#!/usr/bin/env python3 # -*- coding: utf-8 -*- ''' @author: tanu ''' #%% Description # FIXME: include error checking to enure you only # concentrate on positions that have structural info? # FIXME: import dirs.py to get the basic dir paths available #======================================================================= # TASK: extract ALL matched mutations from GWAS data # Input data file has the following format: each row = unique sample id # id,country,lineage,sublineage,drtype,drug,dr_muts_col,other_muts_col... # 0,sampleID,USA,lineage2,lineage2.2.1,Drug-resistant,0.0,WT,gene_matchPOS; pncA_c.POS... # where multiple mutations and multiple mutation types are separated by ';'. # We are interested in the protein coding region i.e mutation with the_'p.' format. # This script splits the mutations on the ';' and extracts protein coding muts only # where each row is a separate mutation # sample ids AND mutations are NOT unique, but the COMBINATION (sample id + mutation) = unique # output files: all lower case # 1) _gwas.csv # 2) _common_ids.csv # 3) _ambiguous_muts.csv # 4) _mcsm_formatted_snps.csv # 5) _metadata_poscounts.csv # 6) _metadata.csv # 7) _all_muts_msa.csv # 8) _mutational_positons.csv #------------ # NOTE #----------- # drtype is renamed to 'resistance' in the 35k dataset # all colnames in the ouput files lowercase #------------- # requires #------------- #reference_dict.py #tidy_split.py # bash counting for cross checks: example #grep -oP 'pncA_p.[A-Za-z]{3}[0-9]+[A-Za-z]{3}' mtb_gwas_meta_v6.csv | sort | uniq -c | wc -l #======================================================================= #%% load libraries import os, sys import re import pandas as pd import numpy as np import argparse from statistics import mean, median, mode from statistics import multimode # adding values for common keys import itertools import collections #======================================================================= #%% dir and local imports homedir = os.path.expanduser('~') # set working dir os.getcwd() os.chdir(homedir + '/git/LSHTM_analysis/scripts') os.getcwd() #======================================================================= #%% command line args arg_parser = argparse.ArgumentParser() arg_parser.add_argument('-d', '--drug', help='drug name (case sensitive)', default = None) arg_parser.add_argument('-g', '--gene', help='gene name (case sensitive)', default = None) arg_parser.add_argument('--datadir', help = 'Data Directory. By default, it assmumes homedir + git/Data') arg_parser.add_argument('-i', '--input_dir', help = 'Input dir containing pdb files. By default, it assmumes homedir + + input') arg_parser.add_argument('-o', '--output_dir', help = 'Output dir for results. By default, it assmes homedir + + output') arg_parser.add_argument('-m', '--make_dirs', help = 'Make dir for input and output', action='store_true') arg_parser.add_argument('--debug', action ='store_true', help = 'Debug Mode') args = arg_parser.parse_args() ############################################################################### #%% variable assignment: input and output paths & filenames drug = args.drug gene = args.gene datadir = args.datadir indir = args.input_dir outdir = args.output_dir make_dirs = args.make_dirs ############################################################################### #%% variable assignment: input and output dirs and files #======= # dirs #======= if not datadir: datadir = homedir + '/' + 'git/Data' if not indir: indir = datadir + '/' + drug + '/input_v2' if not outdir: outdir = datadir + '/' + drug + '/output_v2' if make_dirs: print('make_dirs is turned on, creating data dir:', datadir) try: os.makedirs(datadir, exist_ok = True) print("Directory '%s' created successfully" %datadir) except OSError as error: print("Directory '%s' can not be created") print('make_dirs is turned on, creating indir:', indir) try: os.makedirs(indir, exist_ok = True) print("Directory '%s' created successfully" %indir) except OSError as error: print("Directory '%s' can not be created") print('make_dirs is turned on, creating outdir:', outdir) try: os.makedirs(outdir, exist_ok = True) print("Directory '%s' created successfully" %outdir) except OSError as error: print("Directory '%s' can not be created") # handle missing dirs here if not os.path.isdir(datadir): print('ERROR: Data directory does not exist:', datadir , '\nPlease create and ensure gwas data is present and then rerun\nelse specify cmd option --make_dirs') sys.exit() if not os.path.isdir(indir): print('ERROR: Input directory does not exist:', indir , '\nPlease either create or specify indir and rerun\nelse specify cmd option --make_dirs') sys.exit() if not os.path.isdir(outdir): print('ERROR: Output directory does not exist:', outdir , '\nPlease create or specify outdir and rerun\nelse specify cmd option --make_dirs') sys.exit() ############################################################################### #%% required local imports from reference_dict import my_aa_dict # CHECK DIR STRUC THERE! from tidy_split import tidy_split ############################################################################### #%% creating required variables: gene and drug dependent, and input filename gene_match = gene + '_p.' print('mut pattern for gene', gene, ':', gene_match) nssnp_match = gene_match +'[A-Za-z]{3}[0-9]+[A-Za-z]{3}' print('nsSNP for gene', gene, ':', nssnp_match) nssnp_match_captureG = "(" + nssnp_match + ")" wt_regex = gene_match.lower()+'([A-Za-z]{3})' print('wt regex:', wt_regex) mut_regex = r'[0-9]+(\w{3})$' print('mt regex:', mut_regex) pos_regex = r'([0-9]+)' print('position regex:', pos_regex) # building cols to extract dr_muts_col = 'dr_mutations_' + drug other_muts_col = 'other_mutations_' + drug resistance_col = 'drtype' print('Extracting columns based on variables:\n' , drug , '\n' , dr_muts_col , '\n' , other_muts_col , '\n' , resistance_col , '\n===============================================================') #======= # input #======= #in_filename_master_master = 'original_tanushree_data_v2.csv' #19k in_filename_master = 'mtb_gwas_meta_v6.csv' #35k infile_master = datadir + '/' + in_filename_master print('Input file: ', infile_master , '\n============================================================') #======= # output #======= # several output files: in respective sections at the time of outputting files print('Output filename: in the respective sections' , '\nOutput path: ', outdir , '\n=============================================================') #end of variable assignment for input and output files ############################################################################### #%% Read input file master_data = pd.read_csv(infile_master, sep = ',') # column names #list(master_data.columns) # extract elevant columns to extract from meta data related to the drug if in_filename_master == 'original_tanushree_data_v2.csv': meta_data = master_data[['id' , 'country' , 'lineage' , 'sublineage' , 'drtype' , drug , dr_muts_col , other_muts_col]] else: core_cols = ['id' , 'sample' #, 'patient_id' #, 'strain' , 'lineage' , 'sublineage' , 'country_code' #, 'geographic_source' , resistance_col] variable_based_cols = [drug , dr_muts_col , other_muts_col] cols_to_extract = core_cols + variable_based_cols print('Extracting', len(cols_to_extract), 'columns from master data') meta_data = master_data[cols_to_extract] del(master_data, variable_based_cols, cols_to_extract) print('Extracted meta data from filename:', in_filename_master , '\nDim:', meta_data.shape) # checks and results total_samples = meta_data['id'].nunique() print('RESULT: Total samples:', total_samples , '\n===========================================================') # counts NA per column meta_data.isna().sum() print('No. of NAs/column:' + '\n', meta_data.isna().sum() , '\n===========================================================\n') ############################################################################### #%% Quick checks: meta_data['lineage'].value_counts() meta_data['lineage'].value_counts().sum() meta_data['lineage'].nunique() # replace lineage with 'L' in lineage_labels #meta_data['lineage_labels'] = meta_data['lineage'] #meta_data['lineage_labels'].equals(meta_data['lineage']) #all(meta_data['lineage_labels'].value_counts() == meta_data['lineage'].value_counts()) #meta_data['lineage_labels'] = meta_data['lineage_labels'].str.replace('lineage', 'L') #meta_data['lineage'].value_counts() #meta_data['lineage_labels'].value_counts() meta_data['lineage'] = meta_data['lineage'].str.replace('lineage', 'L') meta_data['lineage'].value_counts() print("\n================================" , "\nLineage numbers" , "\nComplete lineage samples:", meta_data['lineage'].value_counts().sum() , "\nMissing lineage samples:", meta_data['id'].nunique() - meta_data['lineage'].value_counts().sum() , "\n================================") meta_data['id'].nunique() meta_data['sample'].nunique() meta_data['id'].equals(meta_data['sample']) foo = meta_data.copy() foo['Diff'] = np.where( foo['id'] == foo['sample'] , '1', '0') foo['Diff'].value_counts() meta_data['drtype'].value_counts() meta_data['drtype'].value_counts().sum() print("\n================================" , "\ndrtype numbers" , "\nComplete drtype samples:", meta_data['drtype'].value_counts().sum() , "\nMissing drtype samples:", meta_data['id'].nunique() - meta_data['drtype'].value_counts().sum() , "\n================================") meta_data['drug_name'] = meta_data[drug].map({1:'R' , 0:'S'}) meta_data['drug_name'].value_counts() meta_data[drug].value_counts() meta_data[drug].value_counts().sum() print("\n================================" , "\ndrug", drug, "numbers" , "\nComplete drug samples:", meta_data[drug].value_counts().sum() , "\nMissing drug samples:", meta_data['id'].nunique() - meta_data[drug].value_counts().sum() , "\n================================") print("\n================================" , "\ndrug", drug, "numbers" , "\nComplete drug samples:", meta_data['drug_name'].value_counts().sum() , "\nMissing drug samples:", meta_data['id'].nunique() - meta_data['drug_name'].value_counts().sum() , "\n================================") #%% Quick counts: All samples, drug: print('===========================================================\n' , 'RESULT: Total no. of samples tested for', drug, ':', meta_data[drug].value_counts().sum() , '\n===========================================================\n' , 'RESULT: No. of Sus and Res', drug, 'samples:\n', meta_data[drug].value_counts() , '\n===========================================================\n' , 'RESULT: Percentage of Sus and Res', drug, 'samples:\n', meta_data[drug].value_counts(normalize = True)*100 , '\n===========================================================') print('===========================================================\n' , 'RESULT: Total no. of samples tested for', drug, ':', meta_data['drug_name'].value_counts().sum() , '\n===========================================================\n' , 'RESULT: No. of Sus and Res', drug, 'samples:\n', meta_data['drug_name'].value_counts() , '\n===========================================================\n' , 'RESULT: Percentage of Sus and Res', drug, 'samples:\n', meta_data['drug_name'].value_counts(normalize = True)*100 , '\n===========================================================') ############################################################################## #%% Extracting nsSNP for gene (meta_gene_all): from dr_muts col and other_muts_col #meta_data_gene = meta_data.loc[ (meta_data[dr_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False) ) | (meta_data[other_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False) ) ] # so downstream code doesn't change meta_gene_all = meta_data.loc[ (meta_data[dr_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False) ) | (meta_data[other_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False) ) ] #%% DF: with dr_muts_col meta_gene_dr = meta_gene_all.loc[meta_gene_all[dr_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False)] meta_gene_dr1 = meta_data.loc[meta_data[dr_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False)] if meta_gene_dr.equals(meta_gene_dr1): print('\nPASS: DF with column:', dr_muts_col, 'extracted successfully' , '\ngene_snp_match in column:',dr_muts_col, meta_gene_dr.shape) else: sys.exit('\nFAIL: DF with column:', dr_muts_col,'could not be extracted' , '\nshape of df1:', meta_gene_dr.shape , '\nshape of df2:', meta_gene_dr1.shape , '\nCheck again!') ############################################################################## #%% DF: with other_muts_col meta_gene_other = meta_gene_all.loc[meta_gene_all[other_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False)] meta_gene_other1 = meta_data.loc[meta_data[other_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False)] print('gene_snp_match in dr:', len(meta_gene_other)) meta_gene_other.equals(meta_gene_other1) if meta_gene_other.equals(meta_gene_other1): print('\nPASS: DF with column:', other_muts_col,'extracted successfully' , '\ngene_snp_match in column:',other_muts_col, meta_gene_other.shape) else: sys.exit('\nFAIL: DF with column:', other_muts_col,'could not be extracted' , '\nshape of df1:', meta_gene_other.shape , '\nshape of df2:', meta_gene_other1.shape , '\nCheck again!') ############################################################################## #%% Quick counts: nsSNP samples, drug meta_gene_all[drug].value_counts() print('===========================================================\n' , 'RESULT: Total no. of samples for', drug, 'with nsSNP mutations:', meta_gene_all['id'].nunique() , '\n===========================================================\n' , '===========================================================\n' , 'RESULT: Total no. of samples tested for', drug, 'with nsSNP mutations:', meta_gene_all[drug].value_counts().sum() , '\n===========================================================\n' , '===========================================================\n' , 'RESULT: Total no. of samples tested for', drug, 'with nsSNP mutations:', meta_gene_all['drug_name'].value_counts().sum() , '\n===========================================================\n' , 'RESULT: No. of Sus and Res', drug, 'samples with nsSNP:\n', meta_gene_all['drug_name'].value_counts() , '\n===========================================================\n' , 'RESULT: Percentage of Sus and Res', drug, 'samples with nsSNP mutations:\n', meta_gene_all['drug_name'].value_counts(normalize = True)*100 , '\n===========================================================') ############################################################################### #%% Create a copy of indices for downstream mergeing meta_gene_all['index_orig'] = meta_gene_all.index meta_gene_all['index_orig_copy'] = meta_gene_all.index all(meta_gene_all.index.values == meta_gene_all['index_orig'].values) all(meta_gene_all.index.values == meta_gene_all['index_orig_copy'].values) ############################################################################## #%% Important sanity checks: Dr muts column for tidy split(), nsSNPs, etc. # Split based on semi colon dr_muts_col search = ";" # count of occurrence of ";" in dr_muts_col: No.of semicolons + 1 is no. of rows created * occurence count_df_dr = meta_gene_dr[['id', dr_muts_col]] count_df_dr['dr_semicolon_count'] = meta_gene_dr.loc[:, dr_muts_col].str.count(search, re.I) dr_sc_C = count_df_dr['dr_semicolon_count'].value_counts().reset_index() dr_sc_C dr_sc_C['index_semicolon'] = (dr_sc_C['index'] + 1) *dr_sc_C['dr_semicolon_count'] dr_sc_C expected_drC = dr_sc_C['index_semicolon'].sum() expected_drC # count no. of nsSNPs and extract those nsSNPs count_df_dr['dr_geneSNP_count'] = meta_gene_dr.loc[:, dr_muts_col].str.count(nssnp_match, re.I) dr_gene_count1 = count_df_dr['dr_geneSNP_count'].sum() ############################################################################### # This is to find out how many samples have 1 and more than 1 mutation,so you # can use it to check if your data extraction process for dr_muts # and other_muts has worked correctly AND also to check the dim of the # final formatted data. # This will have: unique COMBINATION of sample id and mutations #======== # First: counting mutations in dr_muts_col column #======== print('Now counting WT &', nssnp_match, 'muts within the column:', dr_muts_col) # drop na and extract a clean df clean_df = meta_data.dropna(subset=[dr_muts_col]) # sanity check: count na na_count = meta_data[dr_muts_col].isna().sum() if len(clean_df) == (total_samples - na_count): print('PASS: clean_df extracted: length is', len(clean_df) , '\nNo.of NAs in', dr_muts_col, '=', na_count, '/', total_samples , '\n==========================================================') else: sys.exit('FAIL: Could not drop NAs') dr_gene_count = 0 wt = 0 id_dr = [] id2_dr = [] dr_gene_mutsL = [] #nssnp_match_regex = re.compile(nssnp_match) for i, id in enumerate(clean_df.id): #print (i, id) #id_dr.append(id) #count_gene_dr = clean_df[dr_muts_col].iloc[i].count(gene_match) # can include stop muts count_gene_dr = len(re.findall(nssnp_match, clean_df[dr_muts_col].iloc[i], re.IGNORECASE)) gene_drL = re.findall(nssnp_match, clean_df[dr_muts_col].iloc[i], re.IGNORECASE) #print(count_gene_dr) if count_gene_dr > 0: id_dr.append(id) if count_gene_dr > 1: id2_dr.append(id) #print(id, count_gene_dr) dr_gene_count = dr_gene_count + count_gene_dr dr_gene_mutsL = dr_gene_mutsL + gene_drL count_wt = clean_df[dr_muts_col].iloc[i].count('WT') wt = wt + count_wt print('RESULTS:') print('Total WT in dr_muts_col:', wt) print('Total matches of', gene, 'SNP matches in', dr_muts_col, ':', dr_gene_count) print('Total samples with > 1', gene, 'nsSNPs in dr_muts_col:', len(id2_dr) ) print('Total matches of UNIQUE', gene, 'SNP matches in', dr_muts_col, ':', len(set(dr_gene_mutsL))) print('=================================================================') if dr_gene_count == dr_gene_count1: print('\nPass: dr gene SNP count match') else: sys.exit('\nFAIL: dr gene SNP count MISmatch') del(clean_df, na_count, i, id, wt, id2_dr, count_gene_dr, count_wt) #%% tidy_split(): dr_muts_col #========= # DF1: dr_muts_col # and remove leading white spaces #========= col_to_split1 = dr_muts_col print ('Firstly, applying tidy split on dr muts df', meta_gene_dr.shape , '\ncolumn name to apply tidy_split():' , col_to_split1 , '\n============================================================') # apply tidy_split() dr_WF0 = tidy_split(meta_gene_dr, col_to_split1, sep = ';') # remove leading white space else these are counted as distinct mutations as well dr_WF0[dr_muts_col] = dr_WF0[dr_muts_col].str.strip() if len(dr_WF0) == expected_drC: print('\nPass: tidy split on', dr_muts_col, 'worked' , '\nExpected nrows:', expected_drC , '\nGot nrows:', len(dr_WF0) ) else: print('\nFAIL: tidy split on', dr_muts_col, 'did not work' , '\nExpected nrows:', expected_drC , '\nGot nrows:', len(dr_WF0) ) # Extract only the samples/rows with nssnp_match #dr_gene_WF0 = dr_WF0.loc[dr_WF0[dr_muts_col].str.contains(gene_match)] dr_gene_WF0 = dr_WF0.loc[dr_WF0[dr_muts_col].str.contains(nssnp_match, regex = True, case = False)] #dr_gene_WF0_v2 = dr_WF0.loc[dr_WF0[dr_muts_col].str.contains(nssnp_match_captureG, regex = True, case = False)] print('Lengths after tidy split and extracting', nssnp_match, 'muts:' , '\nOld length:' , len(meta_gene_dr) , '\nLength after split:', len(dr_WF0) , '\nLength of nssnp df:', len(dr_gene_WF0) , '\nExpected len:', dr_gene_count , '\n=============================================================') # Important: Assign 'column name' on which split was performed as an extra column # This is so you can identify if mutations are dr_type or other in the final df dr_df = dr_gene_WF0.assign(mutation_info = dr_muts_col) print('Dim of dr_df:', dr_df.shape , '\n==============================================================' , '\nEnd of tidy split() on dr_muts, and added an extra column relecting mut_category' , '\n===============================================================') if other_muts_col in dr_df.columns: print('Dropping:', other_muts_col, 'from WF gene dr_df') dr_df = dr_df.drop([other_muts_col], axis = 1) ######################################################################## #%% Important sanity checks: other muts column for tidy split(), nsSNPs, etc. # Split based on semi colon on other_muts_col # count of occurrence of ";" in other_muts_col: No.of semicolons + 1 is no. of rows created * occurence count_df_other = meta_gene_other[['id', other_muts_col]] count_df_other['other_semicolon_count'] = meta_gene_other.loc[:, other_muts_col].str.count(search, re.I) other_sc_C = count_df_other['other_semicolon_count'].value_counts().reset_index() other_sc_C other_sc_C['index_semicolon'] = (other_sc_C['index']+1)*other_sc_C['other_semicolon_count'] other_sc_C expected_otherC = other_sc_C['index_semicolon'].sum() expected_otherC # count no. of nsSNPs and extract those nsSNPs count_df_other['other_geneSNP_count'] = meta_gene_other.loc[:, other_muts_col].str.count(nssnp_match, re.I) other_gene_count1 = count_df_other['other_geneSNP_count'].sum() # This is to find out how many samples have 1 and more than 1 mutation,so you # can use it to check if your data extraction process for dr_muts # and other_muts has worked correctly AND also to check the dim of the # final formatted data. # This will have: unique COMBINATION of sample id and mutations #======== # Second: counting mutations in other_muts_col column #======== print('Now counting WT &', nssnp_match, 'muts within the column:', other_muts_col) # drop na and extract a clean df clean_df = meta_data.dropna(subset=[other_muts_col]) # sanity check: count na na_count = meta_data[other_muts_col].isna().sum() if len(clean_df) == (total_samples - na_count): print('PASS: clean_df extracted: length is', len(clean_df) , '\nNo.of NAs =', na_count, '/', total_samples , '\n=========================================================') else: sys.exit('FAIL: Could not drop NAs') other_gene_count = 0 wt_other = 0 id_other = [] id2_other = [] other_gene_mutsL = [] for i, id in enumerate(clean_df.id): #print (i, id) #id_other.append(id) #count_gene_other = clean_df[other_muts_col].iloc[i].count(gene_match) # can include stop muts count_gene_other = len(re.findall(nssnp_match, clean_df[other_muts_col].iloc[i], re.IGNORECASE)) gene_otherL = re.findall(nssnp_match, clean_df[other_muts_col].iloc[i], re.IGNORECASE) #print(count_gene_other) if count_gene_other > 0: id_other.append(id) if count_gene_other > 1: id2_other.append(id) #print(id, count_gene_other) other_gene_count = other_gene_count + count_gene_other other_gene_mutsL = other_gene_mutsL + gene_otherL count_wt = clean_df[other_muts_col].iloc[i].count('WT') wt_other = wt_other + count_wt print('RESULTS:') print('Total WT in other_muts_col:', wt_other) print('Total matches of', gene, 'SNP matches in', other_muts_col, ':', other_gene_count) print('Total samples with > 1', gene, 'nsSNPs in other_muts_col:', len(id2_other) ) print('Total matches of UNIQUE', gene, 'SNP matches in', other_muts_col, ':', len(set(other_gene_mutsL))) print('=================================================================') if other_gene_count == other_gene_count1: print('\nPass: other gene SNP count match') else: sys.exit('\nFAIL: other gene SNP count MISmatch') del(clean_df, na_count, i, id, wt_other, id2_other, count_gene_other, count_wt ) #%% tidy_split(): other_muts_col #========= # DF2: other_muts_col # and remove leading white spaces #========= col_to_split2 = other_muts_col print ('applying second tidy split() separately on other muts df', meta_gene_other.shape , '\ncolumn name to apply tidy_split():', col_to_split2 , '\n============================================================') # apply tidy_split() other_WF1 = tidy_split(meta_gene_other, col_to_split2, sep = ';') # remove the leading white spaces in the column other_WF1[other_muts_col] = other_WF1[other_muts_col].str.strip() # extract only the samples/rows with nssnp_match #other_gene_WF1 = other_WF1.loc[other_WF1[other_muts_col].str.contains(gene_match)] other_gene_WF1 = other_WF1.loc[other_WF1[other_muts_col].str.contains(nssnp_match, regex = True, case = False)] print('Lengths after tidy split and extracting', gene_match, 'muts:', '\nOld length:' , len(meta_gene_other), '\nLength after split:', len(other_WF1), '\nLength of nssnp df:', len(other_gene_WF1), '\nExpected len:', other_gene_count , '\n=============================================================') # Important: Assign 'column name' on which split was performed as an extra column # This is so you can identify if mutations are dr_type or other in the final df other_df = other_gene_WF1.assign(mutation_info = other_muts_col) print('dim of other_df:', other_df.shape , '\n===============================================================' , '\nEnd of tidy split() on other_muts, and added an extra column relecting mut_category' , '\n===============================================================') if dr_muts_col in other_df.columns: print('Dropping:', dr_muts_col, 'from WF gene other_df') other_df = other_df.drop([dr_muts_col], axis = 1) ############################################################################### #%% Finding ambiguous muts common_snps_dr_other = set(dr_gene_mutsL).intersection(set(other_gene_mutsL)) #%% More sanity checks: expected unique snps and nrows in LF data expected_unique_snps = len(set(dr_gene_mutsL)) + len(set(other_gene_mutsL)) - len(common_snps_dr_other) expected_rows = dr_gene_count + other_gene_count #%% Useful results to note==> counting dr, other and common muts print('\n===================================================================' , '\nCount unique nsSNPs for', gene, ':' , expected_unique_snps , '\n===================================================================') Vcounts_dr = pd.Series(dr_gene_mutsL).value_counts() Vcounts_common_dr = Vcounts_dr.get(list(common_snps_dr_other)) print('\n===================================================================' , "\nCount of samples for common muts in dr muts\n" , Vcounts_common_dr , '\n===================================================================') Vcounts_other = pd.Series(other_gene_mutsL).value_counts() Vcounts_common_other = Vcounts_other.get(list(common_snps_dr_other)) print('\n===================================================================' , '\nCount of samples for common muts in other muts\n' , Vcounts_common_other , '\n===================================================================') print('\n===================================================================' , '\nPredicting total no. of rows in the curated df:', expected_rows , '\n===================================================================') #%%another way: Add value checks for dict so you can know if its correct for LF data count below dr_snps_vc_dict = pd.Series(dr_gene_mutsL).value_counts().to_dict() other_snps_vc_dict = pd.Series(other_gene_mutsL).value_counts().to_dict() for k, v in dr_snps_vc_dict.items(): if k in common_snps_dr_other: print(k,v) for k, v in other_snps_vc_dict.items(): if k in common_snps_dr_other: print(k,v) # using defaultdict Cdict = collections.defaultdict(int) # iterating key, val with chain() for key, val in itertools.chain(dr_snps_vc_dict.items(), other_snps_vc_dict.items()): if key in common_snps_dr_other: Cdict[key] += val else: Cdict[key] = val print(dict(Cdict)) for k, v in Cdict.items(): if k in common_snps_dr_other: print(k,v) ############################################################################### # USE Vcounts to get expected curated df # resolve dm om muts funda #%%#%% Concatenating two dfs: gene_LF0 # equivalent of rbind in R #========== # Concatentating the two dfs: equivalent of rbind in R #========== # Important: Change column names to allow concat: # dr_muts.. & other_muts : 'mutation' print('Now concatenating the two dfs by row' , '\nFirst assigning a common colname: "mutation" to the col containing muts' , '\nThis is done for both dfs' , '\n===================================================================') dr_df.columns dr_df.rename(columns = {dr_muts_col: 'mutation'}, inplace = True) dr_df.columns other_df.columns other_df.rename(columns = {other_muts_col: 'mutation'}, inplace = True) other_df.columns if len(dr_df.columns) == len(other_df.columns): print('Checking dfs for concatening by rows:' , '\nDim of dr_df:', dr_df.shape , '\nDim of other_df:', other_df.shape , '\nExpected nrows:', len(dr_df) + len(other_df) , '\n=============================================================') else: sys.exit('FAIL: No. of cols mismatch for concatenating') # checking colnames before concat print('Checking colnames BEFORE concatenating the two dfs...') if (set(dr_df.columns) == set(other_df.columns)): print('PASS: column names match necessary for merging two dfs') else: sys.exit('FAIL: Colnames mismatch for concatenating!') # concatenate (axis = 0): Rbind, adn keeo original index print('Now appending the two dfs: Rbind') gene_LF_comb = pd.concat([dr_df, other_df] #, ignore_index = True , axis = 0) if gene_LF_comb.index.nunique() == len(meta_gene_all.index): print('\nPASS:length of index in LF data:', len(gene_LF_comb.index) , '\nLength of unique index in LF data:', gene_LF_comb.index.nunique() ) else: sys.exit('\nFAIL: expected length for combined LF data mismatch:' , '\nExpected length:', len(meta_gene_all.index) , '\nGot:', gene_LF_comb.index.nunique() ) print('Finding stop mutations in concatenated df') stop_muts = gene_LF_comb['mutation'].str.contains('\*').sum() if stop_muts == 0: print('PASS: No stop mutations detected') else: print('stop mutations detected' , '\nNo. of stop muts:', stop_muts, '\n' , gene_LF_comb.groupby(['mutation_info'])['mutation'].apply(lambda x: x[x.str.contains('\*')].count()) , '\nNow removing them') gene_LF0_nssnp = gene_LF_comb[~gene_LF_comb['mutation'].str.contains('\*')] print('snps only: by subtracting stop muts:', len(gene_LF0_nssnp)) gene_LF0 = gene_LF_comb[gene_LF_comb['mutation'].str.contains(nssnp_match, case = False)] print('snps only by direct regex:', len(gene_LF0)) if gene_LF0_nssnp.equals(gene_LF0): print('PASS: regex for extracting nssnp worked correctly & stop mutations successfully removed' , '\nUsing the regex extracted df') else: sys.exit('FAIL: posssibly regex or no of stop mutations' , 'Regex being used:', nssnp_match) #sys.exit() # checking colnames and length after concat print('Checking colnames AFTER concatenating the two dfs...') if (set(dr_df.columns) == set(gene_LF0.columns)): print('PASS: column names match' , '\n=============================================================') else: sys.exit('FAIL: Colnames mismatch AFTER concatenating') print('Checking concatenated df') if len(gene_LF0) == (len(dr_df) + len(other_df))- stop_muts: print('PASS:length of df after concat match' , '\n===============================================================') else: sys.exit('FAIL: length mismatch') #%% NEW check2:id, sample, etc. gene_LF0['id'].count() gene_LF0['id'].nunique() gene_LF0['sample'].nunique() gene_LF0['mutation_info'].value_counts() gene_LF0[drug].isna().sum() #%% Concatenating two dfs sanity checks: gene_LF1 #========================================================= # This is hopefully clean data, but just double check # Filter LF data so that you only have # mutations corresponding to nssnp_match (case insensitive) # this will be your list you run OR calcs #========================================================= print('Length of gene_LF0:', len(gene_LF0) , '\nThis should be what we need. But just double checking and extracting nsSNP for', gene , '\nfrom LF0 (concatenated data) using case insensitive regex match:', nssnp_match) gene_LF1 = gene_LF0[gene_LF0['mutation'].str.contains(nssnp_match, regex = True, case = False)] if len(gene_LF0) == len(gene_LF1): print('PASS: length of gene_LF0 and gene_LF1 match', '\nConfirming extraction and concatenating worked correctly' , '\n==========================================================') else: print('FAIL: BUT NOT FATAL!' , '\ngene_LF0 and gene_LF1 lengths differ' , '\nsuggesting error in extraction process' , ' use gene_LF1 for downstreama analysis' , '\n=========================================================') print('Length of dfs pre and post processing...' , '\ngene WF data (unique samples in each row):',len(meta_gene_all) , '\ngene LF data (unique mutation in each row):', len(gene_LF1) , '\n=============================================================') # sanity check for extraction # This ought to pass if nsnsp_match happens right at the beginning of creating 'expected_rows' print('Verifying whether extraction process worked correctly...') if len(gene_LF1) == expected_rows: print('PASS: extraction process performed correctly' , '\nExpected length:', expected_rows , '\nGot:', len(gene_LF1) , '\nRESULT: Total no. of mutant sequences for logo plot:', len(gene_LF1) , '\n=========================================================') else: print('FAIL: extraction process has bugs' , '\nExpected length:', expected_rows , '\nGot:', len(gene_LF1) , '\nDebug please' , '\n=========================================================') # more sanity checks print('Performing some more sanity checks...') # From LF1: useful for OR counts # no. of unique muts distinct_muts = gene_LF1.mutation.value_counts() print('Distinct genomic mutations:', len(distinct_muts)) # no. of samples contributing the unique muts gene_LF1.id.nunique() print('No.of samples contributing to distinct genomic muts:', gene_LF1.id.nunique()) # no. of dr and other muts mut_grouped = gene_LF1.groupby('mutation_info').mutation.nunique() print('No.of unique dr and other muts:\n', gene_LF1.groupby('mutation_info').mutation.nunique()) # sanity check if len(distinct_muts) == mut_grouped.sum() : print('PASS:length of LF1 is as expected' , '\n===============================================================') else: print('FAIL: Mistmatch in count of muts' , '\nExpected count:', len(distinct_muts) , '\nActual count:', mut_grouped.sum() , '\nMuts should be distinct within dr* and other* type' , '\nInspecting...possibly ambiguous muts' , '\nNo. of possible ambiguous muts:', mut_grouped.sum() - len(distinct_muts) , '\n=========================================================') muts_split = list(gene_LF1.groupby('mutation_info')) dr_muts = muts_split[0][1].mutation other_muts = muts_split[1][1].mutation print('splitting muts by mut_info:', muts_split) print('no.of dr_muts samples:', len(dr_muts)) print('no. of other_muts samples', len(other_muts)) #%% Ambiguous muts # IMPORTANT: The same mutation cannot be classed as a drug AND 'others' if dr_muts.isin(other_muts).sum() & other_muts.isin(dr_muts).sum() > 0: print('WARNING: Ambiguous muts detected in dr_ and other_ mutation category' , '\n===============================================================') else: print('PASS: NO ambiguous muts detected' , '\nMuts are unique to dr_ and other_ mutation class' , '\n=========================================================') # inspect dr_muts and other muts: Fixed in case no ambiguous muts detected! if dr_muts.isin(other_muts).sum() & other_muts.isin(dr_muts).sum() > 0: print('Finding ambiguous muts...' , '\n=========================================================' , '\nTotal no. of samples in dr_muts present in other_muts:', dr_muts.isin(other_muts).sum() , '\nThese are:', dr_muts[dr_muts.isin(other_muts)] , '\n=========================================================' , '\nTotal no. of samples in other_muts present in dr_muts:', other_muts.isin(dr_muts).sum() , '\nThese are:\n', other_muts[other_muts.isin(dr_muts)] , '\n=========================================================') print('Counting no. of ambiguous muts...' , '\nNo. of ambiguous muts in dr:' , len(dr_muts[dr_muts.isin(other_muts)].value_counts().keys().tolist()) , '\nNo. of ambiguous muts in other:' , len(other_muts[other_muts.isin(dr_muts)].value_counts().keys().tolist()) , '\n=========================================================') if dr_muts[dr_muts.isin(other_muts)].nunique() == other_muts[other_muts.isin(dr_muts)].nunique(): common_muts = dr_muts[dr_muts.isin(other_muts)].value_counts().keys().tolist() print('Distinct no. of ambigiuous muts detected:'+ str(len(common_muts)) , '\nlist of ambiguous mutations (see below):', *common_muts, sep = '\n') print('\n===========================================================') else: #sys.exit('Error: ambiguous muts present, but extraction failed. Debug!') print('No: ambiguous muts present') #%% Ambiguous muts: revised annotation for mutation_info ambiguous_muts_df = gene_LF1[gene_LF1['mutation'].isin(common_muts)] ambiguous_muts_value_counts = ambiguous_muts_df.groupby('mutation')['mutation_info'].value_counts() ambiguous_muts_value_counts gene_LF1_orig = gene_LF1.copy() gene_LF1_orig.equals(gene_LF1) # copy the old columns for checking gene_LF1['mutation_info_orig'] = gene_LF1['mutation_info'] gene_LF1['mutation_info_v1'] = gene_LF1['mutation_info'] gene_LF1['mutation_info'].value_counts() #%% Inspect ambiguous muts #===================================== # Now create a new df that will have: # ambiguous muts # mutation_info # revised mutation_info # The revised label is based on value_counts # for mutaiton_info. The corresponding mutation_info: # label is chosen that corresponds to the max of value counts #===================================== ambig_muts_rev_df = pd.DataFrame() changes_val = [] changes_dict = {} for i in common_muts: #print(i) temp_df = gene_LF1[gene_LF1['mutation'] == i][['mutation', 'mutation_info_orig']] # DANGER: ASSUMES TWO STATES ONLY and that value_counts sorts by descending max_info_table = gene_LF1[gene_LF1['mutation'] == i][['mutation', 'mutation_info_orig']].value_counts() revised_label = max_info_table[[0]].index[0][1] # max value_count old_label = max_info_table[[1]].index[0][1] # min value_count print('\nAmbiguous mutation labels...' , '\nSetting mutation_info for', i, 'to', revised_label) temp_df['mutation_info_REV'] = np.where( (temp_df['mutation_info_orig'] == old_label) , revised_label , temp_df['mutation_info_orig']) ambig_muts_rev_df = pd.concat([ambig_muts_rev_df,temp_df]) f = max_info_table[[1]] old_label_count = f[[0]][0] changes_val.append(old_label_count) cc_dict = f.to_dict() changes_dict.update(cc_dict) ambig_muts_rev_df['mutation_info_REV'].value_counts() ambig_muts_rev_df['mutation_info_orig'].value_counts() changes_val changes_total = sum(changes_val) changes_dict #%% OUTFILE 3, write file: ambiguous muts and ambiguous mut counts #=================== # ambiguous muts #======================= #dr_muts.to_csv('dr_muts.csv', header = True) #other_muts.to_csv('other_muts.csv', header = True) if dr_muts.isin(other_muts).sum() & other_muts.isin(dr_muts).sum() > 0: out_filename_ambig_muts = gene.lower() + '_ambiguous_muts.csv' outfile_ambig_muts = outdir + '/' + out_filename_ambig_muts print('\n----------------------------------' , '\nWriting file: ambiguous muts' , '\n----------------------------------' , '\nFilename:', outfile_ambig_muts) inspect = gene_LF1[gene_LF1['mutation'].isin(common_muts)] inspect.to_csv(outfile_ambig_muts, index = True) print('Finished writing:', out_filename_ambig_muts , '\nNo. of rows:', len(inspect) , '\nNo. of cols:', len(inspect.columns) , '\nNo. of rows = no. of samples with the ambiguous muts present:' , dr_muts.isin(other_muts).sum() + other_muts.isin(dr_muts).sum() , '\n=============================================================') del(out_filename_ambig_muts) #%% FIXME: ambig mut counts #======================= # ambiguous mut counts #======================= out_filename_ambig_mut_counts = gene.lower() + '_ambiguous_mut_counts.csv' outfile_ambig_mut_counts = outdir + '/' + out_filename_ambig_mut_counts print('\n----------------------------------' , '\nWriting file: ambiguous muts' , '\n----------------------------------' , '\nFilename:', outfile_ambig_mut_counts) ambiguous_muts_value_counts.to_csv(outfile_ambig_mut_counts, index = True) #%%FIXME: TODO: Add sanity check to make sure you can add value_count checks #%% Resolving ambiguous muts # Merging ambiguous muts #================= # Merge ambig muts # with gene_LF1 #=================== ambig_muts_rev_df.index gene_LF1.index all(ambig_muts_rev_df.index.isin(gene_LF1.index)) gene_LF1.loc[ambig_muts_rev_df.index, 'mutation_info_v1'] = ambig_muts_rev_df['mutation_info_REV'] gene_LF1['mutation_info_orig'].value_counts() gene_LF1['mutation_info_v1'].value_counts() foo = gene_LF1.iloc[ambig_muts_rev_df.index] # Sanity check1: if there are still any ambiguous muts muts_split_rev = list(gene_LF1.groupby('mutation_info_v1')) dr_muts_rev = muts_split_rev[0][1].mutation other_muts_rev = muts_split_rev[1][1].mutation print('splitting muts by mut_info:', muts_split_rev) print('no.of dr_muts samples:', len(dr_muts_rev)) print('no. of other_muts samples', len(other_muts_rev)) if not dr_muts_rev.isin(other_muts_rev).sum() & other_muts_rev.isin(dr_muts_rev).sum() > 0: print('\nAmbiguous muts corrected. Proceeding with downstream analysis') else: print('\nAmbiguous muts NOT corrected. Quitting!') sys.exit() gene_LF1['mutation_info_v1'].value_counts() #%% PHEW! Good to go for downstream stuff