Delete code/add_domains_alphafold.py

code/add_domains_alphafold.py

@@ -1,57 +0,0 @@
-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