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OptiTypePipeline.py
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# coding=utf-8
"""
###################################################################
OptiType: precision HLA typing from next-generation sequencing data
###################################################################
Authors: András Szolek, Benjamin Schubert, Christopher Mohr
Date: April 2014
Version: 1.0
License: OptiType is released under a three-clause BSD license
Introduction:
-------------
OptiType, is a novel HLA genotyping algorithm based on integer linear
programming, capable of producing accurate 4-digit HLA genotyping predictions
from NGS data by simultaneously selecting all minor and major HLA-I alleles.
Requirements:
-------------
OptiType uses the following software and libraries:
1) Python 2.7
2) Biopython 1.63
3) Pyomo 4.1
4) Matplotlib 1.3.1
5) Pandas 0.12 (with HDF5 support)
6) HDF5 1.8.11
7) RazerS 3.1
8) Cplex 12.5
Please make sure you have installed said software/libraries
and their dependencies.
Installation:
-------------
First install all required software and libraries and register the static path
in the configuration file for RazerS 3.1. CPLEX should be globally executable
via command line. Alternative ILP solver supported by Cooper are also usable.
Please do not change the folder structure or make sure you changed the necessary
entries in the config file.
Usage:
-------------
1) First filter the read files with the following settings:
>razers3 --percent-identity 90 --max-hits 1 --distance-range 0
--output-format sam --output sample_fished.sam
./data/hla_reference.fasta sample.fastq
where reference.fasta is either nuc_reference.fasta or gen_reference.fasta
depending on the type of NGS data. The references can be found in the ./data
sub-folder or in the supplementary material. To use the results as input
for OptiType the sam-files have to be converted into fastq format. On Unix-
based operating system you can convert from sam to fastq with the following
command:
>cat sample_fished.sam | grep -v ^@
| awk '{print "@"$1"\n"$10"\n+\n"$11}' > sample_fished.fastq
For paired-end data pre-process each file individually.
2) After pre-filtering, OptiType can be called as follows:
>python OptiTypePipeline.py -i sample_fished_1.fastq [sample_fished_2.fastq]
(--rna | --dna) [--beta BETA] [--enumerate ENUMERATE]
--o ./out_dir/
This will produce a CSV with the optimal typing and possible sub-optimal
typings if specified, as well as a coverage plot of the genotype for
diagnostic purposes and a HTML file containing a summary of the results.
>python OptiTypePipeline.py --help
usage: OptiType [-h] --input INPUT [INPUT ...] (--rna | --dna) [--beta BETA]
[--enumerate ENUMERATE] --outdir OUTDIR [--verbose]
OptiType: 4-digit HLA typer
optional arguments:
-h, --help show this help message and exit
--input INPUT [INPUT ...], -i INPUT [INPUT ...]
Fastq files with fished HLA reads. Max two files (for
paired-end)
--rna, -r Specifiying the mapped data as RNA.
--dna, -d Specifiying the mapped data as DNA.
--beta BETA, -b BETA The beta value for for homozygosity detection.
--enumerate ENUMERATE, -e ENUMERATE
The number of enumerations.
--outdir OUTDIR, -o OUTDIR
Specifies the out directory to which all files should
be written
--verbose, -v Set verbose mode on.
"""
from __future__ import print_function
from future import standard_library
standard_library.install_aliases()
from builtins import zip
from builtins import filter
from builtins import map
from builtins import range
## eliminate dependency on an X11 server
import matplotlib
matplotlib.use('Agg')
import sys
import subprocess
import os
import argparse
import configparser
import time
import datetime
import pandas as pd
import hlatyper as ht
import numpy as np
from model import OptiType
from collections import defaultdict
# Try to import pysam, important for RazerS 3 output format
try:
import pysam
PYSAM_AVAILABLE = True
except ImportError:
PYSAM_AVAILABLE = False
freq_alleles = '''A*01:01 A*01:02 A*01:03 A*01:06 A*01:09 A*01:23 A*01:38 A*01:44 A*02:01 A*02:02 A*02:03 A*02:04 A*02:05 A*02:06 A*02:07 A*02:08 A*02:09 A*02:10 A*02:11 A*02:12 A*02:13 A*02:133 A*02:14 A*02:141 A*02:146 A*02:15N A*02:16 A*02:17 A*02:18 A*02:19 A*02:20 A*02:21 A*02:22 A*02:226N A*02:24 A*02:25 A*02:26 A*02:27 A*02:28 A*02:29 A*02:30 A*02:33 A*02:34 A*02:35 A*02:36 A*02:37 A*02:38 A*02:40 A*02:42 A*02:44 A*02:45 A*02:46 A*02:48 A*02:49 A*02:51 A*02:53N A*02:54 A*02:55 A*02:57 A*02:58 A*02:60 A*02:64 A*02:67 A*02:74 A*02:85 A*02:90 A*02:93 A*03:01 A*03:02 A*03:05 A*03:07 A*03:08 A*03:10 A*03:12 A*03:22 A*03:25 A*03:65 A*03:69N A*03:97 A*11:01 A*11:02 A*11:03 A*11:04 A*11:05 A*11:06 A*11:08 A*11:10 A*11:12 A*11:13 A*11:18 A*11:19 A*11:20 A*11:29 A*11:40 A*23:01 A*23:02 A*23:03 A*23:04 A*23:05 A*23:09 A*24:02 A*24:03 A*24:04 A*24:05 A*24:06 A*24:07 A*24:08 A*24:09N A*24:10 A*24:13 A*24:14 A*24:15 A*24:17 A*24:18 A*24:20 A*24:21 A*24:22 A*24:23 A*24:24 A*24:25 A*24:26 A*24:27 A*24:28 A*24:29 A*24:31 A*24:35 A*24:46 A*24:51 A*24:56 A*24:63 A*24:93 A*25:01 A*25:02 A*25:04 A*26:01 A*26:02 A*26:03 A*26:04 A*26:05 A*26:06 A*26:07 A*26:08 A*26:09 A*26:10 A*26:11N A*26:12 A*26:14 A*26:15 A*26:16 A*26:17 A*26:18 A*26:20 A*26:49 A*29:01 A*29:02 A*29:03 A*29:04 A*29:10 A*29:12 A*30:01 A*30:02 A*30:03 A*30:04 A*30:06 A*30:08 A*30:09 A*30:10 A*30:11 A*30:12 A*30:16 A*31:01 A*31:02 A*31:03 A*31:04 A*31:05 A*31:06 A*31:08 A*31:09 A*31:12 A*32:01 A*32:02 A*32:03 A*32:04 A*32:05 A*32:06 A*32:08 A*32:13 A*32:20 A*32:22 A*33:01 A*33:03 A*33:04 A*33:05 A*33:10 A*33:26 A*34:01 A*34:02 A*34:03 A*34:05 A*36:01 A*36:03 A*43:01 A*66:01 A*66:02 A*66:03 A*68:01 A*68:02 A*68:03 A*68:04 A*68:05 A*68:06 A*68:07 A*68:08 A*68:12 A*68:13 A*68:15 A*68:16 A*68:17 A*68:18N A*68:23 A*68:24 A*68:38 A*69:01 A*74:01 A*74:02 A*74:03 A*74:04 A*74:06 A*74:09 A*74:11 A*80:01 A*80:02 B*07:02 B*07:03 B*07:04 B*07:05 B*07:06 B*07:07 B*07:08 B*07:09 B*07:10 B*07:12 B*07:13 B*07:14 B*07:15 B*07:17 B*07:20 B*07:22 B*07:26 B*07:33 B*07:36 B*07:47 B*07:53 B*07:85 B*08:01 B*08:02 B*08:03 B*08:04 B*08:05 B*08:09 B*08:12 B*08:18 B*08:23 B*13:01 B*13:02 B*13:03 B*13:04 B*13:07N B*13:09 B*13:11 B*13:13 B*14:01 B*14:02 B*14:03 B*14:04 B*14:05 B*14:06 B*15:01 B*15:02 B*15:03 B*15:04 B*15:05 B*15:06 B*15:07 B*15:08 B*15:09 B*15:10 B*15:108 B*15:11 B*15:12 B*15:123 B*15:125 B*15:13 B*15:135 B*15:15 B*15:153 B*15:16 B*15:17 B*15:18 B*15:20 B*15:21 B*15:23 B*15:24 B*15:25 B*15:27 B*15:28 B*15:29 B*15:30 B*15:31 B*15:32 B*15:33 B*15:34 B*15:35 B*15:36 B*15:37 B*15:38 B*15:39 B*15:40 B*15:42 B*15:45 B*15:46 B*15:47 B*15:48 B*15:50 B*15:52 B*15:53 B*15:54 B*15:55 B*15:56 B*15:58 B*15:61 B*15:63 B*15:67 B*15:68 B*15:70 B*15:71 B*15:73 B*15:82 B*15:86 B*18:01 B*18:02 B*18:03 B*18:04 B*18:05 B*18:06 B*18:07 B*18:08 B*18:09 B*18:11 B*18:13 B*18:14 B*18:18 B*18:19 B*18:20 B*18:28 B*18:33 B*27:01 B*27:02 B*27:03 B*27:04 B*27:05 B*27:06 B*27:07 B*27:08 B*27:09 B*27:10 B*27:11 B*27:12 B*27:13 B*27:14 B*27:19 B*27:20 B*27:21 B*27:30 B*27:39 B*35:01 B*35:02 B*35:03 B*35:04 B*35:05 B*35:06 B*35:08 B*35:09 B*35:10 B*35:11 B*35:12 B*35:13 B*35:14 B*35:15 B*35:16 B*35:17 B*35:18 B*35:19 B*35:20 B*35:21 B*35:22 B*35:23 B*35:24 B*35:25 B*35:27 B*35:28 B*35:29 B*35:30 B*35:31 B*35:32 B*35:33 B*35:34 B*35:36 B*35:43 B*35:46 B*35:51 B*35:77 B*35:89 B*37:01 B*37:02 B*37:04 B*37:05 B*38:01 B*38:02 B*38:04 B*38:05 B*38:06 B*38:15 B*39:01 B*39:02 B*39:03 B*39:04 B*39:05 B*39:06 B*39:07 B*39:08 B*39:09 B*39:10 B*39:11 B*39:12 B*39:13 B*39:14 B*39:15 B*39:23 B*39:24 B*39:31 B*39:34 B*40:01 B*40:02 B*40:03 B*40:04 B*40:05 B*40:06 B*40:07 B*40:08 B*40:09 B*40:10 B*40:11 B*40:12 B*40:14 B*40:15 B*40:16 B*40:18 B*40:19 B*40:20 B*40:21 B*40:23 B*40:27 B*40:31 B*40:35 B*40:36 B*40:37 B*40:38 B*40:39 B*40:40 B*40:42 B*40:44 B*40:49 B*40:50 B*40:52 B*40:64 B*40:80 B*41:01 B*41:02 B*41:03 B*42:01 B*42:02 B*44:02 B*44:03 B*44:04 B*44:05 B*44:06 B*44:07 B*44:08 B*44:09 B*44:10 B*44:12 B*44:13 B*44:15 B*44:18 B*44:20 B*44:21 B*44:22 B*44:26 B*44:27 B*44:29 B*44:31 B*44:59 B*45:01 B*45:02 B*45:04 B*45:06 B*46:01 B*46:02 B*46:13 B*47:01 B*47:02 B*47:03 B*48:01 B*48:02 B*48:03 B*48:04 B*48:05 B*48:06 B*48:07 B*48:08 B*49:01 B*49:02 B*49:03 B*50:01 B*50:02 B*50:04 B*50:05 B*51:01 B*51:02 B*51:03 B*51:04 B*51:05 B*51:06 B*51:07 B*51:08 B*51:09 B*51:10 B*51:12 B*51:13 B*51:14 B*51:15 B*51:18 B*51:21 B*51:22 B*51:27N B*51:29 B*51:31 B*51:32 B*51:33 B*51:34 B*51:36 B*51:37 B*51:63 B*51:65 B*52:01 B*52:02 B*52:06 B*53:01 B*53:02 B*53:03 B*53:04 B*53:05 B*53:07 B*53:08 B*54:01 B*54:02 B*55:01 B*55:02 B*55:03 B*55:04 B*55:07 B*55:08 B*55:10 B*55:12 B*55:16 B*55:46 B*56:01 B*56:02 B*56:03 B*56:04 B*56:05 B*56:06 B*56:07 B*56:09 B*56:11 B*57:01 B*57:02 B*57:03 B*57:04 B*57:05 B*57:06 B*57:10 B*58:01 B*58:02 B*58:06 B*59:01 B*67:01 B*67:02 B*73:01 B*78:01 B*78:02 B*78:03 B*78:05 B*81:01 B*81:02 B*82:01 B*82:02 B*83:01 C*01:02 C*01:03 C*01:04 C*01:05 C*01:06 C*01:08 C*01:14 C*01:17 C*01:30 C*01:32 C*02:02 C*02:03 C*02:04 C*02:06 C*02:08 C*02:09 C*02:10 C*02:19 C*02:20 C*02:27 C*03:02 C*03:03 C*03:04 C*03:05 C*03:06 C*03:07 C*03:08 C*03:09 C*03:10 C*03:13 C*03:14 C*03:15 C*03:16 C*03:17 C*03:19 C*03:21 C*03:32 C*03:42 C*03:43 C*03:56 C*03:67 C*03:81 C*04:01 C*04:03 C*04:04 C*04:05 C*04:06 C*04:07 C*04:10 C*04:11 C*04:14 C*04:15 C*04:24 C*04:29 C*04:33 C*04:37 C*05:01 C*05:03 C*05:04 C*05:07N C*05:09 C*06:02 C*06:03 C*06:04 C*06:06 C*06:08 C*06:09 C*06:17 C*06:24 C*06:53 C*07:01 C*07:02 C*07:03 C*07:04 C*07:05 C*07:06 C*07:07 C*07:08 C*07:09 C*07:10 C*07:109 C*07:123 C*07:13 C*07:14 C*07:17 C*07:18 C*07:19 C*07:21 C*07:22 C*07:27 C*07:29 C*07:32N C*07:37 C*07:43 C*07:46 C*07:49 C*07:56 C*07:66 C*07:67 C*07:68 C*07:80 C*07:95 C*08:01 C*08:02 C*08:03 C*08:04 C*08:05 C*08:06 C*08:13 C*08:15 C*08:20 C*08:21 C*08:27 C*12:02 C*12:03 C*12:04 C*12:05 C*12:07 C*12:12 C*12:15 C*12:16 C*14:02 C*14:03 C*14:04 C*15:02 C*15:03 C*15:04 C*15:05 C*15:06 C*15:07 C*15:08 C*15:09 C*15:11 C*15:12 C*15:13 C*15:17 C*16:01 C*16:02 C*16:04 C*16:08 C*16:09 C*17:01 C*17:02 C*17:03 C*17:04 C*18:01 C*18:02 A*30:07 B*15:64 B*18:12'''.split(' ')
def is_frequent(allele_id):
# prepare for HLA12345_HLA67890 and use the first part
allele_id = allele_id.split('_')[0]
return table.loc[allele_id]['4digit'] in freq_alleles and table.loc[allele_id]['flags'] == 0 or (table.loc[allele_id]['locus'] in 'HGJ')
def get_4digit(allele_id):
allele_id = allele_id.split('_')[0] # for reconstructed IDs like HLA12345_HLA67890 return HLA12345's 4-digit type
return table.loc[allele_id]['4digit']
def get_types(allele_id):
if not isinstance(allele_id, str):
return allele_id
else:
aa = allele_id.split('_')
if len(aa) == 1:
return table.loc[aa[0]]['4digit']
else:
return table.loc[aa[0]]['4digit'] #+ '/' + table.loc[aa[1]]['4digit']
def get_num_threads(configured_threads):
try:
import multiprocessing
except (ImportError, NotImplementedError):
return 2
if(multiprocessing.cpu_count() < configured_threads):
return multiprocessing.cpu_count()
return configured_threads
if __name__ == '__main__':
this_dir = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
config_default = os.path.join(this_dir, 'config.ini')
parser = argparse.ArgumentParser(description=' OptiType: 4-digit HLA typer', prog='OptiType')
parser.add_argument('--input','-i',
nargs='+',
required=True,
metavar='FQ',
help=(".fastq file(s) (fished or raw) or .bam files stored for re-use, generated by "
"an earlier OptiType run. One file: single-end mode, two files: paired-end mode.")
)
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('--rna','-r',
action="store_true",
help="Use with RNA sequencing data."
)
group.add_argument('--dna','-d',
action="store_true",
help="Use with DNA sequencing data."
)
parser.add_argument('--beta','-b',
type=float,
metavar='B',
default=0.009,
help="The beta value for for homozygosity detection (see paper). Default: 0.009. Handle with care."
)
parser.add_argument('--enumerate','-e',
type=int,
default=1,
metavar='N',
help=("Number of enumerations. OptiType will output the optimal solution and "
"the top N-1 suboptimal solutions in the results CSV. Default: 1")
)
parser.add_argument('--outdir','-o',
required=True,
help="Specifies the out directory to which all files should be written."
)
parser.add_argument('--verbose','-v',
required=False,
action="store_true",
help="Set verbose mode on."
)
parser.add_argument('--config', '-c',
type=argparse.FileType('r'),
default=config_default,
help="Path to config file. Default: config.ini in the same directory as this script"
)
args = parser.parse_args()
if not os.path.isfile(args.config.name):
print ("Config file not found. Place config.ini either alongside this script or use the -c option. "
"See config.ini.example and note that its fields have changed recently.")
sys.exit(-1)
config = configparser.SafeConfigParser(os.environ)
config.read(args.config.name)
unpaired_weight = config.getfloat('behavior', 'unpaired_weight')
use_discordant = config.getboolean('behavior', 'use_discordant')
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
# test if inputs are legit:
if args.beta < 0.0 or args.beta >= 0.1:
print("Beta value is not correctly chosen. Please choose another beta value between [0,0.1]")
sys.exit(-1)
if args.enumerate <= 0:
print("The specified number of enumerations must be bigger than %i"%args.enumeration)
sys.exit(-1)
if len(args.input) not in (1, 2):
print("Number of input files can only be 1 (single-end) or 2 (paired-end)")
sys.exit(-1)
input_extension = args.input[0].split('.')[-1]
assert all(ii.endswith('.' + input_extension) for ii in args.input), 'Mixed input file extensions'
bam_input = (input_extension in ('sam', 'bam', 'SAM', 'BAM')) # otherwise treated as fastq
# Constants
VERBOSE = ht.VERBOSE = bool(args.verbose) # set verbosity setting in hlatyper too
COMMAND = "-i 97 -m 99999 --distance-range 0 -pa -tc %d -o %s %s %s"
ALLELE_HDF = os.path.join(this_dir, 'data/alleles.h5')
MAPPING_REF = {'gen': os.path.join(this_dir, 'data/hla_reference_dna.fasta'),
'nuc': os.path.join(this_dir, 'data/hla_reference_rna.fasta')}
MAPPING_CMD = config.get("mapping", "razers3") + " " + COMMAND
date = datetime.datetime.fromtimestamp(time.time()).strftime('%Y_%m_%d_%H_%M_%S')
out_dir = os.path.join(args.outdir, date)
os.makedirs(out_dir)
if PYSAM_AVAILABLE:
extension = 'bam'
else:
extension = 'sam'
bam_paths = args.input if bam_input else [os.path.join(out_dir, ("%s_%i.%s" % (date, i+1, extension))) for i in range(len(args.input))]
# SETUP variables and OUTPUT samples
ref_type = "nuc" if args.rna else "gen"
is_paired = len(args.input) > 1
out_csv = os.path.join(out_dir, ("%s_result.tsv" % date))
out_plot = os.path.join(out_dir, ("%s_coverage_plot.pdf" % date))
# mapping fished file to reference
if not bam_input:
threads = get_num_threads(config.getint("mapping", "threads"))
if VERBOSE:
print("\nmapping with %s threads..." % threads)
for (i, sample), outbam in zip(enumerate(args.input), bam_paths):
if VERBOSE:
print("\n", ht.now(), "Mapping %s to %s reference..." % (os.path.basename(sample), ref_type.upper()))
subprocess.call(MAPPING_CMD % (threads, outbam,
MAPPING_REF[ref_type], sample), shell=True)
# sam-to-hdf5
table, features = ht.load_hdf(ALLELE_HDF, False, 'table', 'features')
if VERBOSE:
print("\n", ht.now(), "Generating binary hit matrix.")
if is_paired:
# combine matrices for paired-end mapping
pos, read_details = ht.pysam_to_hdf(bam_paths[0])
binary1 = np.sign(pos) # dtype=np.uint16
pos2, read_details2 = ht.pysam_to_hdf(bam_paths[1])
binary2 = np.sign(pos2) # dtype=np.uint16
if not bam_input and config.getboolean('behavior', 'deletebam'):
os.remove(bam_paths[0])
os.remove(bam_paths[1])
id1 = set(binary1.index)
id2 = set(binary2.index)
'''
test if we actually can do paired-end mapping
1) look at the last character 2-1 character if they are always the same if so -> proon them away and do
paired-end
2) if not test if the intersection of ID-binary1 and ID-binary2 has at least 10% of the former read
number -> do paired-end
3) if nothing worked ( perhaps pair-end ID was in the middle or something) raise flag and do single-end
mapping on first input
'''
if len(set([r[-1] for r in id1])) == 1 and len(set([r[-1] for r in id2])) == 1:
# if this case is true you have to edit also all pos,etc,desc indices such that the plotting works correctly
# again .. maybe it is also neccessary to test for the last two characters
cut_last_char = lambda x: x[:-1]
binary1.index = list(map(cut_last_char, binary1.index))
binary2.index = list(map(cut_last_char, binary2.index))
pos.index = list(map(cut_last_char, pos.index))
pos2.index = list(map(cut_last_char, pos2.index))
read_details.index = list(map(cut_last_char, read_details.index))
read_details2.index = list(map(cut_last_char, read_details2.index))
binary_p, binary_mis, binary_un = ht.create_paired_matrix(binary1, binary2)
if binary_p.shape[0] < len(id1) * 0.1:
print(("\nWARNING: Less than 10%% of reads could be paired. Consider an appropriate unpaired_weight setting "
"in your config file (currently %.3f), because you may need to resort to using unpaired reads.") % unpaired_weight)
if unpaired_weight > 0:
if use_discordant:
binary = pd.concat([binary_p, binary_un, binary_mis])
else:
binary = pd.concat([binary_p, binary_un])
else:
binary = binary_p
else:
pos, read_details = ht.pysam_to_hdf(bam_paths[0])
if not bam_input and config.getboolean('behavior', 'deletebam'):
os.remove(bam_paths[0])
binary = np.sign(pos) # dtype=np.uint16
# dimensionality reduction and typing
alleles_to_keep = list(filter(is_frequent, binary.columns))
binary = binary[alleles_to_keep]
if VERBOSE:
print("\n", ht.now(), 'temporary pruning of identical rows and columns')
unique_col, representing = ht.prune_identical_alleles(binary, report_groups=True)
representing_df = pd.DataFrame([[a1, a2] for a1, a_l in representing.items() for a2 in a_l],
columns=['representative', 'represented'])
temp_pruned = ht.prune_identical_reads(unique_col)
if VERBOSE:
print("\n", ht.now(), 'Size of mtx with unique rows and columns:', temp_pruned.shape)
print(ht.now(), 'determining minimal set of non-overshadowed alleles')
minimal_alleles = ht.prune_overshadowed_alleles(temp_pruned)
if VERBOSE:
print("\n", ht.now(), 'Keeping only the minimal number of required alleles', minimal_alleles.shape)
binary = binary[minimal_alleles]
if VERBOSE:
print("\n", ht.now(), 'Creating compact model...')
if is_paired and unpaired_weight > 0:
if use_discordant:
compact_mtx, compact_occ = ht.get_compact_model(binary_p[minimal_alleles],
pd.concat([binary_un, binary_mis])[minimal_alleles], weight=unpaired_weight)
else:
compact_mtx, compact_occ = ht.get_compact_model(binary_p[minimal_alleles],
binary_un[minimal_alleles], weight=unpaired_weight)
else:
compact_mtx, compact_occ = ht.get_compact_model(binary)
allele_ids = binary.columns
groups_4digit = defaultdict(list)
for allele in allele_ids:
type_4digit = get_4digit(allele)
groups_4digit[type_4digit].append(allele)
sparse_dict = ht.mtx_to_sparse_dict(compact_mtx)
threads = get_num_threads(config.getint("ilp", "threads"))
if VERBOSE:
print("\nstarting ilp solver with %s threads..." % threads)
print("\n", ht.now(), 'Initializing OptiType model...')
op = OptiType(sparse_dict, compact_occ, groups_4digit, table, args.beta, 2,
config.get("ilp", "solver"), threads, verbosity=VERBOSE)
result = op.solve(args.enumerate)
if VERBOSE:
print("\n", ht.now(), 'Result dataframe has been constructed...')
result_4digit = result.applymap(get_types)
for iii in ["A1", "A2", "B1", "B2", "C1", "C2"]:
if not iii in result_4digit:
result_4digit[iii] = None
r = result_4digit[["A1", "A2", "B1", "B2", "C1", "C2", "nof_reads", "obj"]]
# write CSV to out. And generate plots
r.to_csv(out_csv, sep="\t",
columns=["A1", "A2", "B1", "B2", "C1", "C2", "nof_reads", "obj"],
header=["A1", "A2", "B1", "B2", "C1", "C2", "Reads", "Objective"])
hlatype = result.iloc[0][["A1", "A2", "B1", "B2", "C1", "C2"]].drop_duplicates().dropna()
features_used = [('intron', 1), ('exon', 2), ('intron', 2), ('exon', 3), ('intron', 3)] \
if not args.rna else [('exon',2),('exon',3)]
plot_variables = [pos, read_details, pos2, read_details2, (binary_p, binary_un, binary_mis)] if is_paired else [pos, read_details]
coverage_mat = ht.calculate_coverage(plot_variables, features, hlatype, features_used)
ht.plot_coverage(out_plot, coverage_mat, table, features, features_used)