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390 lines (341 loc) · 11.5 KB
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#!/usr/bin/env python3
"""Plot genome depth with nucleotide polymorphisms"""
import argparse
import os
import sys
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
def get_args():
"""Initiate arguments"""
parser = argparse.ArgumentParser(
description='Plot genome depth with nucleotide polymorphisms',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'vcf_file',
help='VCF file with AD format',
default=sys.stdin,
type=str,
)
def float01(x):
try:
x = float(x)
except ValueError as error:
raise argparse.ArgumentTypeError('%r must be float' % (x,)) from error
if x < 0.0 or x > 1.0:
raise argparse.ArgumentTypeError('%r is out of range, must be in [0.0, 1.0]' % (x,))
return x
parser.add_argument(
'-M',
'--major',
help='Threshold to include variant at position',
type=float01,
metavar='FLOAT',
default=0.1,
)
parser.add_argument(
'-m',
'--minor',
help='Threshold to include base variant',
type=float01,
metavar='FLOAT',
default=0.1,
)
parser.add_argument(
'-e',
'--extend',
help='Extend variant bar for INT position to both sides',
type=int,
metavar='INT',
default=4,
)
parser.add_argument(
'-f',
'--figure',
help='Output figure',
type=str,
metavar='STR',
)
parser.add_argument(
'-F',
'--force',
help='Force overwite output',
action='store_true',
)
parser.add_argument(
'-l',
'--log',
help='Plot depth in log scale',
action='store_true'
)
parser.add_argument(
'-D',
'--dpi',
help='Image DPI',
type=int,
metavar='INT',
default=300
)
parser.add_argument(
'-s',
'--size',
help='Size of output',
metavar='FLOAT,FLOAT',
default='16,9',
)
parser.add_argument(
'-g',
'--gap',
help='Gap position and size',
type=str,
metavar='INT,INT[,INT,INT[,INT,INT,...]]',
)
parser.add_argument(
'-x',
'--x-tick',
help='Tick interval',
type=int,
default=500,
)
parser.add_argument(
'-t',
'--table-relative',
help="Save relative variant table to STR. Use '' for STDOUT",
type=str,
metavar='STR',
)
parser.add_argument(
'-T',
'--table-absolute',
help="Save aboslute variant table to STR. Use '' for STDOUT",
type=str,
metavar='STR',
)
parser.add_argument(
'-d',
'--depth',
help='Depth of position to report variant',
type=int,
metavar='INT',
default=20,
)
args = parser.parse_args()
# Check input
if not os.path.isfile(args.vcf_file):
parser.error('Input not exist')
# Check output
if args.figure:
if os.path.isfile(args.figure) and not args.force:
parser.error(f'{args.figure} exist. Use flag -F to overwrite.')
if args.table_relative:
if os.path.isfile(args.table_relative) and not args.force:
parser.error(f'{args.table_relative} exist. Use flag -F to overwrite.')
if args.table_relative == '':
args.table_relative = sys.stdout
if args.table_absolute:
if os.path.isfile(args.table_absolute) and not args.force:
parser.error(f'{args.table_absolute} exist. Use flag -F to overwrite.')
if args.table_absolute == '':
args.table_absolute = sys.stdout
# Check size
try:
fig_size = args.size
fig_size = fig_size.split(',')
if len(fig_size) != 2:
parser.error('Size must contain 2 integers and seperate by ","')
fig_size = [float(x) for x in fig_size]
args.size = fig_size
except ValueError:
parser.error('Size must be integer')
# Check gap
if args.gap:
try:
gap = args.gap
gap = gap.split(',')
gap = [float(x) for x in gap]
gap_decimal = [x%1 for x in gap]
if sum(gap_decimal):
parser.error('Gap must be integer')
if len(gap)%2 != 0:
parser.error('Gap size missing')
gap = [int(x) for x in gap]
gap = [[gap[x], gap[x+1]] for x in range(0, len(gap), 2)]
args.gap = gap
except ValueError:
parser.error('Gap must be integer')
# Check depth
if args.depth < 0:
parser.error('Depth argument must not be negative')
return args
def parse_vcf(vcf_file):
"""
Read VCF file and return vcf dataframe
Extract only pos, ref, alt, and AD in info column
"""
vcf_lines = [line.rstrip().split() for line in vcf_file if line[0] != '#']
pos = [int(line[1]) for line in vcf_lines]
refs = [line[3] for line in vcf_lines]
alts = [line[4].split(',') for line in vcf_lines] # nested list`
ad_pos = vcf_lines[0][8].split(':')
ad_pos = ad_pos.index('AD')
ads = [
[int(x) for x in line[9].split(':')[ad_pos].split(',')[:-1]]
for line in vcf_lines] # nested list
vcf_df = pd.DataFrame({
'pos': pos,
'ref': refs,
'alt': alts,
'ads': ads,
})
return vcf_df
def find_variants(freq_list, major_threshold=0.2, depth=20):
"""Return Boolean value if proportion of variant >= threshold"""
total = sum(freq_list)
if total < depth:
return False
variant_sum = sum(freq_list[1:])
return variant_sum/total > major_threshold # make parameter
def make_depth_df(vcf_df, minor_threshold=0.1, gaps=None):
"""Return df with reference depth and alternative depth"""
depth_list = [] # [A, T, C, G, noVar] for pos x
base_dict = {
'A': 1,
'T': 2,
'C': 3,
'G': 4,
'noVar': 5,
}
for index, row in vcf_df.iterrows():
if row['has_variant']:
entry = [int(row['pos']), 0, 0, 0, 0, 0]
base = row['ref']
base_index = base_dict[base]
depth = row['ads'][0]
entry[base_index] = depth
for i in range(len(row['ads'][1:])):
depth = row['ads'][i+1]
if depth/sum(row['ads']) > minor_threshold: # make parameter
base = row['alt'][i]
base_index = base_dict[base]
entry[base_index] = depth
else:
entry = [int(row['pos']), 0, 0, 0, 0, row['ads'][0]]
entry.append(sum(row['ads']))
depth_list.append(entry)
if gaps:
gaps.sort(key=lambda x: x[0], reverse=True)
for pos, length in gaps:
for entry in depth_list:
if entry[0] >= pos:
entry[0] += length
gap_entries = [[x, 0, 0, 0, 0, 0, np.nan] for x in range(pos, pos+length)]
depth_list = depth_list[:pos-1] + gap_entries + depth_list[pos-1:]
depth_df = pd.DataFrame(
depth_list,
columns=['pos', 'A', 'T', 'C', 'G', 'noVar', 'sum_depth'],
)
return depth_df
def make_variant_df(depth_df, extend=4):
"""Return two df with true variant depth and extended variant depth"""
variant_list = []
for index, row in depth_df.iterrows():
row = list(row)
row = row[:-1]
if row[-1]:
row[-1] = 100
variant_list.append(row)
else:
base_sum = sum(row[1:5])
entry = [row[0]]
for base in row[1:5]:
relative_depth = base/base_sum*100 if base_sum > 0 else 0
entry.append(relative_depth)
entry.append(0)
variant_list.append(entry)
extended_variant_list = list(variant_list)
if extend:
takeover_pos = []
for i, entry in enumerate(extended_variant_list):
if sum(entry[1:5]):
takeover_pos.append(i)
takeover_entry = [extended_variant_list[x] for x in takeover_pos]
for pos, entry in zip(takeover_pos, takeover_entry):
for i in range(-extend,extend+1):
extended_variant_list[pos+i] = entry
variant_df = pd.DataFrame(
variant_list,
columns=['pos', 'A', 'T', 'C', 'G', 'noVar'],
)
extended_variant_df = pd.DataFrame(
extended_variant_list,
columns=['pos', 'A', 'T', 'C', 'G', 'noVar'],
)
return variant_df, extended_variant_df
def main():
"""Plot please"""
args = get_args()
with open(args.vcf_file) as vcf_file:
vcf_df = parse_vcf(vcf_file)
vcf_df['has_variant'] = vcf_df.apply(
lambda x: find_variants(x['ads'], args.major, args.depth), axis=1
)
depth_df = make_depth_df(vcf_df, minor_threshold=args.minor, gaps=args.gap)
variant_df, extended_variant_df = make_variant_df(depth_df, args.extend)
if args.figure:
colors = ['#5772B2', '#3A9276', '#F0430F', '#B615D6', '#DEE0E3']
# fig = plt.figure(figsize=args.size)
plt.rcParams["figure.dpi"] = args.dpi
ax = extended_variant_df.plot.bar(
x='pos',
stacked=True,
color=colors,
figsize=args.size, #(10.5,0.75),
rot=30,
width=1,
ylim=[0,100],
yticks=[0, 50, 100],
xticks=list(range(0, len(extended_variant_df), args.x_tick)),
# legend=False,
)
ax2 = ax.twinx()
depth_df.plot.line(
x='pos',
y='sum_depth',
lw=0.5,
secondary_y=True,
# ylim=[1, max(list(depth_df['sum_depth']).remove(np.inf))],
logy=args.log,
color=['black'],
legend=False,
# xticks=list(range(-1, len(depth_df), 500)) + [0],
ax=ax2,
)
ax.ticklabel_format(axis='x', style='plain')
ax.minorticks_off()
ax.figure.savefig(args.figure, bbox_inches='tight')
if args.table_relative:
relative_variant_df = variant_df[
(variant_df['A'] > 0) | # or
(variant_df['T'] > 0) | # or
(variant_df['C'] > 0) | # or
(variant_df['G'] > 0)
]
relative_variant_df.to_csv(
args.table_relative,
columns=['pos', 'A', 'T', 'C', 'G'],
index=False,
)
if args.table_absolute:
absolute_variant_df = depth_df[
(depth_df['A'] > 0) |
(depth_df['T'] > 0) |
(depth_df['C'] > 0) |
(depth_df['G'] > 0)
]
absolute_variant_df.to_csv(
args.table_absolute,
columns=['pos', 'A', 'T', 'C', 'G'],
index=False,
)
if __name__ == "__main__":
main()
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