def main(args=None):
"""
1. get read counts at different positions either
all of same length or from genomic regions from the BED file
2. compute the scores
"""
args = process_args(args)
if 'BED' in args:
bed_regions = args.BED
else:
bed_regions = None
if len(args.bwfiles) == 1 and not args.outRawCounts:
sys.stderr.write("You've input a single bigWig file and not specified "
"--outRawCounts. The resulting output will NOT be "
"useful with any deepTools program!\n")
num_reads_per_bin = score_bw.getScorePerBin(
args.bwfiles,
args.binSize,
blackListFileName=args.blackListFileName,
numberOfProcessors=args.numberOfProcessors,
stepSize=args.binSize + args.distanceBetweenBins,
verbose=args.verbose,
region=args.region,
bedFile=bed_regions,
chrsToSkip=args.chromosomesToSkip,
out_file_for_raw_data=args.outRawCounts)
sys.stderr.write("Number of bins "
"found: {}\n".format(num_reads_per_bin.shape[0]))
if num_reads_per_bin.shape[0] < 2:
exit("ERROR: too few non zero bins found.\n"
"If using --region please check that this "
"region is covered by reads.\n")
np.savez_compressed(args.outFileName,
matrix=num_reads_per_bin,
labels=args.labels)
if args.outRawCounts:
# append to the generated file the
# labels
header = "#'chr'\t'start'\t'end'\t"
header += "'" + "'\t'".join(args.labels) + "'\n"
# import ipdb;ipdb.set_trace()
with open(args.outRawCounts.name, 'r+') as f:
content = f.read()
f.seek(0, 0)
f.write(header + content)
"""
def main(args=None):
"""
1. get read counts at different positions either
all of same length or from genomic regions from the BED file
2. compute the scores
"""
args = process_args(args)
if 'BED' in args:
bed_regions = args.BED
else:
bed_regions = None
if len(args.bwfiles) == 1 and not args.outRawCounts:
sys.stderr.write("You've input a single bigWig file and not specified "
"--outRawCounts. The resulting output will NOT be "
"useful with any deepTools program!\n")
num_reads_per_bin = score_bw.getScorePerBin(
args.bwfiles,
args.binSize,
blackListFileName=args.blackListFileName,
numberOfProcessors=args.numberOfProcessors,
stepSize=args.binSize + args.distanceBetweenBins,
verbose=args.verbose,
region=args.region,
bedFile=bed_regions,
chrsToSkip=args.chromosomesToSkip,
out_file_for_raw_data=args.outRawCounts)
sys.stderr.write("Number of bins "
"found: {}\n".format(num_reads_per_bin.shape[0]))
if num_reads_per_bin.shape[0] < 2:
exit("ERROR: too few non zero bins found.\n"
"If using --region please check that this "
"region is covered by reads.\n")
np.savez_compressed(args.outFileName,
matrix=num_reads_per_bin,
labels=args.labels)
if args.outRawCounts:
# append to the generated file the
# labels
header = "#'chr'\t'start'\t'end'\t"
header += "'" + "'\t'".join(args.labels) + "'\n"
# import ipdb;ipdb.set_trace()
with open(args.outRawCounts.name, 'r+') as f:
content = f.read()
f.seek(0, 0)
f.write(header + content)
"""
def main(args=None):
"""
1. get read counts at different positions either
all of same length or from genomic regions from the BED file
2. compute the scores
"""
args = process_args(args)
if 'BED' in args:
bed_regions = args.BED
else:
bed_regions = None
if len(args.bwfiles) == 1 and not args.outRawCounts:
sys.stderr.write("You've input a single bigWig file and not specified "
"--outRawCounts. The resulting output will NOT be "
"useful with any deepTools program!\n")
# Preload deepBlue files, which need to then be deleted
deepBlueFiles = []
for idx, fname in enumerate(args.bwfiles):
if db.isDeepBlue(fname):
deepBlueFiles.append([fname, idx])
if len(deepBlueFiles) > 0:
sys.stderr.write(
"Preloading the following deepBlue files: {}\n".format(",".join(
[x[0] for x in deepBlueFiles])))
if 'BED' in args:
regs = db.makeRegions(args.BED, args)
else:
foo = db.deepBlue(deepBlueFiles[0][0],
url=args.deepBlueURL,
userKey=args.userKey)
regs = db.makeTiles(foo, args)
del foo
for x in deepBlueFiles:
x.extend([args, regs])
if len(deepBlueFiles) > 1 and args.numberOfProcessors > 1:
pool = multiprocessing.Pool(args.numberOfProcessors)
res = pool.map_async(db.preloadWrapper, deepBlueFiles).get(9999999)
else:
res = list(map(db.preloadWrapper, deepBlueFiles))
# substitute the file names with the temp files
for (ftuple, r) in zip(deepBlueFiles, res):
args.bwfiles[ftuple[1]] = r
deepBlueFiles = [[x[0], x[1]] for x in deepBlueFiles]
del regs
num_reads_per_bin = score_bw.getScorePerBin(
args.bwfiles,
args.binSize,
blackListFileName=args.blackListFileName,
numberOfProcessors=args.numberOfProcessors,
stepSize=args.binSize + args.distanceBetweenBins,
verbose=args.verbose,
region=args.region,
bedFile=bed_regions,
chrsToSkip=args.chromosomesToSkip,
out_file_for_raw_data=args.outRawCounts,
allArgs=args)
sys.stderr.write("Number of bins "
"found: {}\n".format(num_reads_per_bin.shape[0]))
if num_reads_per_bin.shape[0] < 2:
exit("ERROR: too few non zero bins found.\n"
"If using --region please check that this "
"region is covered by reads.\n")
f = open(args.outFileName, "wb")
np.savez_compressed(f, matrix=num_reads_per_bin, labels=args.labels)
f.close()
if args.outRawCounts:
# append to the generated file the
# labels
header = "#'chr'\t'start'\t'end'\t"
header += "'" + "'\t'".join(args.labels) + "'\n"
f = open(args.outRawCounts, "r+")
content = f.read()
f.seek(0, 0)
f.write(header + content)
"""
if bed_regions:
bed_regions.seek(0)
reg_list = bed_regions.readlines()
args.outRawCounts.write("#'chr'\t'start'\t'end'\t")
args.outRawCounts.write("'" + "'\t'".join(args.labels) + "'\n")
fmt = "\t".join(np.repeat('%s', num_reads_per_bin.shape[1])) + "\n"
for idx, row in enumerate(num_reads_per_bin):
args.outRawCounts.write("{}\t{}\t{}\t".format(*reg_list[idx].strip().split("\t")[0:3]))
args.outRawCounts.write(fmt % tuple(row))
else:
args.outRawCounts.write("'" + "'\t'".join(args.labels) + "'\n")
fmt = "\t".join(np.repeat('{}', num_reads_per_bin.shape[1])) + "\n"
for row in num_reads_per_bin:
args.outRawCounts.write(fmt.format(*tuple(row)))
"""
f.close()
# Clean up temporary bigWig files, if applicable
if not args.deepBlueKeepTemp:
for k, v in deepBlueFiles:
os.remove(args.bwfiles[v])
else:
for k, v in deepBlueFiles:
print("{} is stored in {}".format(k, args.bwfiles[v]))
def get_labels_and_correlation(
bw_files,
# chrs_to_skip,
bin_size=10000,
method='pearson',
fileset_name='result',
blacklist=None,
labels=bw_labels,
output_dir=BASE_DIR):
my_listUnnested = []
# my_labels_list = []
assert method in ('pearson', 'spearman'), 'Invalid correlation method'
# Autogenerate labels from filename if not provided
if not labels:
labels = [
filename.split('/')[-1].split('.')[0] for filename in bw_files
]
# Generate a name for the unique combination
test_name = fileset_name + '_' + method
if blacklist:
blacklist_title = 'Blacklisted'
test_name += '_blacklisted'
else:
blacklist_title = ''
image_name = test_name + '.png'
# Bin the bigwig data in 10kb increments
num_reads_per_bin = score_bw.getScorePerBin(
bw_files,
bin_size,
# chrsToSkip=chrs_to_skip,
blackListFileName=blacklist)
# Write to npz file
print("right before npz")
os.system('pwd')
os.system('ls -lat')
print('svo')
print(output_dir)
print(test_name)
filename = output_dir + test_name + '.npz'
print(filename)
print('svo.. ')
with open(filename, "wb") as f:
np.savez_compressed(f, matrix=num_reads_per_bin, labels=labels)
# Compute the correlations
corr = Correlation(filename, method, labels=labels)
np_array = corr.compute_correlation()
print("ALERT CORR")
print(np_array)
listNested = np_array.tolist()
def removeNestings(listNest):
for i in listNest:
if type(i) == list:
print(str(i) + "is: " + str(type(i)))
removeNestings(i)
print(str(i) + "is after: " + str(type(i)))
else:
print(str(i) + "is finally " + str(type(i)))
my_listUnnested.append(i)
removeNestings(listNested)
print("FINAL CORRELATION VALUES")
print(my_listUnnested)
with open("corrScores.txt", "w") as f:
f.write(str(my_listUnnested))
plot_title = '{}{} Correlation of {}'.format(blacklist_title,
method.capitalize(),
fileset_name)
# Create a png file of correlation heatmap
image_path = output_dir + image_name
corr.plot_correlation(image_path, plot_title=plot_title)
# return np_ar
my_labels_list = labels
return image_path, my_labels_list, my_listUnnested
def main(args=None):
"""
1. get read counts at different positions either
all of same length or from genomic regions from the BED file
2. compute the scores
"""
args = process_args(args)
if 'BED' in args:
bed_regions = args.BED
else:
bed_regions = None
if len(args.bwfiles) == 1 and not args.outRawCounts:
sys.stderr.write("You've input a single bigWig file and not specified "
"--outRawCounts. The resulting output will NOT be "
"useful with any deepTools program!\n")
num_reads_per_bin = score_bw.getScorePerBin(
args.bwfiles,
args.binSize,
blackListFileName=args.blackListFileName,
numberOfProcessors=args.numberOfProcessors,
stepSize=args.binSize + args.distanceBetweenBins,
verbose=args.verbose,
region=args.region,
bedFile=bed_regions,
chrsToSkip=args.chromosomesToSkip,
out_file_for_raw_data=args.outRawCounts,
allArgs=args)
sys.stderr.write("Number of bins "
"found: {}\n".format(num_reads_per_bin.shape[0]))
if num_reads_per_bin.shape[0] < 2:
exit("ERROR: too few non zero bins found.\n"
"If using --region please check that this "
"region is covered by reads.\n")
f = open(args.outFileName, "wb")
np.savez_compressed(f, matrix=num_reads_per_bin, labels=args.labels)
f.close()
if args.outRawCounts:
# append to the generated file the
# labels
header = "#'chr'\t'start'\t'end'\t"
header += "'" + "'\t'".join(args.labels) + "'\n"
f = open(args.outRawCounts, "r+")
content = f.read()
f.seek(0, 0)
f.write(header + content)
"""
if bed_regions:
bed_regions.seek(0)
reg_list = bed_regions.readlines()
args.outRawCounts.write("#'chr'\t'start'\t'end'\t")
args.outRawCounts.write("'" + "'\t'".join(args.labels) + "'\n")
fmt = "\t".join(np.repeat('%s', num_reads_per_bin.shape[1])) + "\n"
for idx, row in enumerate(num_reads_per_bin):
args.outRawCounts.write("{}\t{}\t{}\t".format(*reg_list[idx].strip().split("\t")[0:3]))
args.outRawCounts.write(fmt % tuple(row))
else:
args.outRawCounts.write("'" + "'\t'".join(args.labels) + "'\n")
fmt = "\t".join(np.repeat('{}', num_reads_per_bin.shape[1])) + "\n"
for row in num_reads_per_bin:
args.outRawCounts.write(fmt.format(*tuple(row)))
"""
f.close()
def main(args=None):
"""
1. get read counts at different positions either
all of same length or from genomic regions from the BED file
2. compute the scores
"""
args = process_args(args)
if 'BED' in args:
bed_regions = args.BED
else:
bed_regions = None
if len(args.bwfiles) == 1 and not args.outRawCounts:
sys.stderr.write("You've input a single bigWig file and not specified "
"--outRawCounts. The resulting output will NOT be "
"useful with any deepTools program!\n")
# Preload deepBlue files, which need to then be deleted
deepBlueFiles = []
for idx, fname in enumerate(args.bwfiles):
if db.isDeepBlue(fname):
deepBlueFiles.append([fname, idx])
if len(deepBlueFiles) > 0:
sys.stderr.write("Preloading the following deepBlue files: {}\n".format(",".join([x[0] for x in deepBlueFiles])))
if 'BED' in args:
regs = db.makeRegions(args.BED, args)
else:
foo = db.deepBlue(deepBlueFiles[0][0], url=args.deepBlueURL, userKey=args.userKey)
regs = db.makeTiles(foo, args)
del foo
for x in deepBlueFiles:
x.extend([args, regs])
if len(deepBlueFiles) > 1 and args.numberOfProcessors > 1:
pool = multiprocessing.Pool(args.numberOfProcessors)
res = pool.map_async(db.preloadWrapper, deepBlueFiles).get(9999999)
else:
res = list(map(db.preloadWrapper, deepBlueFiles))
# substitute the file names with the temp files
for (ftuple, r) in zip(deepBlueFiles, res):
args.bwfiles[ftuple[1]] = r
deepBlueFiles = [[x[0], x[1]] for x in deepBlueFiles]
del regs
num_reads_per_bin = score_bw.getScorePerBin(
args.bwfiles,
args.binSize,
blackListFileName=args.blackListFileName,
numberOfProcessors=args.numberOfProcessors,
stepSize=args.binSize + args.distanceBetweenBins,
verbose=args.verbose,
region=args.region,
bedFile=bed_regions,
chrsToSkip=args.chromosomesToSkip,
out_file_for_raw_data=args.outRawCounts,
allArgs=args)
sys.stderr.write("Number of bins "
"found: {}\n".format(num_reads_per_bin.shape[0]))
if num_reads_per_bin.shape[0] < 2:
exit("ERROR: too few non zero bins found.\n"
"If using --region please check that this "
"region is covered by reads.\n")
f = open(args.outFileName, "wb")
np.savez_compressed(f,
matrix=num_reads_per_bin,
labels=args.labels)
f.close()
if args.outRawCounts:
# append to the generated file the
# labels
header = "#'chr'\t'start'\t'end'\t"
header += "'" + "'\t'".join(args.labels) + "'\n"
f = open(args.outRawCounts, "r+")
content = f.read()
f.seek(0, 0)
f.write(header + content)
"""
if bed_regions:
bed_regions.seek(0)
reg_list = bed_regions.readlines()
args.outRawCounts.write("#'chr'\t'start'\t'end'\t")
args.outRawCounts.write("'" + "'\t'".join(args.labels) + "'\n")
fmt = "\t".join(np.repeat('%s', num_reads_per_bin.shape[1])) + "\n"
for idx, row in enumerate(num_reads_per_bin):
args.outRawCounts.write("{}\t{}\t{}\t".format(*reg_list[idx].strip().split("\t")[0:3]))
args.outRawCounts.write(fmt % tuple(row))
else:
args.outRawCounts.write("'" + "'\t'".join(args.labels) + "'\n")
fmt = "\t".join(np.repeat('{}', num_reads_per_bin.shape[1])) + "\n"
for row in num_reads_per_bin:
args.outRawCounts.write(fmt.format(*tuple(row)))
"""
f.close()
# Clean up temporary bigWig files, if applicable
if not args.deepBlueKeepTemp:
for k, v in deepBlueFiles:
os.remove(args.bwfiles[v])
else:
for k, v in deepBlueFiles:
print("{} is stored in {}".format(k, args.bwfiles[v]))
def main(args=None):
"""
1. get read counts at different positions either
all of same length or from genomic regions from the BED file
2. compute correlation
"""
args = process_args(args)
if len(args.bwfiles) < 2:
print "Please input at least two bigWig (.bw) files to compare"
exit(1)
if 'BED' in args:
bed_regions = args.BED
else:
bed_regions = None
bwFiles = []
for fname in args.bwfiles:
f = fname.name
fname.close()
if f:
bwFiles.append(f)
if len(bwFiles) == 0:
print "No valid bigwig files"
exit(1)
num_reads_per_bin = score_bw.getScorePerBin(
bwFiles,
args.binSize,
numberOfProcessors=args.numberOfProcessors,
stepSize=args.binSize + args.distanceBetweenBins,
verbose=args.verbose,
region=args.region,
bedFile=bed_regions,
chrsToSkip=args.chromosomesToSkip,
out_file_for_raw_data=args.outRawCounts)
sys.stderr.write("Number of bins "
"found: {}\n".format(num_reads_per_bin.shape[0]))
if num_reads_per_bin.shape[0] < 2:
exit("ERROR: too few non zero bins found.\n"
"If using --region please check that this "
"region is covered by reads.\n")
np.savez_compressed(args.outFileName,
matrix=num_reads_per_bin,
labels=args.labels)
if args.outRawCounts:
# append to the generated file the
# labels
header = "#'chr'\t'start'\t'end'\t"
header += "'" + "'\t'".join(args.labels) + "'\n"
# import ipdb;ipdb.set_trace()
with open(args.outRawCounts.name, 'r+') as f:
content = f.read()
f.seek(0, 0)
f.write(header + content)
"""
def get_labels_and_correlation(
bw_files,
# chrs_to_skip,
bin_size=10000,
method='pearson',
fileset_name='result',
blacklist=None,
labels=bw_labels,
output_dir='/Users/baditya02/Downloads/treatment-data/graphs/test/'
):
my_listUnnested = []
my_labels_list = []
assert method in ('pearson', 'spearman'), 'Invalid correlation method'
# Autogenerate labels from filename if not provided
if not labels:
labels = [filename.split( '/' )[-1].split( '.' )[0] for filename in bw_files]
# Generate a name for the unique combination
test_name = fileset_name + '_' + method
if blacklist:
blacklist_title = 'Blacklisted '
test_name += '_blacklisted'
else:
blacklist_title = ''
image_name = test_name + '.png'
# Bin the bigwig data in 10kb increments
num_reads_per_bin = score_bw.getScorePerBin(
bw_files,
bin_size,
# chrsToSkip=chrs_to_skip,
blackListFileName=blacklist
)
# Write to npz file
filename = output_dir + test_name + '.npz'
with open(filename, "wb") as f:
np.savez_compressed(f, matrix=num_reads_per_bin, labels=labels)
# Compute the correlations
corr = Correlation(filename, method, labels=labels)
np_array = corr.compute_correlation()
listNested = np_array.tolist()
def removeNestings(listNest):
for i in listNest:
if type(i) == list:
removeNestings(i)
else:
my_listUnnested.append(i)
removeNestings(listNested)
plot_title = '{}{} Correlation of {}'.format(
blacklist_title,
method.capitalize(),
fileset_name
)
# Create a png file of correlation heatmap
image_path = output_dir + image_name
corr.plot_correlation( image_path, plot_title=plot_title )
# return np_ar
my_labels_list = labels
return image_path, my_labels_list, my_listUnnested
