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log_parserWithSpikeIn.py
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log_parserWithSpikeIn.py
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import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
#import seaborn as sns
import sys
import glob
ORIGINAL_FILE=sys.argv[1]
class BarCodeSeq:
total=0
def __init__(self,name,totalreads):
self.name=name
self.totalreads=int(totalreads)
self.sample=None
self.unique_reads=None
self.dup_reads=None
self.unmapped_reads=None
self.PCR_reads=None
def demultiplexing(title):
barcode_dict=dict()
with open('barcodes.bar','r') as barcode_file:
for line in barcode_file:
temp=line.split('\t')
barcode_dict[temp[0]]=temp[1][9:].rstrip()
with open('out.log','r') as log_file:
log=log_file.readlines()
global Barcode_Objects
Barcode_Objects=[]
for item in log:
if item.startswith("Total FastQ records: "):
temp=item.split(':')
BarCodeSeq.total=int(temp[1])
break
for item in log:
if item.startswith("FastQ records for barcode"):
temp=item[26:].split(':')
Barcode_Objects.append(BarCodeSeq(barcode_dict[temp[0]],temp[1]))
UNMULTIPLEXED_READS=BarCodeSeq.total-sum([item.totalreads for item in Barcode_Objects])
fig,ax=plt.subplots(1,1)
ax.axis('equal')
ax.set_title('Demultiplexing Output\nfor file {}'.format(title), fontsize=18)
_,text,__=ax.pie([sum([item.totalreads for item in Barcode_Objects]),UNMULTIPLEXED_READS], explode=(0, 0.3),
autopct='%1.1f%%', shadow=True, startangle=0,colors=['yellowgreen','lightcoral'],
labels=['Mapped \nTo\n Barcodes \n({})'.format(sum([item.totalreads for item in Barcode_Objects])),
'Unmapped \n({})'.format(UNMULTIPLEXED_READS)])
for item in text:
item.set_fontsize(14)
fig.savefig('demultiplexing.pdf')
def mapped_unmappedOLd(title):
with open('out.log','r') as log_file:
for line in log_file:
if line.startswith('# read'):
sample=int(line.split(' ')[-1].rstrip())
mapped=int(next(log_file).split(' ')[-2])
unmapped=int(next(log_file).split(' ')[-2])
duplicated=int(next(log_file).split(' ')[-2])
for item in Barcode_Objects:
if item.totalreads==sample:
item.unique_reads=mapped
item.unmapped_reads=unmapped
item.dup_reads=duplicated
#assert item.totalreads==item.unique_reads+item.unmapped_reads+item.dup_reads
ind = np.arange(len(Barcode_Objects))
Barcode_Objects.sort(key=lambda x:x.totalreads)
print(ind)
ind=ind+0.2
width=0.7
fig, ax = plt.subplots(figsize=(8,10))
p1 = ax.barh(ind, [item.unique_reads for item in Barcode_Objects], width, color='yellowgreen',label="Unique Mapping")
p2 = ax.barh(ind, [item.dup_reads for item in Barcode_Objects], width,
left=np.array([item.unique_reads for item in Barcode_Objects]),
color='gold',label='Duplicated Mapping')
p3 = ax.barh(ind, [item.unmapped_reads for item in Barcode_Objects], width, color='lightcoral',label="Unmapped Reads",
left=np.array([item.unique_reads for item in Barcode_Objects])+np.array([item.dup_reads for item in Barcode_Objects]))
ax.set_yticks(list(ind+0.4))
ax.set_yticklabels([item.name for item in Barcode_Objects])
ax.set_title('Reads Alignment\nfor file {}'.format(title),fontsize=18)
ax.set_xlabel('Number of Reads Aligned',fontsize=16)
ax.legend(loc='best')
fig.savefig('mapping.pdf')
def mapped_unmapped():
with open('unalignBowtie.log','r') as log_file:
for line in log_file:
if line.startswith("working on:"):
sample=line.rstrip().split(":")[1][9:]
#print("sample", sample)
if line.startswith('# read'):
total=int(line.split(' ')[-1].rstrip())
mapped=int(next(log_file).split(' ')[-2])
unmapped=int(next(log_file).split(' ')[-2])
duplicated=int(next(log_file).split(' ')[-2])
#print("data", sample, total, mapped, unmapped, duplicated)
for item in Barcode_Objects:
if item.name==sample:
item.unique_reads=mapped
item.unmapped_reads=unmapped
item.dup_reads=duplicated
item.totalreads=total
ind = np.arange(len(Barcode_Objects))
Barcode_Objects.sort(key=lambda x:x.totalreads)
print(ind)
ind=ind+0.2
width=0.7
fig, ax = plt.subplots(figsize=(8,10))
p1 = ax.barh(ind, [item.unique_reads for item in Barcode_Objects], width, color='yellowgreen',label="Mapped Reads")
p2 = ax.barh(ind, [item.dup_reads for item in Barcode_Objects], width,
left=np.array([item.unique_reads for item in Barcode_Objects]),
color='gold',label='Duplicated Mapping')
p3 = ax.barh(ind, [item.unmapped_reads for item in Barcode_Objects], width, color='lightcoral',label="Unmapped Reads",
left=np.array([item.unique_reads for item in Barcode_Objects])+np.array([item.dup_reads for item in Barcode_Objects]))
ax.set_yticks(list(ind+0.4))
ax.set_yticklabels([item.name for item in Barcode_Objects])
ax.set_title('Reads Alignment\nfor file {}'.format(ORIGINAL_FILE),fontsize=18)
ax.set_xlabel('Number of Reads Aligned',fontsize=16)
ax.legend(loc='best')
fig.savefig('mapping.pdf')
ratio_dict=dict()
ratio_dict1=dict()
with open('alignedBowtie.log','r') as log_file:
for line in log_file:
if line.startswith("working on:"):
sample=line.rstrip().split(":")[1][15:]
if line.startswith('# read'):
#total=int(line.split(' ')[-1].rstrip())
mapped=int(next(log_file).split(' ')[-2])
unmapped=int(next(log_file).split(' ')[-2])
#duplicated=int(next(log_file).split(' ')[-2])
for item in Barcode_Objects:
if item.name==sample:
ratio_dict[item.name]="%d\t%d\t%d\t%.10f" % (item.totalreads, unmapped, item.unique_reads+item.dup_reads, float(unmapped)/(unmapped+item.unique_reads+item.dup_reads))
ratio_dict1[item.name]=float(unmapped)/(unmapped+item.unique_reads+item.dup_reads)
break
with open("setup.cfg") as init:
demult_par=init.readline().split(':')[1].rstrip()
bowtie_par=init.readline().split(':')[1].rstrip()
macs2_par=init.readline().split(':')[1].rstrip()
normalization_factor_file=open('normalization_factor.txt','w')
normalization_factor_file.write("sample\tinput\ttreatment\ttotal\tspombe\tgenome\tratio\ttotal\tspombe\tgenome\tratio\tnormalizationFactor\n")
for line in init:
items=line.rstrip().split()
name=items[0]
input=items[1]
sample=items[2]
normalization_factor_file.write("%s\t%s\t%s\t%.10f\n" % (line.rstrip(), ratio_dict[input], ratio_dict[sample],ratio_dict1[input]/ratio_dict1[sample]))
normalization_factor_file.close()
def PCR_duplicates():
log_files=glob.glob('IGV/*.log')
#print (log_files)
for log in log_files:
temp=open(log).readlines()
#print(temp)
for item in temp:
#print(item)
if item.find('BAR')!=-1:
bar_line=item
#print(item)
else:pass
barcode=bar_line[bar_line.find('BAR'):bar_line.find('BAR')+5]
#print(barcode)
for item in temp:
#print(item)
if item.find('Unknown')!=-1:
stats_line=item.split('\t')
#print(stats_line)
else:pass
for item in Barcode_Objects:
if item.name==barcode:
item.PCR_reads=stats_line[4]
demultiplexing(ORIGINAL_FILE)
mapped_unmapped()
PCR_duplicates()
n_cols=5
n_rows=int(np.ceil(len(Barcode_Objects)/n_cols) + 1)
#print("length:", len(Barcode_Objects), "col: 5 row: ", int(np.ceil(n_rows)))
fig,ax=plt.subplots(n_cols,n_rows,figsize=(40,40))
max_number_of_reads=[]
for item in Barcode_Objects:
#print(item.name, item.totalreads)
max_number_of_reads.append(item.totalreads)
max_number_of_reads.sort(reverse=True)
for n,item in enumerate(ax.flatten()):
#print(n, item)
if n==len(Barcode_Objects):
break
#print(n, Barcode_Objects[n].name)
item.set_title(Barcode_Objects[n].name,fontsize=36)
item.set_axis_bgcolor('red')
if Barcode_Objects[n].PCR_reads is None:
Barcode_Objects[n].PCR_reads = 0
item.pie([Barcode_Objects[n].unique_reads-int(Barcode_Objects[n].PCR_reads),
Barcode_Objects[n].dup_reads,
Barcode_Objects[n].unmapped_reads,
int(Barcode_Objects[n].PCR_reads)],radius=(Barcode_Objects[n].totalreads)/(max_number_of_reads[0]),colors=['green','blue','red','yellow'])
fig.savefig('final_log.pdf')
print("All done")