added sections and slotted relevant bits from data_extraction to v2
This commit is contained in:
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e6faf80c20
commit
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2 changed files with 446 additions and 52 deletions
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@ -1210,7 +1210,7 @@ print('Finished writing:', outfile_mcsmsnps
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, '\n=============================================================')
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del(out_filename_mcsmsnps)
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#%%# write frequency of position counts
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#%% write frequency of position counts
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metadata_pos = pd.DataFrame(gene_LF1['position'])
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z = gene_LF1['position'].value_counts()
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z1 = z.to_dict()
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@ -1219,8 +1219,6 @@ metadata_pos['meta_pos_count'].value_counts()
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metadata_pos.sort_values(by = ['meta_pos_count'], ascending = False, inplace = True)
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# Write file: gene_metadata (i.e gene_LF1)
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# where each row has UNIQUE mutations NOT unique sample ids
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out_filename_metadata_poscounts = gene.lower() + '_metadata_poscounts.csv'
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outfile_metadata_poscounts = outdir + '/' + out_filename_metadata_poscounts
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print('\n----------------------------------'
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@ -1236,7 +1234,6 @@ print('Finished writing:', outfile_metadata_poscounts
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, '\n=============================================================')
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del(out_filename_metadata_poscounts)
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#%% Write file: gene_metadata (i.e gene_LF1)
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# where each row has UNIQUE mutations NOT unique sample ids
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#out_filename_metadata = gene.lower() + '_metadata_raw.csv' #! rationale?
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@ -905,14 +905,16 @@ else:
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#%% Ambiguous muts: revised annotation for mutation_info
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ambiguous_muts_df = gene_LF1[gene_LF1['mutation'].isin(common_muts)]
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ambiguous_muts_value_counts = ambiguous_muts_df.groupby('mutation')['mutation_info'].value_counts()
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ambiguous_muts_value_counts
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bar = gene_LF1.copy()
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bar.equals(gene_LF1)
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gene_LF1_orig = gene_LF1.copy()
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gene_LF1_orig.equals(gene_LF1)
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# copy the old column for checking
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gene_LF1['mutation_info_orig'] = gene_LF1['mutation_info']
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gene_LF1['mutation_info_orig'].value_counts()
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gene_LF1['mutation_info'].value_counts()
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#=====================================
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# Now create a new df that will have:
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@ -924,42 +926,52 @@ gene_LF1_orig = gene_LF1.copy()
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# label is chosen that corresponds to the max of value counts
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#=====================================
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ambig_muts_rev_df = pd.DataFrame()
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changes_val = []
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changes_dict = {}
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for i in common_muts:
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#print(i)
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temp_df = gene_LF1[gene_LF1['mutation'] == i][['mutation', 'mutation_info']]
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temp_df = gene_LF1[gene_LF1['mutation'] == i][['mutation', 'mutation_info_orig']]
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# DANGER: ASSUMES TWO STATES ONLY and that value_counts sorts by descending
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max_info_table = gene_LF1[gene_LF1['mutation'] == i][['mutation', 'mutation_info']].value_counts()
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max_info_table = gene_LF1[gene_LF1['mutation'] == i][['mutation', 'mutation_info_orig']].value_counts()
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revised_label = max_info_table[[0]].index[0][1] # max value_count
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old_label = max_info_table[[1]].index[0][1] # min value_count
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print('\nAmbiguous mutation labels...'
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, '\nSetting mutation_info for', i, 'to', revised_label)
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temp_df['mutation_info_REV'] = np.where( (temp_df['mutation_info'] == old_label)
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temp_df['mutation_info_REV'] = np.where( (temp_df['mutation_info_orig'] == old_label)
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, revised_label
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, temp_df['mutation_info'])
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, temp_df['mutation_info_orig'])
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ambig_muts_rev_df = pd.concat([ambig_muts_rev_df,temp_df])
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f = max_info_table[[1]]
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old_label_count = f[[0]][0]
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changes_val.append(old_label_count)
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cc_dict = f.to_dict()
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changes_dict.update(cc_dict)
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ambig_muts_rev_df['mutation_info'].value_counts()
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ambig_muts_rev_df['mutation_info_REV'].value_counts()
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ambig_muts_rev_df['mutation_info_orig'].value_counts()
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changes_val
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changes_total = sum(changes_val)
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changes_dict
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# TODO: Add sanity check to make sure you can add value_count checks
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#=================
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# Merge ambig muts
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# with gene_LF1
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#===================
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bar = gene_LF1.copy()
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bar['mutation_info_old'] = bar['mutation_info']
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ambig_muts_rev_df.index
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bar.loc[ambig_muts_rev_df.index, 'mutation_info'] = ambig_muts_rev_df['mutation_info_REV']
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bar['mutation_info_old'].value_counts()
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bar['mutation_info'].value_counts()
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foo = bar.iloc[ambig_muts_rev_df.index]
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foo[['mutation', 'mutation_info', 'mutation_info_old']]
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# CHECK if there are still any ambiguous muts
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muts_split_rev = list(bar.groupby('mutation_info'))
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gene_LF1.loc[ambig_muts_rev_df.index, 'mutation_info'] = ambig_muts_rev_df['mutation_info_REV']
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gene_LF1['mutation_info_orig'].value_counts()
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gene_LF1['mutation_info'].value_counts()
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foo = gene_LF1.iloc[ambig_muts_rev_df.index]
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foo[['mutation', 'mutation_info', 'mutation_info_orig']]
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# Sanity check1: if there are still any ambiguous muts
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muts_split_rev = list(gene_LF1.groupby('mutation_info'))
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dr_muts_rev = muts_split_rev[0][1].mutation
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other_muts_rev = muts_split_rev[1][1].mutation
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print('splitting muts by mut_info:', muts_split_rev)
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@ -972,34 +984,8 @@ else:
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print('\nAmbiguous muts corrected. Quitting!')
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sys.exit()
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#%% ROUND THE HOUSES: DELETE
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foo = ambig_muts_rev_df[['mutation', 'mutation_info_REV']]
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fooU = foo.drop_duplicates()
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#https://cmdlinetips.com/2021/04/convert-two-column-values-from-pandas-dataframe-to-a-dictionary/
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fooD = fooU.set_index('mutation').to_dict()['mutation_info_REV']
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fooD
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#https://stackoverflow.com/questions/45334020/pandas-map-column-data-based-on-value-from-another-column-using-if-to-determine
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#df['New_State_Name'] = np.where(df['Name']=='Person1',df['State'].map(state_map),df['State'].map(state_map2))
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bar['NEW'] = bar['mutation_info_old']
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bar['NEW'].value_counts()
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bar['mutation_info_old'].value_counts()
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bar['NEW'].value_counts()
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bar2 = bar[['mutation', 'mutation_info', 'NEW']]
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bar2.isnull().sum()
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bar['NEW'].value_counts()
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bar['mutation_info'].value_counts()
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bar.loc[bar['mutation'].isin(fooD.keys()), 'NEW'] = bar['mutation_info'].map(fooD)
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bar['NEW'] = bar['NEW'].map(fooD).fillna(bar['NEW'])
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bar['NEW2'].value_counts()
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bar['NEW'].value_counts()
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#%% OUTFILE 3, write file: ambiguous muts and ambiguous mut counts
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#print(outdir)
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===================
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#===================
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# ambiguous muts
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#=======================
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#dr_muts.to_csv('dr_muts.csv', header = True)
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@ -1034,7 +1020,7 @@ print('\n----------------------------------'
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ambiguous_muts_value_counts.to_csv(outfile_ambig_mut_counts, index = True)
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#%% clear variables
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# clear variables
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del(id_dr, id_other
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#, meta_data
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, meta_gene_dr, meta_gene_other
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@ -1043,9 +1029,420 @@ del(id_dr, id_other
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del(c1, c2, col_to_split1, col_to_split2, comp_gene_samples, dr_WF0, dr_df, dr_muts_df, dr_gene_WF0, dr_gene_count, expected_gene_samples, other_gene_WF1)
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#%% Splitting mutation column to get mCSM style muts i.e. mutationinformation column
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#%% Add column: Mutationinformation
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# splitting mutation column to get mCSM style muts
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#=======================
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# Formatting df: read aa dict and pull relevant info
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#=======================
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print('Now some more formatting:'
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, '\nRead aa dict and pull relevant info'
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, '\nFormat mutations:'
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, '\nsplit mutation into mCSM style muts: '
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, '\nFormatting mutation in mCSM style format: {WT}<POS>{MUT}'
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, '\nAssign aa properties: adding 2 cols at a time for each prop'
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, '\n===================================================================')
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# BEWARE hardcoding : only works as we are adding aa prop once for wt and once for mut
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# in each lookup cycle
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ncol_mutf_add = 3 # mut split into 3 cols
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ncol_aa_add = 2 # 2 aa prop add (wt & mut) in each mapping
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#===========
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# Split 'mutation' column into three: wild_type, position and
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# mutant_type separately. Then map three letter code to one using
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# reference_dict imported at the beginning.
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# After importing, convert to mutation to lowercase for compatibility with dict
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#===========
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gene_LF1['mutation'] = gene_LF1.loc[:, 'mutation'].str.lower()
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print('wt regex being used:', wt_regex
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, '\nmut regex being used:', mut_regex
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, '\nposition regex being used:', pos_regex)
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mylen0 = len(gene_LF1.columns)
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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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print('Adding', ncol_mutf_add, 'more cols:\n')
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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_regex).squeeze()converts to a series that map works on
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wt = gene_LF1['mutation'].str.extract(wt_regex).squeeze()
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gene_LF1['wild_type'] = wt.map(lookup_dict)
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#mut = gene_LF1['mutation'].str.extract('\d+(\w{3})$').squeeze()
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mut = gene_LF1['mutation'].str.extract(mut_regex).squeeze()
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gene_LF1['mutant_type'] = mut.map(lookup_dict)
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# extract position info from mutation column separetly using string match
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#gene_LF1['position'] = gene_LF1['mutation'].str.extract(r'(\d+)')
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gene_LF1['position'] = gene_LF1['mutation'].str.extract(pos_regex)
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mylen1 = len(gene_LF1.columns)
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# sanity checks
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print('checking if 3-letter wt&mut residue extraction worked correctly')
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if wt.isna().sum() & mut.isna().sum() == 0:
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print('PASS: 3-letter wt&mut residue extraction worked correctly:'
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, '\nNo NAs detected:'
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, '\nwild-type\n', wt
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, '\nmutant-type\n', mut
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, '\ndim of df:', gene_LF1.shape)
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else:
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print('FAIL: 3-letter wt&mut residue extraction failed'
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, '\nNo NAs detected:'
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, '\nwild-type\n', wt
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, '\nmutant-type\n', mut
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, '\ndim of df:', gene_LF1.shape)
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if mylen1 == mylen0 + ncol_mutf_add:
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print('PASS: successfully added', ncol_mutf_add, 'cols'
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, '\nold length:', mylen0
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, '\nnew len:', mylen1)
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else:
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print('FAIL: failed to add cols:'
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, '\nold length:', mylen0
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, '\nnew len:', mylen1)
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# clear variables
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del(k, v, wt, mut, lookup_dict)
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########
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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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#########
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gene_LF1['mutationinformation'] = gene_LF1['wild_type'] + gene_LF1.position.map(str) + gene_LF1['mutant_type']
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print('Created column: mutationinformation'
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, '\n=====================================================================\n'
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, gene_LF1.mutationinformation.head(10))
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#order by position for convenience
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gene_LF1.dtypes
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# converting position to numeric
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gene_LF1['position'] = pd.to_numeric(gene_LF1['position'])
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# sort by position inplace
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foo = gene_LF1['position'].value_counts()
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gene_LF1.sort_values(by = ['position'], inplace = True)
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bar = gene_LF1['position'].value_counts()
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if (foo == bar).all():
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print('PASS: df ordered by position')
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print(gene_LF1['position'].head())
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else:
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print('FAIL: df could not be ordered. Check source')
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sys.exit()
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#%% OUTFILE 4, write file mCSM style muts
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snps_only = pd.DataFrame(gene_LF1['mutationinformation'].unique())
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snps_only.head()
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# assign column name
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snps_only.columns = ['mutationinformation']
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# count how many positions this corresponds to
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pos_only = pd.DataFrame(gene_LF1['position'].unique())
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print('Checking NA in snps...')# should be 0
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if snps_only.mutationinformation.isna().sum() == 0:
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print ('PASS: NO NAs/missing entries for SNPs'
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, '\n===============================================================')
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else:
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sys.exit('FAIL: SNP has NA, Possible mapping issues from dict?')
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# write file: mCSM muts
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out_filename_mcsmsnps = gene.lower() + '_mcsm_formatted_snps.csv'
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outfile_mcsmsnps = outdir + '/' + out_filename_mcsmsnps
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print('\n----------------------------------'
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, '\nWriting file: mCSM style muts'
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, '\n----------------------------------'
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, '\nFile:', outfile_mcsmsnps
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, '\nmutation format (SNP): {WT}<POS>{MUT}'
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, '\nNo. of distinct muts:', len(snps_only)
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, '\nNo. of distinct positions:', len(pos_only)
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, '\n=============================================================')
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snps_only.to_csv(outfile_mcsmsnps, header = False, index = False)
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print('Finished writing:', outfile_mcsmsnps
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, '\nNo. of rows:', len(snps_only)
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, '\nNo. of cols:', len(snps_only.columns)
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, '\n=============================================================')
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del(out_filename_mcsmsnps)
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#%% OUTFILE 5, write file frequency of position counts
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metadata_pos = pd.DataFrame(gene_LF1['position'])
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z = gene_LF1['position'].value_counts()
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z1 = z.to_dict()
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metadata_pos['meta_pos_count'] = metadata_pos['position'].map(z1)
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metadata_pos['meta_pos_count'].value_counts()
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metadata_pos.sort_values(by = ['meta_pos_count'], ascending = False, inplace = True)
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out_filename_metadata_poscounts = gene.lower() + '_metadata_poscounts.csv'
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outfile_metadata_poscounts = outdir + '/' + out_filename_metadata_poscounts
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print('\n----------------------------------'
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, '\nWriting file: Metadata poscounts'
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, '\n----------------------------------'
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, '\nFile:', outfile_metadata_poscounts
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, '\n============================================================')
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metadata_pos.to_csv(outfile_metadata_poscounts, header = True, index = False)
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print('Finished writing:', outfile_metadata_poscounts
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, '\nNo. of rows:', len(metadata_pos)
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, '\nNo. of cols:', len(metadata_pos.columns)
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, '\n=============================================================')
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del(out_filename_metadata_poscounts)
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#%% OUTFILE 6, write file <gene>_metadata
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# gene_LF1, where each row has UNIQUE mutations NOT unique sample ids
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out_filename_metadata = gene.lower() + '_metadata.csv'
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outfile_metadata = outdir + '/' + out_filename_metadata
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print('\n----------------------------------'
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, '\nWriting file: LF formatted data'
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, '\n----------------------------------'
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, '\nFile:', outfile_metadata
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, '\n============================================================')
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gene_LF1.to_csv(outfile_metadata, header = True, index = False)
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print('Finished writing:', outfile_metadata
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, '\nNo. of rows:', len(gene_LF1)
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, '\nNo. of cols:', len(gene_LF1.columns)
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, '\n=============================================================')
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del(out_filename_metadata)
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#%% OUTFILE 7, write file MSA plots
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# mCSM style muts but with repetitions
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# !!! THINK !!!: Implications if mut is coming from the same sample, etc.
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all_muts_msa = pd.DataFrame(gene_LF1['mutationinformation'])
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all_muts_msa.head()
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# assign column name
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all_muts_msa.columns = ['mutationinformation']
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# make sure it is string
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all_muts_msa.columns.dtype
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# sort the column
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all_muts_msa_sorted = all_muts_msa.sort_values(by = 'mutationinformation')
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# create an extra column with protein name
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#all_muts_msa_sorted = all_muts_msa_sorted.assign(fasta_name = '3PL1')
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#all_muts_msa_sorted.head()
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# rearrange columns so the fasta name is the first column (required for mutate.script)
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#all_muts_msa_sorted = all_muts_msa_sorted[['fasta_name', 'mutationinformation']]
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all_muts_msa_sorted.head()
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print('Checking NA in snps...')# should be 0
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if all_muts_msa.mutationinformation.isna().sum() == 0:
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print ('PASS: NO NAs/missing entries for SNPs'
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, '\n===============================================================')
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else:
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sys.exit('FAIL: SNP has NA, Possible mapping issues from dict?')
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out_filename_msa = gene.lower() +'_all_muts_msa.csv'
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outfile_msa = outdir + '/' + out_filename_msa
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print('\n----------------------------------'
|
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, '\nWriting file: mCSM style muts for msa'
|
||||
, '\n----------------------------------'
|
||||
, '\nFile:', outfile_msa
|
||||
, '\nmutation format (SNP): {WT}<POS>{MUT}'
|
||||
, '\nNo.of lines of msa:', len(all_muts_msa))
|
||||
|
||||
all_muts_msa_sorted.to_csv(outfile_msa, header = False, index = False)
|
||||
|
||||
print('Finished writing:', outfile_msa
|
||||
, '\nNo. of rows:', len(all_muts_msa)
|
||||
, '\nNo. of cols:', len(all_muts_msa.columns)
|
||||
, '\n=============================================================')
|
||||
|
||||
del(out_filename_msa)
|
||||
#%% OUTFILE 8, write file mutational position with count
|
||||
# count how many positions this corresponds to
|
||||
pos_only = pd.DataFrame(gene_LF1['position'].unique())
|
||||
# assign column name
|
||||
pos_only.columns = ['position']
|
||||
# make sure dtype of column position is int or numeric and not string
|
||||
pos_only.position.dtype
|
||||
pos_only['position'] = pos_only['position'].astype(int)
|
||||
pos_only.position.dtype
|
||||
|
||||
# sort by position value
|
||||
pos_only_sorted = pos_only.sort_values(by = 'position', ascending = True)
|
||||
|
||||
out_filename_pos = gene.lower() + '_mutational_positons.csv'
|
||||
outfile_pos = outdir + '/' + out_filename_pos
|
||||
|
||||
print('\n----------------------------------'
|
||||
, '\nWriting file: mutational positions'
|
||||
, '\n----------------------------------'
|
||||
, '\nFile:', outfile_pos
|
||||
, '\nNo. of distinct positions:', len(pos_only_sorted)
|
||||
, '\n=============================================================')
|
||||
|
||||
pos_only_sorted.to_csv(outfile_pos, header = True, index = False)
|
||||
|
||||
print('Finished writing:', outfile_pos
|
||||
, '\nNo. of rows:', len(pos_only_sorted)
|
||||
, '\nNo. of cols:', len(pos_only_sorted.columns)
|
||||
, '\n============================================================='
|
||||
, '\n\n\n')
|
||||
|
||||
del(out_filename_pos)
|
||||
#%% Add column: aa property_water
|
||||
#=========
|
||||
# iterate through the dict, create a lookup dict that i.e
|
||||
# lookup_dict = {three_letter_code: aa_prop_water}
|
||||
# Do this for both wild_type and mutant as above.
|
||||
#=========
|
||||
print('Adding', ncol_aa_add, 'more cols:\n')
|
||||
|
||||
# initialise a sub dict that is lookup dict for three letter code to aa prop
|
||||
# adding two more cols
|
||||
lookup_dict = dict()
|
||||
for k, v in my_aa_dict.items():
|
||||
lookup_dict[k] = v['aa_prop_water']
|
||||
#print(lookup_dict)
|
||||
wt = gene_LF1['mutation'].str.extract(wt_regex).squeeze()
|
||||
gene_LF1['wt_prop_water'] = wt.map(lookup_dict)
|
||||
#mut = gene_LF1['mutation'].str.extract('\d+(\w{3})$').squeeze()
|
||||
mut = gene_LF1['mutation'].str.extract(mut_regex).squeeze()
|
||||
gene_LF1['mut_prop_water'] = mut.map(lookup_dict)
|
||||
|
||||
mylen2 = len(gene_LF1.columns)
|
||||
|
||||
# sanity checks
|
||||
print('checking if 3-letter wt&mut residue extraction worked correctly')
|
||||
if wt.isna().sum() & mut.isna().sum() == 0:
|
||||
print('PASS: 3-letter wt&mut residue extraction worked correctly:'
|
||||
, '\nNo NAs detected:'
|
||||
, '\nwild-type\n', wt
|
||||
, '\nmutant-type\n', mut
|
||||
, '\ndim of df:', gene_LF1.shape)
|
||||
else:
|
||||
print('FAIL: 3-letter wt&mut residue extraction failed'
|
||||
, '\nNo NAs detected:'
|
||||
, '\nwild-type\n', wt
|
||||
, '\nmutant-type\n', mut
|
||||
, '\ndim of df:', gene_LF1.shape)
|
||||
|
||||
if mylen2 == mylen1 + ncol_aa_add:
|
||||
print('PASS: successfully added', ncol_aa_add, 'cols'
|
||||
, '\nold length:', mylen1
|
||||
, '\nnew len:', mylen2)
|
||||
else:
|
||||
print('FAIL: failed to add cols:'
|
||||
, '\nold length:', mylen1
|
||||
, '\nnew len:', mylen2)
|
||||
|
||||
# clear variables
|
||||
del(k, v, wt, mut, lookup_dict)
|
||||
#%% Add column: aa_prop_polarity
|
||||
#========
|
||||
# iterate through the dict, create a lookup dict that i.e
|
||||
# lookup_dict = {three_letter_code: aa_prop_polarity}
|
||||
# Do this for both wild_type and mutant as above.
|
||||
#=========
|
||||
print('Adding', ncol_aa_add, 'more cols:\n')
|
||||
|
||||
# initialise a sub dict that is lookup dict for three letter code to aa prop
|
||||
# adding two more cols
|
||||
lookup_dict = dict()
|
||||
for k, v in my_aa_dict.items():
|
||||
lookup_dict[k] = v['aa_prop_polarity']
|
||||
#print(lookup_dict)
|
||||
wt = gene_LF1['mutation'].str.extract(wt_regex).squeeze()
|
||||
gene_LF1['wt_prop_polarity'] = wt.map(lookup_dict)
|
||||
#mut = gene_LF1['mutation'].str.extract(r'\d+(\w{3})$').squeeze()
|
||||
mut = gene_LF1['mutation'].str.extract(mut_regex).squeeze()
|
||||
gene_LF1['mut_prop_polarity'] = mut.map(lookup_dict)
|
||||
|
||||
mylen3 = len(gene_LF1.columns)
|
||||
|
||||
# sanity checks
|
||||
print('checking if 3-letter wt&mut residue extraction worked correctly')
|
||||
if wt.isna().sum() & mut.isna().sum() == 0:
|
||||
print('PASS: 3-letter wt&mut residue extraction worked correctly:'
|
||||
, '\nNo NAs detected:'
|
||||
, '\nwild-type\n', wt
|
||||
, '\nmutant-type\n', mut
|
||||
, '\ndim of df:', gene_LF1.shape)
|
||||
else:
|
||||
print('FAIL: 3-letter wt&mut residue extraction failed'
|
||||
, '\nNo NAs detected:'
|
||||
, '\nwild-type\n', wt
|
||||
, '\nmutant-type\n', mut
|
||||
, '\ndim of df:', gene_LF1.shape)
|
||||
|
||||
if mylen3 == mylen2 + ncol_aa_add:
|
||||
print('PASS: successfully added', ncol_aa_add, 'cols'
|
||||
, '\nold length:', mylen1
|
||||
, '\nnew len:', mylen2)
|
||||
else:
|
||||
print('FAIL: failed to add cols:'
|
||||
, '\nold length:', mylen1
|
||||
, '\nnew len:', mylen2)
|
||||
|
||||
# clear variables
|
||||
del(k, v, wt, mut, lookup_dict)
|
||||
#%% Add column: aa_calcprop
|
||||
#========
|
||||
# iterate through the dict, create a lookup dict that i.e
|
||||
# lookup_dict = {three_letter_code: aa_calcprop}
|
||||
# Do this for both wild_type and mutant as above.
|
||||
#=========
|
||||
print('Adding', ncol_aa_add, 'more cols:\n')
|
||||
|
||||
lookup_dict = dict()
|
||||
for k, v in my_aa_dict.items():
|
||||
lookup_dict[k] = v['aa_calcprop']
|
||||
#print(lookup_dict)
|
||||
wt = gene_LF1['mutation'].str.extract(wt_regex).squeeze()
|
||||
gene_LF1['wt_calcprop'] = wt.map(lookup_dict)
|
||||
mut = gene_LF1['mutation'].str.extract(mut_regex).squeeze()
|
||||
gene_LF1['mut_calcprop'] = mut.map(lookup_dict)
|
||||
|
||||
mylen4 = len(gene_LF1.columns)
|
||||
|
||||
# sanity checks
|
||||
print('checking if 3-letter wt&mut residue extraction worked correctly')
|
||||
if wt.isna().sum() & mut.isna().sum() == 0:
|
||||
print('PASS: 3-letter wt&mut residue extraction worked correctly:'
|
||||
, '\nNo NAs detected:'
|
||||
, '\nwild-type\n', wt
|
||||
, '\nmutant-type\n', mut
|
||||
, '\ndim of df:', gene_LF1.shape)
|
||||
else:
|
||||
print('FAIL: 3-letter wt&mut residue extraction failed'
|
||||
, '\nNo NAs detected:'
|
||||
, '\nwild-type\n', wt
|
||||
, '\nmutant-type\n', mut
|
||||
, '\ndim of df:', gene_LF1.shape)
|
||||
|
||||
if mylen4 == mylen3 + ncol_aa_add:
|
||||
print('PASS: successfully added', ncol_aa_add, 'cols'
|
||||
, '\nold length:', mylen3
|
||||
, '\nnew len:', mylen4)
|
||||
else:
|
||||
print('FAIL: failed to add cols:'
|
||||
, '\nold length:', mylen3
|
||||
, '\nnew len:', mylen4)
|
||||
|
||||
# clear variables
|
||||
del(k, v, wt, mut, lookup_dict)
|
||||
|
||||
#%% NEW TODO: map mutationinformation
|
||||
|
||||
#%% NEW: mappings
|
||||
#=======================
|
||||
# column name: <drug>
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue