tidy script for linking or_kinship with missense variant info
This commit is contained in:
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805868ce7e
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2 changed files with 143 additions and 153 deletions
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@ -4,15 +4,15 @@ import pandas as pd
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DEBUG = False
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#%%
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#def find_missense(test_df, ref_allele1, alt_allele0):
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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'):
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#def find_missense(df, ref_allele1, alt_allele0):
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def find_missense(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'):
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"""Find mismatches in pairwise comparison of strings b/w col_a and col_b
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Case insensitive, converts strings to uppercase before comparison
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@test_df: df containing columns to compare
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@df: df containing columns to compare
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@type: pandas df
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@ref_allele_column: column containing ref allele str
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@ -37,7 +37,7 @@ def find_missense(test_df, ref_allele_column, alt_allele_column, n_diff_colname
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name will override original column
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@rtype: pandas df
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"""
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for ind, val in test_df.iterrows():
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for ind, val in df.iterrows():
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if DEBUG:
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print('index:', ind, 'value:', val
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, '\n============================================================')
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@ -46,9 +46,9 @@ def find_missense(test_df, ref_allele_column, alt_allele_column, n_diff_colname
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if DEBUG:
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print('ref_allele_string:', ref_a, 'alt_allele_string:', alt_a)
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difference = sum(1 for e in zip(ref_a, alt_a) if e[0] != e[1])
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test_df.at[ind, n_diff_colname] = difference # adding column
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df.at[ind, n_diff_colname] = difference # adding column
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tot_difference = difference + abs(len(ref_a) - len(alt_a))
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test_df.at[ind, tot_diff_colname] = tot_difference # adding column
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df.at[ind, tot_diff_colname] = tot_difference # adding column
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if difference != tot_difference:
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print('WARNING: lengths of ref_allele and alt_allele differ at index:', ind
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, '\nNon-missense muts detected')
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@ -57,29 +57,29 @@ def find_missense(test_df, ref_allele_column, alt_allele_column, n_diff_colname
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ref_aln = ''
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alt_aln = ''
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if ref_a == alt_a:
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##test_df.at[ind, 'ref_allele'] = 'no_change' # adding column
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##test_df.at[ind, 'alt_allele'] = 'no_change' # adding column
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test_df.at[ind, ref_a_colname] = 'no_change' # adding column
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test_df.at[ind, alt_a_colname] = 'no_change' # adding column
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##df.at[ind, 'ref_allele'] = 'no_change' # adding column
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##df.at[ind, 'alt_allele'] = 'no_change' # adding column
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df.at[ind, ref_a_colname] = 'no_change' # adding column
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df.at[ind, alt_a_colname] = 'no_change' # adding column
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elif len(ref_a) == len(alt_a) and len(ref_a) > 0:
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print('ref:', ref_a, 'alt:', alt_a)
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for n in range(len(ref_a)):
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if ref_a[n] != alt_a[n]:
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ref_aln += ref_a[n]
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alt_aln += alt_a[n]
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##test_df.at[ind, 'ref_allele'] = ref_aln
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##test_df.at[ind, 'alt_allele'] = alt_aln
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test_df.at[ind, ref_a_colname] = ref_aln
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test_df.at[ind, alt_a_colname] = alt_aln
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##df.at[ind, 'ref_allele'] = ref_aln
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##df.at[ind, 'alt_allele'] = alt_aln
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df.at[ind, ref_a_colname] = ref_aln
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df.at[ind, alt_a_colname] = alt_aln
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print('ref:', ref_aln)
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print('alt:', alt_aln)
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else:
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##test_df.at[ind, 'ref_allele'] = 'ERROR_Not_nsSNP'
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##test_df.at[ind, 'alt_allele'] = 'ERROR_Not_nsSNP'
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test_df.at[ind, ref_a_colname] = 'ERROR_Not_nsSNP'
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test_df.at[ind, alt_a_colname] = 'ERROR_Not_nsSNP'
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##df.at[ind, 'ref_allele'] = 'ERROR_Not_nsSNP'
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##df.at[ind, 'alt_allele'] = 'ERROR_Not_nsSNP'
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df.at[ind, ref_a_colname] = 'ERROR_Not_nsSNP'
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df.at[ind, alt_a_colname] = 'ERROR_Not_nsSNP'
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return test_df
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return df
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#========================================
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# a representative example
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#eg_df = pd.read_csv('pnca_assoc.txt', sep = '\t', nrows = 10, header = 0, index_col = False)
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@ -90,7 +90,7 @@ def find_missense(test_df, ref_allele_column, alt_allele_column, n_diff_colname
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#def main():
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#find_missense(eg_df, ref_allele1 = 'ref_allele', alt_allele0 = 'alt_allele')
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# find_missense(test_df = eg_df, ref_allele_column = 'ref_allele1', alt_allele_column = 'alt_allele0')
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# find_missense(df = eg_df, ref_allele_column = 'ref_allele1', alt_allele_column = 'alt_allele0')
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# print(eg_df)
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#if __name__ == '__main__':
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@ -74,13 +74,8 @@ if not outdir:
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#=======
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# input
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#=======
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info_filename = 'snp_info.txt'
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snp_info = datadir + '/' + info_filename
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print('Info file: ', snp_info
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, '\n============================================================')
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#gene_info_filename = 'ns'+ gene.lower()+ '_snp_info.txt' # without headers
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gene_info_filename = 'ns'+ gene.lower()+ '_snp_info.csv'
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gene_info_filename = 'ns'+ gene.lower()+ '_snp_info.txt'
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#gene_info_filename = 'ns'+ gene.lower()+ '_snp_info.csv'
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gene_info = indir + '/' + gene_info_filename
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print('gene info file: ', gene_info
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, '\n============================================================')
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@ -102,57 +97,22 @@ print('Output file: ', outfile_or_kin
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#%% read files: preformatted using bash
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# or file: '...assoc.txt'
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# FIXME: call bash script from here
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or_df = pd.read_csv(gene_or, sep = '\t', header = 0, index_col = False) # 182, 12 (without filtering for missense muts, it was 212 i.e we 30 muts weren't missense)
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or_df.head()
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or_df.columns
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#%% snp_info file: master and gene specific ones
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#%% snp_info file: master and gene specific ones
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# gene info
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#info_df2 = pd.read_csv('nssnp_info_pnca.txt', sep = '\t', header = 0) #303, 10
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info_df2 = pd.read_csv(gene_info, sep = ',', header = 0) #303, 10
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info_df2 = pd.read_csv(gene_info, sep = '\t', header = 0) #447, 10
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#info_df2 = pd.read_csv(gene_info, sep = ',', header = 0) #447 10
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mis_mut_cover = (info_df2['chromosome_number'].nunique()/info_df2['chromosome_number'].count()) * 100
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print('*****RESULT*****'
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, '\nPercentage of missense mut in pncA:', mis_mut_cover
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, '\n*****RESULT*****') #65.7%
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# large file
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#info_df = pd.read_csv('snp_info.txt', sep = '\t', header = None) #12010
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info_df = pd.read_csv(snp_info, sep = '\t') #12010
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info_df.columns
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#info_df.columns = ['chromosome_number', 'ref_allele', 'alt_allele', 'snp_info'] #12009, 4
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info_df['chromosome_number'].nunique() #10257
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mut_cover = (info_df['chromosome_number'].nunique()/info_df['chromosome_number'].count()) * 100
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print('*****RESULT*****'
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,'\nPercentage of mutations in pncA:', mut_cover
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, '\n*****RESULT*****') #85.4%
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# extract unique chr position numbers
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genomic_pos = info_df['chromosome_number'].unique()
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genomic_pos_df = pd.DataFrame(genomic_pos, columns = ['chr_pos'])
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genomic_pos_df.dtypes
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genomic_pos_min = info_df['chromosome_number'].min()
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genomic_pos_max = info_df['chromosome_number'].max()
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# genomic coord for pnca coding region
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cds_len = (end_cds-start_cds) + 1
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pred_prot_len = (cds_len/3) - 1
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# mindblowing: difference b/w bitwise (&) and 'and'
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# DO NOT want &: is this bit set to '1' in both variables? Is this what you want?
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#if (genomic_pos_min <= start_cds) & (genomic_pos_max >= end_cds):
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print('*****RESULT*****'
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, '\nlength of coding region:', cds_len, 'bp'
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, '\npredicted protein length:', pred_prot_len, 'aa'
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, '\n*****RESULT*****')
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if genomic_pos_min <= start_cds and genomic_pos_max >= end_cds:
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print ('PASS: coding region for gene included in snp_info.txt')
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else:
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sys.exit('FAIL: coding region for gene not included in info file snp_info.txt')
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# v6: 61.07
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# v4: 65.7%
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#%% Extracting ref allele and alt allele as single letters
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# info_df has some of these params as more than a single letter, which means that
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# when you try to merge ONLY using chromosome_number, then it messes up... and is WRONG.
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@ -161,104 +121,101 @@ else:
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df_ncols = len(or_df.columns)
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print('Dim of df:',or_df.shape
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, '\nExtracting missense muts as single letters')
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#find_missense(or_df, 'ref_allele1', 'alt_allele0')
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# adds columns to df passed
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, '\nExtracting missense muts as single letters from: find_missense()')
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find_missense(or_df, ref_allele_column = 'ref_allele1', alt_allele_column = 'alt_allele0')
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print('Dim of revised df:', or_df.shape, ' after extraction of missense muts')
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# FIXME: import this from function
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ncols_from_func = 4
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if or_df.shape[1] == df_ncols + ncols_from_func:
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ncols_added_func = 4
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if or_df.shape[1] == df_ncols + ncols_added_func:
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print('PASS: Succesfuly extracted ref and alt alleles for missense muts')
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else:
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print('FAIL: No. of cols mismatch'
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,'\nOriginal length:', df_ncols
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, '\nExpected no. of cols:', df_ncols + ncols_from_func
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, '\nExpected no. of cols:', df_ncols + ncols_added_func
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, '\nGot:', or_df.shape[1]
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, '\nCheck hardcoded value of ncols_add?')
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if (or_df['tot_diff'] == 1).sum() == len(or_df) and (or_df['n_diff'] == 1).sum() == len(or_df) and or_df['n_diff'].equals(or_df['tot_diff']):
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print('PASS: missene muts correctly extracted from source')
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else:
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print('FAIL: n_diff and tot_diff differ, check source data')
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sys.exit()
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del(df_ncols, ncols_from_func)
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del(df_ncols, ncols_added_func)
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#%% TRY MERGE
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# check dtypes
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or_df.dtypes
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info_df.dtypes
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#or_df.info()
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#%% check dtypes before merging
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#or_df.dtypes
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or_df.info()
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# pandas documentation where it mentions: "Pandas uses the object dtype for storing strings"
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# check how many unique chr_num in info_df are in or_df
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genomic_pos_df['chr_pos'].isin(or_df['chromosome_number']).sum() #144
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#info_df2.dtypes
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info_df2.info()
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# check how many chr_num in or_df are in info_df: should be ALL of them
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or_df['chromosome_number'].isin(genomic_pos_df['chr_pos']).sum() #182
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#%% perform merge: or_df and snp_info
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print('Preparing dfs for merging... Finding common cols to merge')
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# sanity check 2
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if or_df['chromosome_number'].isin(genomic_pos_df['chr_pos']).sum() == len(or_df):
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print('PASS: all genomic locs in or_df have meta datain info.txt')
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# find common columns
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#merging_cols = ['chromosome_number', 'ref_allele', 'alt_allele']
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merging_cols = or_df.columns[or_df.columns.isin(info_df2.columns)].to_list()
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print('No. of common cols identified:', len(merging_cols)
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, '\nColumns to merge on:', merging_cols
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, '\nChecking dtypes in merging_cols...'
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, '\n=================================================')
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# make sure chromosome_number dtypes are consisent
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or_df[merging_cols].dtypes == info_df2[merging_cols].dtypes
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# info_df2 contains multiple chromosome number in the column, so it is not
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# possible to convert this to int. Therefore, converting to string in or_df column
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if not (or_df[merging_cols].dtypes == info_df2[merging_cols].dtypes).all():
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print('Data types not same, converting chromsome_number to str in or_df')
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or_df['chromosome_number'] = or_df['chromosome_number'].astype(str)
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print('Checking after converting dtype in or_df')
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if (or_df[merging_cols].dtypes == info_df2[merging_cols].dtypes).all():
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print('PASS: dfs ready to merge..')
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else:
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sys.exit('FAIL: some genomic locs or_df chr number DO NOT have meta data in snp_info.txt')
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#%% perform merge
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print('FAIL: unable to make dtypes consistent required for merging! Check dtypes')
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sys.exit()
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# %% sanity check and perform merge
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#my_join = 'inner'
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#my_join = 'outer'
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my_join = 'left'
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#my_join = 'right'
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merging_cols = ['chromosome_number', 'ref_allele', 'alt_allele']
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expected_cols = or_df.shape[1] + info_df2.shape[1] - len(merging_cols)
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print('Merging 2 dfs: or_df and info_df using join type:', my_join
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print('Merging 2 dfs: or_df and info_df'
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, '\nJoin type:', my_join
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, '\nColumns to merge on:', merging_cols
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, '\nExpected cols after merging:', expected_cols
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, '\n=================================================')
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#dfm1 = pd.merge(or_df, info_df, on ='chromosome_number', how = my_join, indicator = True) # not unique!
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dfm1 = pd.merge(or_df, info_df, on = merging_cols, how = my_join, indicator = True)
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dfm1['_merge'].value_counts()
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# count no. of missense mutations ONLY
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print('Expected no. of missense mutations:', dfm1.snp_info.str.count(r'(missense.*)').sum())
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# Merge with info_df2 has this has extra columns due to bash preformatting
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# These extra columns are just 'snp_info' column split on '|'
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print('Merging with info_df2 as it has,', len(set(info_df2.columns).difference(info_df.columns))
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, 'extra columns relevant for downstream analyses:\n\n'
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, set(info_df2.columns).difference(info_df.columns))
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dfm2 = pd.merge(or_df, info_df2, on = merging_cols, how = my_join, indicator = True)
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dfm2['_merge'].value_counts()
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expected_cols = expected_cols + 1 # due to indicator = T
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# count no. of nan
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print('No. of NA in dfm2:', dfm2['mut_type'].isna().sum())
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# count no. of nan in 'mut_type'. ideally should be 0
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if not dfm2['mut_type'].isna().sum() > 0:
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print('Good merging, no NA detected')
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else:
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print('OR detected without metadata'
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, '\nNo. of NA in dfm2:', dfm2['mut_type'].isna().sum()
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, '\nWriting these to output file to check later with jody!')
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dfm2_missing_info = dfm2[dfm2['mut_type'].isna()]
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missing_or_metadata = "or_kinship_missing_metadata.csv"
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outfile_missing_or_metadata = outdir + '/' + str(dfm2['mut_type'].isna().sum()) + '_' + missing_or_metadata
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print('\noutput file:', outfile_missing_or_metadata)
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dfm2_missing_info.to_csv(outfile_missing_or_metadata, index = False)
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print('Finsihed writing file'
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, '\nDim:', dfm2_missing_info.shape)
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# drop nan from dfm2_mis
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#PENDING Jody's reply
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# !!!!!!!!
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# drop nan from dfm2_mis as these are not useful
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print('Dropping NAs before further processing...')
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dfm2_mis = dfm2[dfm2['mut_type'].notnull()]
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#%% sanity check
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# count no. of missense muts
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#if len(dfm1) - dfm1.snp_info.str.count(r'(missense.*)').sum() == dfm2['mut_type'].isna().sum():
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if dfm2_mis.shape[0] == dfm1.snp_info.str.count(r'(missense.*)').sum():
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print('PASSED: numbers cross checked'
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, '\nTotal no. of missense mutations:', dfm1.snp_info.str.count(r'(missense.*)').sum()
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, '\nNo. of mutations falsely assumed to be missense:', len(dfm1) - dfm1.snp_info.str.count(r'(missense.*)').sum())
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else:
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print('FAIL: numbers mismatch'
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, '\Expected no. of rows:',dfm1.snp_info.str.count(r'(missense.*)').sum()
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, '\nGot:', dfm2_mis.shape[0]
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, '\nExpected no. of cols:', dfm1.shape[1] + len(set(info_df2.columns).difference(info_df.columns))-1)
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# two ways to filter to get only missense muts
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test = dfm1[dfm1['snp_info'].str.count('missense.*')>0]
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dfm1_mis = dfm1[dfm1['snp_info'].str.match('(missense.*)') == True]
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test.equals(dfm1_mis)
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if dfm1_mis[['chromosome_number', 'ref_allele', 'alt_allele']].equals(dfm2_mis[['chromosome_number', 'ref_allele', 'alt_allele']]):
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print('PASS: Further cross checks successful')
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else:
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sys.exit('FAIL: Second cross check unsuccessful!')
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del(test, dfm1_mis)
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# !!!!!!!!
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#%% extract mut info into three cols
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df_ncols = len(dfm2_mis.columns)
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@ -269,19 +226,20 @@ ncols_add = 0
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if not 'wild_type' in dfm2_mis.columns:
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print('Extracting and adding column: wild_type'
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, '\n===============================================================')
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dfm2_mis['wild_type'] = dfm2_mis['mut_info'].str.extract('(\w{1})>')
|
||||
dfm2_mis['wild_type'] = dfm2_mis['mut_info_f1'].str.extract('(\w{1})>')
|
||||
ncols_add+=1
|
||||
|
||||
if not 'position' in dfm2_mis.columns:
|
||||
print('Extracting and adding column: position'
|
||||
, '\n===============================================================')
|
||||
dfm2_mis['position'] = dfm2_mis['mut_info'].str.extract('(\d+)')
|
||||
dfm2_mis['position'] = dfm2_mis['mut_info_f1'].str.extract('(\d+)')
|
||||
#dfm2_mis['position'] = dfm2_mis[:,'mut_info_f1'].str.extract('(\d+)')
|
||||
ncols_add+=1
|
||||
|
||||
if not 'mutant_type' in dfm2_mis.columns:
|
||||
print('Extracting and adding column: mutant_type'
|
||||
, '\n================================================================')
|
||||
dfm2_mis['mutant_type'] = dfm2_mis['mut_info'].str.extract('>\d+(\w{1})')
|
||||
dfm2_mis['mutant_type'] = dfm2_mis['mut_info_f1'].str.extract('>\d+(\w{1})')
|
||||
ncols_add+=1
|
||||
|
||||
if not 'mutationinformation' in dfm2_mis.columns:
|
||||
|
|
@ -293,7 +251,7 @@ if not 'mutationinformation' in dfm2_mis.columns:
|
|||
print('No. of cols added:', ncols_add)
|
||||
|
||||
if len(dfm2_mis.columns) == df_ncols + ncols_add:
|
||||
print('PASS: mcsm style muts present in df'
|
||||
print('PASS: mcsm style muts added to df'
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print('FAIL: No. of cols mismatch'
|
||||
|
|
@ -349,7 +307,7 @@ del(df_ncols, ncols_add)
|
|||
#%%==============================!!!!!!!=======================================
|
||||
#3) drop some not required cols (including duplicate if you want)
|
||||
#3a) drop duplicate columns
|
||||
dfm2_mis2 = dfm2_mis.T.drop_duplicates().T #changes dtypes in cols, so not used
|
||||
dfm2_mis2 = dfm2_mis.T.drop_duplicates().T #changes dtypes in cols, only used for sanity check
|
||||
dup_cols = set(dfm2_mis.columns).difference(dfm2_mis2.columns)
|
||||
print('Total no of duplicate columns:', len(dup_cols)
|
||||
, '\nDuplicate columns identified:', dup_cols
|
||||
|
|
@ -359,7 +317,7 @@ print('Total no of duplicate columns:', len(dup_cols)
|
|||
#print('removing duplicate columns: kept one of the dup_cols i.e tot_diff')
|
||||
df_ncols = dfm2_mis.shape[1]
|
||||
|
||||
print('Removing duplicate columns'
|
||||
print('Removing', len(dup_cols), 'duplicate columns:', dup_cols
|
||||
, '\nOriginal dim:', dfm2_mis.shape)
|
||||
dfm2_mis.drop(list(dup_cols), axis = 1, inplace = True)
|
||||
|
||||
|
|
@ -379,7 +337,8 @@ else:
|
|||
del(df_ncols)
|
||||
|
||||
#3b) other not useful columns
|
||||
cols_to_drop = ['chromosome_text', 'n_diff', 'chr', 'symbol', '_merge' ]
|
||||
print('Dropping other redundant or unnecessary columns...')
|
||||
cols_to_drop = ['chromosome_text', 'n_diff', 'chr', '_merge' , 'mut_region' , 'reference_allele', 'alternate_allele']
|
||||
df_ncols = dfm2_mis.shape[1]
|
||||
dfm2_mis.drop(cols_to_drop, axis = 1, inplace = True)
|
||||
#dfm2_mis.rename(columns = {'ref_allele1': 'reference_allele'}, inplace = True)
|
||||
|
|
@ -403,21 +362,21 @@ del(df_ncols)
|
|||
print('Reordering', dfm2_mis.shape[1], 'columns'
|
||||
, '\n===============================================')
|
||||
|
||||
column_order = ['mutation',
|
||||
#dfm2_mis.columns
|
||||
|
||||
column_order = [#'mutation',
|
||||
'mutationinformation',
|
||||
'wild_type',
|
||||
'position',
|
||||
'mutant_type',
|
||||
'chr_num_allele',
|
||||
#'chr_num_allele',
|
||||
'ref_allele',
|
||||
'alt_allele',
|
||||
'mut_info',
|
||||
'mut_info_f1',
|
||||
'mut_info_f2',
|
||||
'mut_type',
|
||||
'gene_id',
|
||||
'gene_number',
|
||||
'mut_region',
|
||||
'reference_allele',
|
||||
'alternate_allele',
|
||||
'gene_name',
|
||||
'chromosome_number',
|
||||
#'afs
|
||||
'af_kin',
|
||||
|
|
@ -439,12 +398,14 @@ column_order = ['mutation',
|
|||
# 'n_diff',
|
||||
# 'tot_diff',
|
||||
'n_miss',
|
||||
'wt_3let',
|
||||
'mt_3let']
|
||||
#'wt_3let',
|
||||
#'mt_3let'
|
||||
]
|
||||
|
||||
if len(column_order) == dfm2_mis.shape[1]:
|
||||
if (len(column_order) == dfm2_mis.shape[1] and pd.DataFrame(column_order).isin(dfm2_mis.columns).all()).all():
|
||||
print('PASS: Column order generated for', len(column_order), 'columns'
|
||||
, '\nApplying column order to df...' )
|
||||
, '\nColumn names match to perform reordering'
|
||||
, '\nApplying column order to df...' )
|
||||
orkin_linked = dfm2_mis[column_order]
|
||||
else:
|
||||
print('FAIL: Mismatch in no. of cols to reorder'
|
||||
|
|
@ -459,12 +420,41 @@ if orkin_linked.shape == dfm2_mis.shape and set(orkin_linked.columns) == set(dfm
|
|||
else:
|
||||
sys.exit('FAIL: something went wrong when rearranging columns!')
|
||||
|
||||
|
||||
# converting position and chromosome number to numeric
|
||||
orkin_linked.dtypes
|
||||
#orkin_linked['chromosome_number'] = pd.to_numeric(orkin_linked['chromosome_number'])
|
||||
|
||||
orkin_linked[['chromosome_number', 'position']] = orkin_linked[['chromosome_number', 'position']].apply(pd.to_numeric)
|
||||
orkin_linked.dtypes
|
||||
|
||||
# write frequency of position counts
|
||||
orkin_pos = pd.DataFrame(orkin_linked['position'])
|
||||
z = orkin_pos['position'].value_counts()
|
||||
z1 = z.to_dict()
|
||||
orkin_pos['or_pos_count'] = orkin_pos['position'].map(z1)
|
||||
orkin_pos['or_pos_count'].value_counts()
|
||||
|
||||
orkin_linked['position']
|
||||
foo = orkin_linked['position'].value_counts()
|
||||
|
||||
# order df by position
|
||||
orkin_linked_o = orkin_linked.sort_values(by = ['position'])
|
||||
bar = orkin_linked_o['position'].value_counts()
|
||||
|
||||
if (foo == bar).all():
|
||||
print('PASS: df reorderd by position for output'
|
||||
, '\nWriting output file')
|
||||
else:
|
||||
print('FAIL: could not reorder by position')
|
||||
sys.exit()
|
||||
|
||||
#%% write file
|
||||
print('\n====================================================================='
|
||||
, '\nWriting output file:\n', outfile_or_kin
|
||||
, '\nNo. of rows:', len(dfm2_mis)
|
||||
, '\nNo. of cols:', len(dfm2_mis.columns))
|
||||
orkin_linked.to_csv(outfile_or_kin, index = False)
|
||||
orkin_linked_o.to_csv(outfile_or_kin, index = False)
|
||||
|
||||
#%% diff b/w allele0 and 1: or_df
|
||||
#https://stackoverflow.com/questions/40348541/pandas-diff-with-string
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue