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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 | |