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from collections import defaultdict
import pybedtools
import numpy as np
import pandas as pd
import seqcluster.libs.logger as mylog
from seqcluster.libs.classes import sequence
from seqcluster.libs.tool import _normalize_seqs
logger = mylog.getLogger(__name__)
def parse_align_file(file_in):
"""
Parse sam files with aligned sequences
"""
loc_id = 1
bedfile_clusters = ""
bamfile = pybedtools.BedTool(file_in)
bed = pybedtools.BedTool.bam_to_bed(bamfile)
for c, start, end, name, q, strand in bed:
loc_id += 1
bedfile_clusters += "%s\t%s\t%s\t%s\t%s\t%s\n" % \
(c, start, end, name, loc_id, strand)
return bedfile_clusters
def parse_ma_file(seq_obj, in_file):
"""
read seqs.ma file and create dict with
sequence object
"""
name = ""
index = 1
total = defaultdict(int)
ratio = list()
with open(in_file) as handle_in:
line = handle_in.readline().strip()
cols = line.split("\t")
samples = cols[2:]
for line in handle_in:
line = line.strip()
cols = line.split("\t")
name = int(cols[0].replace("seq_", ""))
seq = cols[1]
exp = {}
for i in range(len(samples)):
exp[samples[i]] = int(cols[i+2])
total[samples[i]] += int(cols[i+2])
ratio.append(np.array(list(exp.values())) / np.mean(list(exp.values())))
index = index+1
if name in seq_obj:
seq_obj[name].set_freq(exp)
seq_obj[name].set_seq(seq)
# new_s = sequence(seq, exp, index)
# seq_l[name] = new_s
df = pd.DataFrame(ratio)
df = df[(df.T != 0).all()]
size_factor = dict(zip(samples, df.median(axis=0)))
seq_obj = _normalize_seqs(seq_obj, size_factor)
return seq_obj, total, index
def parse_ma_file_raw(in_file):
"""
read seqs.ma file and create dict with
sequence object
"""
name = ""
index = 1
total = defaultdict(int)
seq_obj = defaultdict(sequence)
ratio = list()
with open(in_file) as handle_in:
line = handle_in.readline().strip()
cols = line.split("\t")
samples = cols[2:]
for line in handle_in:
line = line.strip()
cols = line.split("\t")
name = int(cols[0].replace("seq_", ""))
seq = cols[1]
exp = {}
for i in range(len(samples)):
exp[samples[i]] = int(cols[i+2])
total[samples[i]] += int(cols[i+2])
ratio.append(np.array(list(exp.values())) / np.mean(list(exp.values())))
index = index+1
if name not in seq_obj:
seq_obj[name] = sequence(name)
seq_obj[name].set_freq(exp)
seq_obj[name].set_seq(seq)
df = pd.DataFrame(ratio)
df = df[(df.T != 0).all()]
size_factor = dict(zip(samples, df.median(axis=0)))
seq_obj = _normalize_seqs(seq_obj, size_factor)
return seq_obj, total, index
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