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from collections import Counter
import pandas as pd
def add_domains(data, path_to_domains):
domains = pd.read_csv(path_to_domains, delimiter=' ')
data = data.merge(domains, right_on='proteinID', left_on='uniprotID', how='left')
data = data.drop(['proteinID'], axis=1)
# Label each data point as range or notRange based on the relative distance of mutation and domain boundaries.
data = data.astype('str')
data.domStart = data.domStart.astype('float')
data.domEnd = data.domEnd.astype('float')
for i in data.index:
if data.at[i, 'domain'] != 'nan':
if int(data.at[i, 'domStart']) <= int(data.at[i, 'pos']) <= int(data.at[i, 'domEnd']):
data.at[i, 'distance'] = 0
else:
distance = min(abs(int(data.at[i, 'domStart']) - int(data.at[i, 'pos'])),
abs(int(data.at[i, 'domEnd']) - int(data.at[i, 'pos'])))
data.at[i, 'distance'] = int(distance)
else:
data.at[i, 'distance'] = 'nan'
data = data.sort_values(by=['datapoint', 'distance']).reset_index(drop=True) # Distances will be sorted.
# Keep the one with the least distance. But we may have more than one range domains for a datapoint if distance = 0.
# For this reason first we need to separate range ones so that when we take the first occurance to get the closest one
# for non range ones, other distance=0 ones wont disappear.
data_range = data[data.distance == 0]
data_out_range = data[data.distance != 0]
# For the range ones, find the most occurance
dom = []
for i in data_range.index:
dom.append(data_range.at[i, 'domain'])
domainCount = Counter(dom) # Occurance of domains.
# For out of range ones, take the closest distance.
data_out_range = data_out_range.drop_duplicates(['datapoint'], keep='first') # Already sorted above.
domain_counts = pd.DataFrame(domainCount.items(), columns=['domain', 'count'])
data_range_counts = data_range.merge(domain_counts, on='domain')
data_range_counts = data_range_counts.sort_values(['datapoint', 'count'])
data_range_counts = data_range_counts.drop_duplicates(['datapoint'], keep='last') # Take with the higher count.
data_range_counts = data_range_counts.drop(['count'], axis=1)
# Merge them back together
frames = [data_range_counts, data_out_range]
data = pd.concat(frames, sort=False) # Here when you concat two data frames, we might have range and not range with
# min distance for the same data point. Delete the one coming from notRange one.
data = data.sort_values(['datapoint', 'distance']).reset_index(drop=True)
data = data.drop_duplicates(['datapoint'], keep='first')
data = data.astype(str)
return data
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