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 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
