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1 # -*- coding: utf-8 -*-
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2 """
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3 Created on Sat Mar 24 17:18:09 2018
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4
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5 @author: chmaramis
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6 """
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7
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8 from __future__ import division
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9 import numpy as np
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10 from pandas import *
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11 from numpy import nan as NA
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12 import sys
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13 import time
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14
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15 clono_def = {'CDR3': ['AA JUNCTION'],
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16 'VCDR3': ['V-GENE','AA JUNCTION'],
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17 'JCDR3': ['J-GENE','AA JUNCTION']}
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18
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19
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20
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21 def publicClonotypeComputation(inputs, clono, thres):
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22
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23 clono_comps = clono_def[clono]
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24
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25 clono=DataFrame()
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26
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27 for x in range(0,len(inputs),2):
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28 cl = DataFrame()
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29 cl = read_csv(inputs[x] , sep = '\t' , index_col = 0)
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30 #tp = read_csv(inp_name, iterator=True, chunksize=5000,sep='\t', index_col=0 )
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31 #cl = concat([chunk for chunk in tp])
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32
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33 if (thres != 'null'):
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34 cl = cl[cl['Reads'] > int(thres)]
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35
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36 x1 = inputs[x+1].split('_')
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37
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38 del cl['Reads']
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39 cl.columns = [cl.columns[0], cl.columns[1], x1[0]+' '+cl.columns[2], x1[0]+' Relative '+cl.columns[3]]
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40
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41 if clono.empty:
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42 clono = cl
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43 else:
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44 clono = clono.merge(cl, how='outer', on=clono_comps)
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45
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46
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47 col = clono.columns
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48 freqs = col.map(lambda x: 'Frequency' in x)
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49 reads = col.map(lambda x: 'Reads/Total' in x)
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50
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51 clono[col[freqs]] = clono[col[freqs]].fillna(0)
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52 clono[col[reads]] = clono[col[reads]].fillna('0/*')
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53
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54 clono['Num of Patients']= clono[col[freqs]].apply(lambda x: np.sum(x != 0), axis=1)
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55
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56 clono = clono[clono['Num of Patients'] > 1]
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57
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58 clono.index = range(1,len(clono)+1)
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59
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60 return clono
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61
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62
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63 if __name__ == '__main__':
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64
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65 start=time.time()
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66
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67 # Parse input arguments
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68 arg = sys.argv[4:]
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69 clono = sys.argv[1]
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70 output = sys.argv[2]
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71 thres = sys.argv[3]
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72
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73
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74
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75 # Execute basic function
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76 mer = publicClonotypeComputation(arg, clono, thres)
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77
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78 # Save output to CSV files
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79 if not mer.empty:
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80 mer.to_csv(output , sep = '\t')
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81
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82 # Print execution time
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83 stop=time.time()
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84 print('Runtime:' + str(stop-start))
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