#!/usr/bin/env python3 import pandas as pd DEBUG = False #%% #def find_missense(test_df, ref_allele1, alt_allele0): def find_missense(test_df, ref_allele_column, alt_allele_column, n_diff_colname = 'n_diff', tot_diff_colname = 'tot_diff', ref_a_colname = 'ref_allele', alt_a_colname = 'alt_allele'): """Find mismatches in pairwise comparison of strings b/w col_a and col_b Case insensitive, converts strings to uppercase before comparison @test_df: df containing columns to compare @type: pandas df @ref_allele_column: column containing ref allele str @type: str (converts to uppercase) @alt_allele_column: column containing alt_allele str @type: str (converts to uppercase) @n_diff_colname: user defined colname for no. of char diff b/w ref_allele_str and alt_allele_str @type: str @tot_diff_colname: user defined colname abs diff to indicate if strings are of equal length @type: str @ref_a_colname: user defined colname containing extracted referece allele @type: str @alt_a_colname: user defined colname containing extracted alt allele @type: str returns df: with 4 added columns. If column names clash, the function column name will override original column @rtype: pandas df """ for ind, val in test_df.iterrows(): if DEBUG: print('index:', ind, 'value:', val , '\n============================================================') ref_a = val[ref_allele_column].upper() alt_a = val[alt_allele_column].upper() if DEBUG: print('ref_allele_string:', ref_a, 'alt_allele_string:', alt_a) difference = sum(1 for e in zip(ref_a, alt_a) if e[0] != e[1]) test_df.at[ind, n_diff_colname] = difference # adding column tot_difference = difference + abs(len(ref_a) - len(alt_a)) test_df.at[ind, tot_diff_colname] = tot_difference # adding column if difference != tot_difference: print('WARNING: lengths of ref_allele and alt_allele differ at index:', ind , '\nNon-missense muts detected') # Now finding the mismatched char ref_aln = '' alt_aln = '' if ref_a == alt_a: ##test_df.at[ind, 'ref_allele'] = 'no_change' # adding column ##test_df.at[ind, 'alt_allele'] = 'no_change' # adding column test_df.at[ind, ref_a_colname] = 'no_change' # adding column test_df.at[ind, alt_a_colname] = 'no_change' # adding column elif len(ref_a) == len(alt_a) and len(ref_a) > 0: print('ref:', ref_a, 'alt:', alt_a) for n in range(len(ref_a)): if ref_a[n] != alt_a[n]: ref_aln += ref_a[n] alt_aln += alt_a[n] ##test_df.at[ind, 'ref_allele'] = ref_aln ##test_df.at[ind, 'alt_allele'] = alt_aln test_df.at[ind, ref_a_colname] = ref_aln test_df.at[ind, alt_a_colname] = alt_aln print('ref:', ref_aln) print('alt:', alt_aln) else: ##test_df.at[ind, 'ref_allele'] = 'ERROR_Not_nsSNP' ##test_df.at[ind, 'alt_allele'] = 'ERROR_Not_nsSNP' test_df.at[ind, ref_a_colname] = 'ERROR_Not_nsSNP' test_df.at[ind, alt_a_colname] = 'ERROR_Not_nsSNP' return test_df #======================================== # a representative example #eg_df = pd.read_csv('pnca_assoc.txt', sep = '\t', nrows = 10, header = 0, index_col = False) #eg_df = pd.DataFrame(eg_df) #eg_df = {'chromosome_number': [2288719, 2288766, 2288775, 2288779, 2288827, 1111111, 2222222], # 'ref_allele1': ['Tc', 'AG', 'AGCACCCTG', 'CCCTGGTGGCC', 'CACA', 'AA', 'CAT'], # 'alt_allele0': ['CC', 'CA', 'GGCACCCTGZ','TCCTGGTGGCCAAD', 'TACA', 'AA', 'TCZ']} #def main(): #find_missense(eg_df, ref_allele1 = 'ref_allele', alt_allele0 = 'alt_allele') # find_missense(test_df = eg_df, ref_allele_column = 'ref_allele1', alt_allele_column = 'alt_allele0') # print(eg_df) #if __name__ == '__main__': # main()