"""Generate annotated scatterplot for one-dimensional PRISM analysis.
:param list subclone_assignments: List containing subclone assignment status for each fingerprint epilocus.
:param int num_subclones: Number of subclones.
:param list depths: List of depths (pattern counts).
:param list fingerprint_fractions: List of fingerprint fractions.
:param float width: Figure width.
:param float height: Figure height.
:param int dpi: Figure DPI.
"""
fig = plt.figure(figsize=(width, height))
ax = fig.add_subplot(111)
ax.grid()
ax.set_axisbelow(True)
ax.set_xlabel('Fraction of fingerprint')
ax.set_ylabel('Depth')
for subclone_index in range(num_subclones):
mask = (subclone_assignments == subclone_index)
ax.scatter(
fingerprint_fractions[mask, 0],
depths[mask, 0],
label=f'S{subclone_index + 1}',
s=60, edgecolors='black', linewidth=0.3,
)
[docs]def scatter_2d(subclone_assignments, num_subclones, fingerprint_fractions, width, height, dpi):
"""Generate annotated scatterplot for two-dimensional PRISM analysis.
:param list subclone_assignments: List containing subclone assignment status for each fingerprint epilocus.
:param int num_subclones: Number of subclones.
:param list fingerprint_fractions: List of fingerprint fractions.
:param list annotation_names: List of the names of annotations.
:param list annotation_mask: List containing annotation status for each fingerprint epilocus.
:param float width: Figure width.
:param float height: Figure height.
:param int dpi: Figure DPI.
"""
fig = plt.figure(figsize=(width, height))
ax = fig.add_subplot(111)
ax.grid()
ax.set_axisbelow(True)
ax.set_xlabel('FF1')
ax.set_ylabel('FF2')
for subclone_index in range(num_subclones):
mask = (subclone_assignments == subclone_index)
ax.scatter(
fingerprint_fractions[mask, 0],
fingerprint_fractions[mask, 1],
label=f'S{subclone_index + 1}',
s=60, edgecolors='black', linewidth=0.3,
)
[docs]def run(input_fp, output_fp, dpi=400, width=4, height=4, scale=1, font_family=None):
util.preset_rc(scale=scale, font_family=font_family)
subclone_assignments = []
depths = []
fingerprint_fractions = []
with open(input_fp) as inFile:
inFile.readline()
for line in inFile.readlines():
header, cluster, subclone, d, c, ffs = util.parse_result_line(line)
subclone_assignments.append(subclone)
depths.append(d)
fingerprint_fractions.append(ffs)
subclone_assignments = np.array(subclone_assignments)
fingerprint_fractions = np.array(fingerprint_fractions)
depths = np.array(depths)
num_subclones = len(set(subclone_assignments))
n_dim = len(fingerprint_fractions[0])
if n_dim == 1:
scatter_1d(subclone_assignments, num_subclones, depths, fingerprint_fractions, width, height, dpi)
elif n_dim == 2:
scatter_2d(subclone_assignments, num_subclones, fingerprint_fractions, width, height, dpi)
plt.legend()
plt.tight_layout()
plt.savefig(output_fp, dpi=dpi)