import cleanlog
import matplotlib.pyplot as plt
import numpy as np
import pybedtools as pb
import prism.util as util
logger = cleanlog.ColoredLogger('annotate')
[docs]def scatter_1d(subclone_assignments, num_subclones, depths, fingerprint_fractions, annotation_names, annotation_mask, 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 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('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],
s=60, alpha=0.2, linewidth=0,
)
plt.gca().set_prop_cycle(None)
for name, mask in zip(annotation_names, annotation_mask):
ax.scatter(fingerprint_fractions[mask, 0], depths[mask, 0], label=name, marker='x', s=60, lw=1.33)
[docs]def scatter_2d(subclone_assignments, num_subclones, fingerprint_fractions, annotation_names, annotation_mask, 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],
s=60, alpha=0.2, linewidth=0,
)
plt.gca().set_prop_cycle(None)
for name, mask in zip(annotation_names, annotation_mask):
ax.scatter(fingerprint_fractions[mask, 0], fingerprint_fractions[mask, 1], label=name, marker='x', s=60, lw=1.33)
[docs]def run(input_fp, output_fp, bed_fps, annotation_names, output_figure_fp=None, dpi=400, width=4, height=4, scale=1, font_family=None):
if len(bed_fps) != len(annotation_names):
logger.error('The number of bed files and their names should match. (Given %d bed files and %d names.)' % (len(bed_fps), len(annotation_names)))
util.preset_rc(scale=scale, font_family=font_family)
headers = []
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)
headers.append(header)
subclone_assignments.append(subclone)
depths.append(d)
fingerprint_fractions.append(ffs)
header_bed = util.prepare_header_bed(headers)
num_annotations = len(annotation_names)
beds = [pb.BedTool(fp) for fp in bed_fps]
annotation_mask = np.array([[False] * len(headers) for _ in range(num_annotations)])
for annotation_index, bed in enumerate(beds):
for epiloci_index, interval in enumerate(header_bed.intersect(bed, c=True)):
overlap_counts = int(interval.fields[-1])
# If there's overlap with current bed file with annotations, mark as annotated.
annotation_mask[annotation_index][epiloci_index] = (overlap_counts != 0)
with open(output_fp, 'w') as outFile:
with open(input_fp) as inFile:
inFile.readline()
for epiloci_index, line in enumerate(inFile.readlines()):
for annotation_index in range(num_annotations):
annotation = ','.join([annotation_names[i] for i in range(num_annotations) if annotation_mask[i][epiloci_index] == True])
print(line.strip() + '\t%s' % annotation, file=outFile)
if output_figure_fp is not None:
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, annotation_names, annotation_mask, width, height, dpi)
elif n_dim == 2:
scatter_2d(subclone_assignments, num_subclones, fingerprint_fractions, annotation_names, annotation_mask, width, height, dpi)
plt.legend()
plt.tight_layout()
plt.savefig(output_figure_fp, dpi=dpi)