tidying script
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b82cc11dbe
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1 changed files with 133 additions and 197 deletions
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@ -30,41 +30,46 @@ os.chdir(homedir + '/git/LSHTM_analysis/scripts')
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os.getcwd()
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# local import
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from reference_dict import my_aa_dict # CHECK DIR STRUC THERE!
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#from reference_dict import my_aa_dict # CHECK DIR STRUC THERE!
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from reference_dict import low_3letter_dict
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#=======================================================================
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#%% command line args
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arg_parser = argparse.ArgumentParser()
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arg_parser.add_argument('-d', '--drug', help = 'drug name', default = 'pyrazinamide')
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arg_parser.add_argument('-g', '--gene', help = 'gene name', default = 'pncA') # case sensitive
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args = arg_parser.parse_args()
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#arg_parser = argparse.ArgumentParser()
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#arg_parser.add_argument('-d', '--drug', help = 'drug name', default = 'pyrazinamide')
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#arg_parser.add_argument('-g', '--gene', help = 'gene name', default = 'pncA') # case sensitive
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#args = arg_parser.parse_args()
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#=======================================================================
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#%% variable assignment: input and output
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#drug = 'pyrazinamide'
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#gene = 'pncA'
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#gene_match = gene + '_p.'
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drug = 'pyrazinamide'
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gene = 'pncA'
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gene_match = gene + '_p.'
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# cmd variables
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drug = args.drug
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gene = args.gene
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gene_match = gene + '_p.'
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#drug = args.drug
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#gene = args.gene
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#gene_match = gene + '_p.'
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#==========
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# dir
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#==========
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datadir = homedir + '/' + 'git/Data'
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indir = datadir + '/' + drug + '/' + 'input'
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outdir = datadir + '/' + drug + '/' + 'output'
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#=======
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# input
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#=======
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in_filename_snpinfo = 'ns' + gene.lower() + '_snp_info.csv'
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in_filename_afor = gene.lower() + '_af_or.csv'
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in_filename_afor_kin = gene.lower() + '_af_or_kinship.csv'
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infile0 = indir + '/' + in_filename_snpinfo
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infile1 = outdir + '/' + in_filename_afor
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infile2 = outdir + '/' + in_filename_afor_kin
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print('Input file1:', infile1
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print('Input file0:', infile0
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, '\nInput file1:', infile1
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, '\nInput file2:', infile2
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, '\n===================================================================')
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@ -77,7 +82,7 @@ print('Output file:', outfile
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, '\n===================================================================')
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del(in_filename_afor, in_filename_afor_kin, datadir, outdir)
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del(in_filename_afor, in_filename_afor_kin, datadir, indir, outdir)
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#%% end of variable assignment for input and output files
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#=======================================================================
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#%% format mutations
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@ -86,211 +91,142 @@ del(in_filename_afor, in_filename_afor_kin, datadir, outdir)
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#========================
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# read input csv files to combine
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#========================
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snpinfo_df = pd.read_csv(infile0, sep = ',')
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snpinfo_ncols = len(snpinfo_df.columns)
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snpinfo_nrows = len(snpinfo_df)
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print('No. of rows in', infile0, ':', snpinfo_nrows
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, '\nNo. of cols in', infile0, ':', snpinfo_ncols)
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afor_df = pd.read_csv(infile1, sep = ',')
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afor_df_ncols = len(afor_df.columns)
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afor_df_nrows = len(afor_df)
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print('No. of rows in', infile1, ':', afor_df_nrows
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, '\nNo. of cols in', infile1, ':', afor_df_ncols)
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afor_ncols = len(afor_df.columns)
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afor_nrows = len(afor_df)
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print('No. of rows in', infile1, ':', afor_nrows
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, '\nNo. of cols in', infile1, ':', afor_ncols)
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afor_kin_df = pd.read_csv(infile2, sep = ',')
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afor_kin_df_nrows = len(afor_kin_df)
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afor_kin_df_ncols = len(afor_kin_df.columns)
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print('No. of rows in', infile2, ':', afor_kin_df_nrows
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, '\nNo. of cols in', infile2, ':', afor_kin_df_ncols)
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afor_kin_nrows = len(afor_kin_df)
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afor_kin_ncols = len(afor_kin_df.columns)
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print('No. of rows in', infile2, ':', afor_kin_nrows
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, '\nNo. of cols in', infile2, ':', afor_kin_ncols)
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#=======
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# Iterate through the dict, create a lookup dict i.e
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# lookup_dict = {three_letter_code: one_letter_code}.
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# lookup dict should be the key and the value (you want to create a column for)
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# Then use this to perform the mapping separetly for wild type and mutant cols.
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# The three letter code is extracted using a string match match from the dataframe and then converted
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# to 'pandas series'since map only works in pandas series
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#=======
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gene_regex = gene_match.lower()+'(\w{3})'
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print('gene regex being used:', gene_regex)
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#%% Process afor_df
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#1) pull all snp_info so you have ref_allele, etc
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# i.e merge afor_df and snpinfo_df
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# find merging column
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# initialise a sub dict that is lookup dict for three letter code to 1-letter code
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# adding three more cols
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lookup_dict = dict()
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for k, v in my_aa_dict.items():
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lookup_dict[k] = v['one_letter_code']
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# wt = gene_LF1['mutation'].str.extract('gene_p.(\w{3})').squeeze() # converts to a series that map works on
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wt = afor_df['mutation'].str.extract(gene_regex).squeeze()
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afor_df['wild_type'] = wt.map(lookup_dict)
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mut = afor_df['mutation'].str.extract('\d+(\w{3})$').squeeze()
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afor_df['mutant_type'] = mut.map(lookup_dict)
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left_df = afor_df.copy()
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left_df_nrows = len(left_df)
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left_df_ncols = len(left_df.columns)
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# extract position info from mutation column separetly using string match
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afor_df['position'] = afor_df['mutation'].str.extract(r'(\d+)')
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right_df = snpinfo_df.copy()
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right_df_nrows = len(right_df)
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right_df_ncols = len(right_df.columns)
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# combine the wild_type+poistion+mutant_type columns to generate
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# mutationinformation (matches mCSM output field)
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# Remember to use .map(str) for int col types to allow string concatenation
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common_cols = np.intersect1d(left_df.columns, right_df.columns).tolist()
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print('Length of common cols:', len(common_cols)
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, '\ncommon column/s:', common_cols, 'type:', type(common_cols))
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afor_df['mutationinformation'] = afor_df['wild_type'] + afor_df['position'].map(str) + afor_df['mutant_type']
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print('Created column: mutationinformation'
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, '\n====================================================================='
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, afor_df['mutationinformation'].head(10))
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print('selecting consistent dtypes for merging (object i.e string)')
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#https://stackoverflow.com/questions/44639772/python-pandas-column-dtype-object-causing-merge-to-fail-with-dtypewarning-colu
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merging_cols = left_df[common_cols].select_dtypes(include = [object]).columns.tolist()
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print(merging_cols)
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nmerging_cols = len(merging_cols)
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print(' length of merging cols:', nmerging_cols
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, '\nmerging cols:', merging_cols, 'type:', type(merging_cols))
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# sanity check
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ncols_add = 4 # beware of hardcoding (3 cols for mcsm style mut + 1 for concatenating them all)
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if len(afor_df.columns) == afor_df_ncols + ncols_add:
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afor_df_ncols = len(afor_df.columns) # update afor_df_ncols after adding cols
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print('PASS: successfully added', ncols_add, 'cols'
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, '\nold length:', afor_df_ncols
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, '\nnew length:', len(afor_df.columns))
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else:
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print('FAIL: failed to add cols:'
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, '\nExpected cols:', afor_df_ncols + ncols_add
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, '\nGot:', len(afor_df.columns))
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sys.exit()
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#%% Detect mutation format to see if you apply this func
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# FIXME
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#afor_df.iloc[[0]].str.match('pnca_')
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#afor_df.dtypes
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# drop duplicates else the expected rows don't match
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print('Checking for duplicates in common col:', common_cols
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, '\nNo of duplicates:'
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, len(right_df[right_df.duplicated(common_cols)])
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, '\noriginal length:', right_df_nrows)
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#foo = afor_df.loc[:, afor_df.dtypes == object]
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genomic_mut_regex = gene_match.lower()+'\w{3}\d+\w{3}'
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print('gene regex being used:', genomic_mut_regex)
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afor_df[(afor_df == genomic_mut_regex).any(axis = 1)]
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#%% Finding common col to merge on
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# Define merging column: multiple cols have been used for merge else the common cols
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# get suffixes '_x' and '_y' attached
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# also, couldn't include 'position' in merging_cols since data types don't match
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merging_cols = ['wild_type', 'mutant_type', 'mutationinformation']
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ncommon_cols= len(merging_cols)
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right_df = right_df[~right_df.duplicated(common_cols)]
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right_df_nrows = len(right_df)
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print('\nrevised length:', right_df_nrows)
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# checking cross-over of mutations in the two dfs to merge
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ndiff1 = afor_kin_df_nrows - afor_df['mutationinformation'].isin(afor_kin_df['mutationinformation']).sum()
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print(ndiff1)
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ndiff2 = afor_kin_df_nrows - afor_kin_df['mutationinformation'].isin(afor_df['mutationinformation']).sum()
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print(ndiff2)
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ndiff1 = afor_nrows - afor_df['mutation'].isin(snpinfo_df['mutation']).sum()
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print('There are', ndiff1, 'mutations with OR, but no snp_info'
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, '\nExtracting and writing out file')
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#%% combining dfs
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#afor_df[afor_df['mutation'].isin(snpinfo_df['mutation'])]
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missing_mutinfo = afor_df[~afor_df['mutation'].isin(snpinfo_df['mutation'])]
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#len(missing_mutinfo.duplicated(common_cols))
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#missing_mutinfo.to_csv('infoless_muts.csv')
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ndiff2 = snpinfo_nrows - snpinfo_df['mutation'].isin(afor_df['mutation']).sum()
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print('There are', ndiff2, 'mutations that do not have OR, but have snp_info')
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# Define join type
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#my_join = 'inner'
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#my_join = 'outer'
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#my_join = 'right'
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#my_join = 'left'
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my_join = 'outer'
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my_join = 'left'
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print('combing with join:', my_join)
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combined_df1 = pd.merge(left_df, right_df, on = merging_cols, how = my_join)
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print('nrows:', len(combined_df1)
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, '\nshape:', combined_df1.shape)
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# inner = 252
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left_df_nrows - ndiff1
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# outer = 331
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right_df_nrows + ndiff1
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# right = 290
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right_df_nrows
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# left = 293
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left_df_nrows
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#%%
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# see if you want an extra clause here!
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# Define join type
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#my_join = 'inner'
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#my_join = 'outer'
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#my_join = 'right'
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my_join = 'left'
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fail = False
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# sanity check: how many muts from afor_kin_df are in afor_df. should be a complete subset
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if ndiff2 == 0:
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print('PASS: all muts in afor_kin_df are present in afor_df'
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, '\nProceeding with combining the dfs...')
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combined_df = pd.merge(afor_df, afor_kin_df, on = merging_cols, how = my_join)
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if my_join == ('outer' or 'left') :
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print('combing with:', my_join)
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expected_rows = afor_df_nrows + ndiff1
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combined_df1 = pd.merge(left_df, right_df, on = merging_cols, how = my_join)
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if my_join == ('inner' or 'right'):
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print('combing with:', my_join)
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expected_rows = afor_kin_df_nrows
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if my_join == 'inner':
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#expected_rows = left_df_nrows - ndiff1
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expected_rows = left_df.shape[0] - ndiff1
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expected_cols = afor_df_ncols + afor_kin_df_ncols - ncommon_cols
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if my_join == 'outer':
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#expected_rows = right_df_nrows + ndiff1
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expected_rows = right_df.shape[0] + ndiff1
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if len(combined_df) == expected_rows and len(combined_df.columns) == expected_cols:
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if my_join == 'right':
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#expected_rows = right_df_nrows
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expected_rows = right_df.shape[0]
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if my_join == 'left':
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#expected_rows = left_df_nrows
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expected_rows = left_df.shape[0]
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expected_cols = left_df.shape[1] + right_df.shape[1] - nmerging_cols
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if len(combined_df1) == expected_rows and len(combined_df1.columns) == expected_cols:
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print('PASS: successfully combined dfs with:', my_join, 'join')
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else:
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print('FAIL: combined_df\'s expected rows and cols not matched')
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fail = True # BAD practice! just a placeholder to avoid code duplication
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fail = True
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print('\nExpected no. of rows:', expected_rows
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, '\nGot:', len(combined_df)
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, '\nGot:', len(combined_df1)
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, '\nExpected no. of cols:', expected_cols
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, '\nGot:', len(combined_df.columns))
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, '\nGot:', len(combined_df1.columns))
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if fail:
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sys.exit('ERROR: combined_df may be incorrectly combined')
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else:
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print('FAIL: numbers mismatch, mutations present in afor_kin_df but not in afor_df')
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sys.exit('ERROR: Not all mutations in the kinship_df are present in the df with other ORs')
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#%% check duplicate cols: ones containing suffix '_x' or '_y'
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# should only be position
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foo = combined_df.filter(regex = r'.*_x|_y', axis = 1)
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print(foo.columns) # should only be position
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# drop position col containing suffix '_y' and then rename col without suffix
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combined_or_df = combined_df.drop(combined_df.filter(regex = r'.*_y').columns, axis = 1)
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combined_or_df['position_x'].head()
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# renaming columns
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combined_or_df.rename(columns = {'position_x': 'position'}, inplace = True)
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combined_or_df['position'].head()
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# recheck
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foo = combined_or_df.filter(regex = r'.*_x|_y', axis = 1)
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print(foo.columns) # should only be empty
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#%% rearraging columns
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print('Dim of df prefromatting:', combined_or_df.shape)
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print(combined_or_df.columns)
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#%% reorder columns
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#https://stackoverflow.com/questions/13148429/how-to-change-the-order-of-dataframe-columns
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# setting column's order
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output_df = combined_or_df[['mutation',
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'mutationinformation',
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'wild_type',
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'position',
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'mutant_type',
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'chr_num_allele',
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'ref_allele',
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'alt_allele',
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'mut_info',
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'mut_type',
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'gene_id',
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'gene_number',
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'mut_region',
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'reference_allele',
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'alternate_allele',
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'chromosome_number',
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'af',
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'af_kin',
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'or_kin',
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'or_logistic',
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'or_mychisq',
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'est_chisq',
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'or_fisher',
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'ci_low_logistic',
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'ci_hi_logistic',
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'ci_low_fisher',
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'ci_hi_fisher',
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'pwald_kin',
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'pval_logistic',
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'pval_fisher',
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'pval_chisq',
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'beta_logistic',
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'beta_kin',
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'se_logistic',
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'se_kin',
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'zval_logistic',
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'logl_H1_kin',
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'l_remle_kin',
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'n_diff',
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'tot_diff',
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'n_miss']]
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# sanity check after rearranging
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if combined_or_df.shape == output_df.shape and set(combined_or_df.columns) == set(output_df.columns):
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print('PASS: Successfully formatted df with rearranged columns')
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else:
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sys.exit('FAIL: something went wrong when rearranging columns!')
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#%% write file
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print('\n====================================================================='
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, '\nWriting output file:\n', outfile
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, '\nNo.of rows:', len(output_df)
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, '\nNo. of cols:', len(output_df.columns))
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output_df.to_csv(outfile, index = False)
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sys.exit()
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# update nrows and ncols
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afor_info_nrows = len(afor_info_df)
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afor_info_ncols = len(afor_info_df.columns)
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#%%
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