phewwww! finally resolved counts for ambiguous muts
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1 changed files with 504 additions and 46 deletions
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@ -53,6 +53,9 @@ import numpy as np
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import argparse
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from statistics import mean, median, mode
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from statistics import multimode
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# adding values for common keys
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import itertools
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import collections
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#=======================================================================
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#%% dir and local imports
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homedir = os.path.expanduser('~')
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@ -370,7 +373,7 @@ all(meta_gene_all.index.values == meta_gene_all['index_orig'].values)
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all(meta_gene_all.index.values == meta_gene_all['index_orig_copy'].values)
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##############################################################################
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#%% Important sanity checks: Dr muts column for tidy split(), nsSNPs, etc.
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# Split based on semi colon dr_muts_col and other_muts_col
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# Split based on semi colon dr_muts_col
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search = ";"
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# count of occurrence of ";" in dr_muts_col: No.of semicolons + 1 is no. of rows created * occurence
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@ -387,13 +390,7 @@ expected_drC
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# count no. of nsSNPs and extract those nsSNPs
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count_df_dr['dr_geneSNP_count'] = meta_gene_dr.loc[:, dr_muts_col].str.count(nssnp_match, re.I)
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dr_gene_count1 = count_df_dr['dr_geneSNP_count'].sum()
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# DEL: need to count by find_all
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# dr_snps_df = meta_gene_dr.loc[:, dr_muts_col].str.extract(nssnp_match_captureG, re.I)
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# dr_snps_df.columns = [dr_muts_col]
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# dr_snps_countU = dr_snps_df[dr_muts_col].nunique()
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# dr_snps_countU
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# dr_snps_count_dict = dr_snps_df[dr_muts_col].value_counts().to_dict()
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# dr_snps_count_dict
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###############################################################################
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# This is to find out how many samples have 1 and more than 1 mutation,so you
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# can use it to check if your data extraction process for dr_muts
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@ -482,7 +479,7 @@ else:
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# Extract only the samples/rows with nssnp_match
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#dr_gene_WF0 = dr_WF0.loc[dr_WF0[dr_muts_col].str.contains(gene_match)]
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dr_gene_WF0 = dr_WF0.loc[dr_WF0[dr_muts_col].str.contains(nssnp_match, regex = True, case = False)]
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dr_gene_WF0_v2 = dr_WF0.loc[dr_WF0[dr_muts_col].str.contains(nssnp_match_captureG, regex = True, case = False)]
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#dr_gene_WF0_v2 = dr_WF0.loc[dr_WF0[dr_muts_col].str.contains(nssnp_match_captureG, regex = True, case = False)]
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print('Lengths after tidy split and extracting', nssnp_match, 'muts:'
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, '\nOld length:' , len(meta_gene_dr)
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@ -491,33 +488,6 @@ print('Lengths after tidy split and extracting', nssnp_match, 'muts:'
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, '\nExpected len:', dr_gene_count
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, '\n=============================================================')
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if len(dr_gene_WF0) == dr_gene_count:
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print('PASS: length matches expected length'
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, '\n===============================================================')
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else:
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sys.exit('FAIL: lengths mismatch')
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# count the freq of 'dr_muts' samples
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dr_muts_df = dr_gene_WF0 [['id', dr_muts_col]]
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print('Dim of dr_muts_df:', dr_muts_df.shape)
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# add freq column
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dr_muts_df['dr_sample_freq'] = dr_muts_df.groupby('id')['id'].transform('count')
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#dr_muts_df['dr_sample_freq'] = dr_muts_df.loc[dr_muts_df.groupby('id')].transform('count')
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print('Revised dim of dr_muts_df:', dr_muts_df.shape)
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c1 = dr_muts_df.dr_sample_freq.value_counts()
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print('Counting no. of sample frequency:\n', c1
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, '\n===================================================================')
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# sanity check: length of gene samples
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if len(dr_gene_WF0) == c1.sum():
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print('PASS: WF data has expected length'
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, '\nLength of dr_gene WFO:', c1.sum()
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, '\n===============================================================')
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else:
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sys.exit('FAIL: length mismatch!')
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# Important: Assign 'column name' on which split was performed as an extra column
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# This is so you can identify if mutations are dr_type or other in the final df
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dr_df = dr_gene_WF0.assign(mutation_info = dr_muts_col)
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@ -525,9 +495,13 @@ print('Dim of dr_df:', dr_df.shape
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, '\n=============================================================='
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, '\nEnd of tidy split() on dr_muts, and added an extra column relecting mut_category'
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, '\n===============================================================')
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if other_muts_col in dr_df.columns:
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print('Dropping:', other_muts_col, 'from WF gene dr_df')
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dr_df = dr_df.drop([other_muts_col], axis = 1)
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########################################################################
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#%% Important sanity checks: other muts column for tidy split(), nsSNPs, etc.
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# Split based on semi colon on other_muts_col
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# count of occurrence of ";" in other_muts_col: No.of semicolons + 1 is no. of rows created * occurence
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count_df_other = meta_gene_other[['id', other_muts_col]]
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count_df_other['other_semicolon_count'] = meta_gene_other.loc[:, other_muts_col].str.count(search, re.I)
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@ -540,13 +514,497 @@ expected_otherC
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# count no. of nsSNPs and extract those nsSNPs
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count_df_other['other_geneSNP_count'] = meta_gene_other.loc[:, other_muts_col].str.count(nssnp_match, re.I)
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other_gene_count1 = count_df_other['other_geneSNP_count'].sum()
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# DEL: need to count by find_all
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# other_snps_df = meta_gene_other.loc[:, other_muts_col].str.extract(nssnp_match_captureG, re.I)
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# other_snps_df
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# other_snps_df.columns = [other_muts_col]
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# other_snps_countU = other_snps_df[other_muts_col].nunique()
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# other_snps_countU
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# other_snps_count_dict = other_snps_df[other_muts_col].value_counts().to_dict()
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# other_snps_count_dict
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###############################################################################
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# This is to find out how many samples have 1 and more than 1 mutation,so you
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# can use it to check if your data extraction process for dr_muts
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# and other_muts has worked correctly AND also to check the dim of the
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# final formatted data.
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# This will have: unique COMBINATION of sample id and <gene_match> mutations
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#========
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# Second: counting <gene_match> mutations in other_muts_col column
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#========
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print('Now counting WT &', nssnp_match, 'muts within the column:', other_muts_col)
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# drop na and extract a clean df
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clean_df = meta_data.dropna(subset=[other_muts_col])
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# sanity check: count na
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na_count = meta_data[other_muts_col].isna().sum()
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if len(clean_df) == (total_samples - na_count):
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print('PASS: clean_df extracted: length is', len(clean_df)
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, '\nNo.of NAs =', na_count, '/', total_samples
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, '\n=========================================================')
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else:
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sys.exit('FAIL: Could not drop NAs')
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other_gene_count = 0
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wt_other = 0
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id_other = []
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id2_other = []
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other_gene_mutsL = []
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for i, id in enumerate(clean_df.id):
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#print (i, id)
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#id_other.append(id)
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#count_gene_other = clean_df[other_muts_col].iloc[i].count(gene_match) # can include stop muts
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count_gene_other = len(re.findall(nssnp_match, clean_df[other_muts_col].iloc[i], re.IGNORECASE))
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gene_otherL = re.findall(nssnp_match, clean_df[other_muts_col].iloc[i], re.IGNORECASE)
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#print(count_gene_other)
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if count_gene_other > 0:
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id_other.append(id)
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if count_gene_other > 1:
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id2_other.append(id)
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#print(id, count_gene_other)
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other_gene_count = other_gene_count + count_gene_other
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other_gene_mutsL = other_gene_mutsL + gene_otherL
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count_wt = clean_df[other_muts_col].iloc[i].count('WT')
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wt_other = wt_other + count_wt
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print('RESULTS:')
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print('Total WT in other_muts_col:', wt_other)
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print('Total matches of', gene, 'SNP matches in', other_muts_col, ':', other_gene_count)
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print('Total samples with > 1', gene, 'nsSNPs in other_muts_col:', len(id2_other) )
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print('Total matches of UNIQUE', gene, 'SNP matches in', other_muts_col, ':', len(set(other_gene_mutsL)))
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print('=================================================================')
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if other_gene_count == other_gene_count1:
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print('\nPass: other gene SNP count match')
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else:
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sys.exit('\nFAIL: other gene SNP count MISmatch')
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del(clean_df, na_count, i, id, wt_other, id2_other, count_gene_other, count_wt )
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#%% tidy_split(): other_muts_col
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#=========
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# DF2: other_muts_col
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# and remove leading white spaces
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#=========
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col_to_split2 = other_muts_col
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print ('applying second tidy split() separately on other muts df', meta_gene_other.shape
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, '\ncolumn name to apply tidy_split():', col_to_split2
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, '\n============================================================')
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# apply tidy_split()
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other_WF1 = tidy_split(meta_gene_other, col_to_split2, sep = ';')
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# remove the leading white spaces in the column
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other_WF1[other_muts_col] = other_WF1[other_muts_col].str.strip()
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# extract only the samples/rows with nssnp_match
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#other_gene_WF1 = other_WF1.loc[other_WF1[other_muts_col].str.contains(gene_match)]
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other_gene_WF1 = other_WF1.loc[other_WF1[other_muts_col].str.contains(nssnp_match, regex = True, case = False)]
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print('Lengths after tidy split and extracting', gene_match, 'muts:',
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'\nOld length:' , len(meta_gene_other),
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'\nLength after split:', len(other_WF1),
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'\nLength of nssnp df:', len(other_gene_WF1),
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'\nExpected len:', other_gene_count
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, '\n=============================================================')
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# Important: Assign 'column name' on which split was performed as an extra column
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# This is so you can identify if mutations are dr_type or other in the final df
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other_df = other_gene_WF1.assign(mutation_info = other_muts_col)
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print('dim of other_df:', other_df.shape
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, '\n==============================================================='
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, '\nEnd of tidy split() on other_muts, and added an extra column relecting mut_category'
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, '\n===============================================================')
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if dr_muts_col in other_df.columns:
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print('Dropping:', dr_muts_col, 'from WF gene other_df')
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other_df = other_df.drop([dr_muts_col], axis = 1)
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###############################################################################
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#%% Finding ambiguous muts
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common_snps_dr_other = set(dr_gene_mutsL).intersection(set(other_gene_mutsL))
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#%% More sanity checks: expected unique snps and nrows in LF data
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expected_unique_snps = len(set(dr_gene_mutsL)) + len(set(other_gene_mutsL)) - len(common_snps_dr_other)
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expected_rows = dr_gene_count + other_gene_count
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#%% Useful results to note==> counting dr, other and common muts
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print('\n==================================================================='
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, '\nCount unique nsSNPs for', gene, ':'
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, expected_unique_snps
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, '\n===================================================================')
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Vcounts_dr = pd.Series(dr_gene_mutsL).value_counts()
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Vcounts_common_dr = Vcounts_dr.get(list(common_snps_dr_other))
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print('\n==================================================================='
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, "\nCount of samples for common muts in dr muts\n"
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, Vcounts_common_dr
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, '\n===================================================================')
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Vcounts_other = pd.Series(other_gene_mutsL).value_counts()
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Vcounts_common_other = Vcounts_other.get(list(common_snps_dr_other))
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print('\n==================================================================='
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, '\nCount of samples for common muts in other muts\n'
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, Vcounts_common_other
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, '\n===================================================================')
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print('\n==================================================================='
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, '\nPredicting total no. of rows in the curated df:', expected_rows
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, '\n===================================================================')
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#%%another way: Add value checks for dict so you can know if its correct for LF data count below
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dr_snps_vc_dict = pd.Series(dr_gene_mutsL).value_counts().to_dict()
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other_snps_vc_dict = pd.Series(other_gene_mutsL).value_counts().to_dict()
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for k, v in dr_snps_vc_dict.items():
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if k in common_snps_dr_other:
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print(k,v)
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for k, v in other_snps_vc_dict.items():
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if k in common_snps_dr_other:
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print(k,v)
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# using defaultdict
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Cdict = collections.defaultdict(int)
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# iterating key, val with chain()
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for key, val in itertools.chain(dr_snps_vc_dict.items(), other_snps_vc_dict.items()):
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if key in common_snps_dr_other:
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Cdict[key] += val
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else:
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Cdict[key] = val
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print(dict(Cdict))
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for k, v in Cdict.items():
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if k in common_snps_dr_other:
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print(k,v)
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###############################################################################
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# USE Vcounts to get expected curated df
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# resolve dm om muts funda
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#%%#%% Concatenating two dfs: gene_LF0
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# equivalent of rbind in R
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#==========
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# Concatentating the two dfs: equivalent of rbind in R
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#==========
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# Important: Change column names to allow concat:
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# dr_muts.. & other_muts : 'mutation'
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print('Now concatenating the two dfs by row'
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, '\nFirst assigning a common colname: "mutation" to the col containing muts'
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, '\nThis is done for both dfs'
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, '\n===================================================================')
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dr_df.columns
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dr_df.rename(columns = {dr_muts_col: 'mutation'}, inplace = True)
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dr_df.columns
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other_df.columns
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other_df.rename(columns = {other_muts_col: 'mutation'}, inplace = True)
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other_df.columns
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if len(dr_df.columns) == len(other_df.columns):
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print('Checking dfs for concatening by rows:'
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, '\nDim of dr_df:', dr_df.shape
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, '\nDim of other_df:', other_df.shape
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, '\nExpected nrows:', len(dr_df) + len(other_df)
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, '\n=============================================================')
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else:
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sys.exit('FAIL: No. of cols mismatch for concatenating')
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# checking colnames before concat
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print('Checking colnames BEFORE concatenating the two dfs...')
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if (set(dr_df.columns) == set(other_df.columns)):
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print('PASS: column names match necessary for merging two dfs')
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else:
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sys.exit('FAIL: Colnames mismatch for concatenating!')
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# concatenate (axis = 0): Rbind, adn keeo original index
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print('Now appending the two dfs: Rbind')
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gene_LF_comb = pd.concat([dr_df, other_df]
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#, ignore_index = True
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, axis = 0)
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if gene_LF_comb.index.nunique() == len(meta_gene_all.index):
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print('\nPASS:length of index in LF data:', len(gene_LF_comb.index)
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, '\nLength of unique index in LF data:', gene_LF_comb.index.nunique()
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)
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else:
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sys.exit('\nFAIL: expected length for combined LF data mismatch:'
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, '\nExpected length:', len(meta_gene_all.index)
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, '\nGot:', gene_LF_comb.index.nunique() )
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print('Finding stop mutations in concatenated df')
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stop_muts = gene_LF_comb['mutation'].str.contains('\*').sum()
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if stop_muts == 0:
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print('PASS: No stop mutations detected')
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else:
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print('stop mutations detected'
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, '\nNo. of stop muts:', stop_muts, '\n'
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, gene_LF_comb.groupby(['mutation_info'])['mutation'].apply(lambda x: x[x.str.contains('\*')].count())
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, '\nNow removing them')
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gene_LF0_nssnp = gene_LF_comb[~gene_LF_comb['mutation'].str.contains('\*')]
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print('snps only: by subtracting stop muts:', len(gene_LF0_nssnp))
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gene_LF0 = gene_LF_comb[gene_LF_comb['mutation'].str.contains(nssnp_match, case = False)]
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print('snps only by direct regex:', len(gene_LF0))
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if gene_LF0_nssnp.equals(gene_LF0):
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print('PASS: regex for extracting nssnp worked correctly & stop mutations successfully removed'
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, '\nUsing the regex extracted df')
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else:
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sys.exit('FAIL: posssibly regex or no of stop mutations'
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, 'Regex being used:', nssnp_match)
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#sys.exit()
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# checking colnames and length after concat
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print('Checking colnames AFTER concatenating the two dfs...')
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if (set(dr_df.columns) == set(gene_LF0.columns)):
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print('PASS: column names match'
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, '\n=============================================================')
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else:
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sys.exit('FAIL: Colnames mismatch AFTER concatenating')
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print('Checking concatenated df')
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if len(gene_LF0) == (len(dr_df) + len(other_df))- stop_muts:
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print('PASS:length of df after concat match'
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, '\n===============================================================')
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else:
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sys.exit('FAIL: length mismatch')
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#%% NEW check2:id, sample, etc.
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gene_LF0['id'].count()
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gene_LF0['id'].nunique()
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gene_LF0['sample'].nunique()
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gene_LF0['mutation_info'].value_counts()
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gene_LF0[drug].isna().sum()
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#%% Concatenating two dfs sanity checks: gene_LF1
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#=========================================================
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# This is hopefully clean data, but just double check
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# Filter LF data so that you only have
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# mutations corresponding to nssnp_match (case insensitive)
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# this will be your list you run OR calcs
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#=========================================================
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print('Length of gene_LF0:', len(gene_LF0)
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, '\nThis should be what we need. But just double checking and extracting nsSNP for', gene
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, '\nfrom LF0 (concatenated data) using case insensitive regex match:', nssnp_match)
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|
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gene_LF1 = gene_LF0[gene_LF0['mutation'].str.contains(nssnp_match, regex = True, case = False)]
|
||||
|
||||
if len(gene_LF0) == len(gene_LF1):
|
||||
print('PASS: length of gene_LF0 and gene_LF1 match',
|
||||
'\nConfirming extraction and concatenating worked correctly'
|
||||
, '\n==========================================================')
|
||||
else:
|
||||
print('FAIL: BUT NOT FATAL!'
|
||||
, '\ngene_LF0 and gene_LF1 lengths differ'
|
||||
, '\nsuggesting error in extraction process'
|
||||
, ' use gene_LF1 for downstreama analysis'
|
||||
, '\n=========================================================')
|
||||
|
||||
print('Length of dfs pre and post processing...'
|
||||
, '\ngene WF data (unique samples in each row):',len(meta_gene_all)
|
||||
, '\ngene LF data (unique mutation in each row):', len(gene_LF1)
|
||||
, '\n=============================================================')
|
||||
|
||||
# sanity check for extraction
|
||||
# This ought to pass if nsnsp_match happens right at the beginning of creating 'expected_rows'
|
||||
print('Verifying whether extraction process worked correctly...')
|
||||
if len(gene_LF1) == expected_rows:
|
||||
print('PASS: extraction process performed correctly'
|
||||
, '\nExpected length:', expected_rows
|
||||
, '\nGot:', len(gene_LF1)
|
||||
, '\nRESULT: Total no. of mutant sequences for logo plot:', len(gene_LF1)
|
||||
, '\n=========================================================')
|
||||
else:
|
||||
print('FAIL: extraction process has bugs'
|
||||
, '\nExpected length:', expected_rows
|
||||
, '\nGot:', len(gene_LF1)
|
||||
, '\nDebug please'
|
||||
, '\n=========================================================')
|
||||
|
||||
# more sanity checks
|
||||
print('Performing some more sanity checks...')
|
||||
|
||||
# From LF1: useful for OR counts
|
||||
# no. of unique muts
|
||||
distinct_muts = gene_LF1.mutation.value_counts()
|
||||
print('Distinct genomic mutations:', len(distinct_muts))
|
||||
|
||||
# no. of samples contributing the unique muts
|
||||
gene_LF1.id.nunique()
|
||||
print('No.of samples contributing to distinct genomic muts:', gene_LF1.id.nunique())
|
||||
|
||||
# no. of dr and other muts
|
||||
mut_grouped = gene_LF1.groupby('mutation_info').mutation.nunique()
|
||||
print('No.of unique dr and other muts:\n', gene_LF1.groupby('mutation_info').mutation.nunique())
|
||||
|
||||
# sanity check
|
||||
if len(distinct_muts) == mut_grouped.sum() :
|
||||
print('PASS:length of LF1 is as expected'
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print('FAIL: Mistmatch in count of muts'
|
||||
, '\nExpected count:', len(distinct_muts)
|
||||
, '\nActual count:', mut_grouped.sum()
|
||||
, '\nMuts should be distinct within dr* and other* type'
|
||||
, '\nInspecting...possibly ambiguous muts'
|
||||
, '\nNo. of possible ambiguous muts:', mut_grouped.sum() - len(distinct_muts)
|
||||
, '\n=========================================================')
|
||||
|
||||
muts_split = list(gene_LF1.groupby('mutation_info'))
|
||||
dr_muts = muts_split[0][1].mutation
|
||||
other_muts = muts_split[1][1].mutation
|
||||
print('splitting muts by mut_info:', muts_split)
|
||||
print('no.of dr_muts samples:', len(dr_muts))
|
||||
print('no. of other_muts samples', len(other_muts))
|
||||
#%% Ambiguous muts
|
||||
# IMPORTANT: The same mutation cannot be classed as a drug AND 'others'
|
||||
if dr_muts.isin(other_muts).sum() & other_muts.isin(dr_muts).sum() > 0:
|
||||
print('WARNING: Ambiguous muts detected in dr_ and other_ mutation category'
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print('PASS: NO ambiguous muts detected'
|
||||
, '\nMuts are unique to dr_ and other_ mutation class'
|
||||
, '\n=========================================================')
|
||||
|
||||
# inspect dr_muts and other muts: Fixed in case no ambiguous muts detected!
|
||||
if dr_muts.isin(other_muts).sum() & other_muts.isin(dr_muts).sum() > 0:
|
||||
print('Finding ambiguous muts...'
|
||||
, '\n========================================================='
|
||||
, '\nTotal no. of samples in dr_muts present in other_muts:', dr_muts.isin(other_muts).sum()
|
||||
, '\nThese are:', dr_muts[dr_muts.isin(other_muts)]
|
||||
, '\n========================================================='
|
||||
, '\nTotal no. of samples in other_muts present in dr_muts:', other_muts.isin(dr_muts).sum()
|
||||
, '\nThese are:\n', other_muts[other_muts.isin(dr_muts)]
|
||||
, '\n=========================================================')
|
||||
|
||||
print('Counting no. of ambiguous muts...'
|
||||
, '\nNo. of ambiguous muts in dr:'
|
||||
, len(dr_muts[dr_muts.isin(other_muts)].value_counts().keys().tolist())
|
||||
, '\nNo. of ambiguous muts in other:'
|
||||
, len(other_muts[other_muts.isin(dr_muts)].value_counts().keys().tolist())
|
||||
, '\n=========================================================')
|
||||
|
||||
if dr_muts[dr_muts.isin(other_muts)].nunique() == other_muts[other_muts.isin(dr_muts)].nunique():
|
||||
common_muts = dr_muts[dr_muts.isin(other_muts)].value_counts().keys().tolist()
|
||||
print('Distinct no. of ambigiuous muts detected:'+ str(len(common_muts))
|
||||
, '\nlist of ambiguous mutations (see below):', *common_muts, sep = '\n')
|
||||
print('\n===========================================================')
|
||||
else:
|
||||
#sys.exit('Error: ambiguous muts present, but extraction failed. Debug!')
|
||||
print('No: ambiguous muts present')
|
||||
|
||||
#%% Ambiguous muts: revised annotation for mutation_info
|
||||
ambiguous_muts_df = gene_LF1[gene_LF1['mutation'].isin(common_muts)]
|
||||
ambiguous_muts_value_counts = ambiguous_muts_df.groupby('mutation')['mutation_info'].value_counts()
|
||||
ambiguous_muts_value_counts
|
||||
|
||||
gene_LF1_orig = gene_LF1.copy()
|
||||
gene_LF1_orig.equals(gene_LF1)
|
||||
|
||||
# copy the old columns for checking
|
||||
gene_LF1['mutation_info_orig'] = gene_LF1['mutation_info']
|
||||
gene_LF1['mutation_info_v1'] = gene_LF1['mutation_info']
|
||||
gene_LF1['mutation_info'].value_counts()
|
||||
#%% Inspect ambiguous muts
|
||||
#=====================================
|
||||
# Now create a new df that will have:
|
||||
# ambiguous muts
|
||||
# mutation_info
|
||||
# revised mutation_info
|
||||
# The revised label is based on value_counts
|
||||
# for mutaiton_info. The corresponding mutation_info:
|
||||
# label is chosen that corresponds to the max of value counts
|
||||
#=====================================
|
||||
ambig_muts_rev_df = pd.DataFrame()
|
||||
changes_val = []
|
||||
changes_dict = {}
|
||||
|
||||
for i in common_muts:
|
||||
#print(i)
|
||||
temp_df = gene_LF1[gene_LF1['mutation'] == i][['mutation', 'mutation_info_orig']]
|
||||
|
||||
# DANGER: ASSUMES TWO STATES ONLY and that value_counts sorts by descending
|
||||
max_info_table = gene_LF1[gene_LF1['mutation'] == i][['mutation', 'mutation_info_orig']].value_counts()
|
||||
|
||||
revised_label = max_info_table[[0]].index[0][1] # max value_count
|
||||
old_label = max_info_table[[1]].index[0][1] # min value_count
|
||||
print('\nAmbiguous mutation labels...'
|
||||
, '\nSetting mutation_info for', i, 'to', revised_label)
|
||||
temp_df['mutation_info_REV'] = np.where( (temp_df['mutation_info_orig'] == old_label)
|
||||
, revised_label
|
||||
, temp_df['mutation_info_orig'])
|
||||
ambig_muts_rev_df = pd.concat([ambig_muts_rev_df,temp_df])
|
||||
f = max_info_table[[1]]
|
||||
old_label_count = f[[0]][0]
|
||||
changes_val.append(old_label_count)
|
||||
cc_dict = f.to_dict()
|
||||
changes_dict.update(cc_dict)
|
||||
|
||||
ambig_muts_rev_df['mutation_info_REV'].value_counts()
|
||||
ambig_muts_rev_df['mutation_info_orig'].value_counts()
|
||||
changes_val
|
||||
changes_total = sum(changes_val)
|
||||
changes_dict
|
||||
#%% OUTFILE 3, write file: ambiguous muts and ambiguous mut counts
|
||||
#===================
|
||||
# ambiguous muts
|
||||
#=======================
|
||||
#dr_muts.to_csv('dr_muts.csv', header = True)
|
||||
#other_muts.to_csv('other_muts.csv', header = True)
|
||||
if dr_muts.isin(other_muts).sum() & other_muts.isin(dr_muts).sum() > 0:
|
||||
out_filename_ambig_muts = gene.lower() + '_ambiguous_muts.csv'
|
||||
outfile_ambig_muts = outdir + '/' + out_filename_ambig_muts
|
||||
print('\n----------------------------------'
|
||||
, '\nWriting file: ambiguous muts'
|
||||
, '\n----------------------------------'
|
||||
, '\nFilename:', outfile_ambig_muts)
|
||||
inspect = gene_LF1[gene_LF1['mutation'].isin(common_muts)]
|
||||
inspect.to_csv(outfile_ambig_muts, index = True)
|
||||
|
||||
print('Finished writing:', out_filename_ambig_muts
|
||||
, '\nNo. of rows:', len(inspect)
|
||||
, '\nNo. of cols:', len(inspect.columns)
|
||||
, '\nNo. of rows = no. of samples with the ambiguous muts present:'
|
||||
, dr_muts.isin(other_muts).sum() + other_muts.isin(dr_muts).sum()
|
||||
, '\n=============================================================')
|
||||
del(out_filename_ambig_muts)
|
||||
|
||||
#%% FIXME: ambig mut counts
|
||||
#=======================
|
||||
# ambiguous mut counts
|
||||
#=======================
|
||||
out_filename_ambig_mut_counts = gene.lower() + '_ambiguous_mut_counts.csv'
|
||||
outfile_ambig_mut_counts = outdir + '/' + out_filename_ambig_mut_counts
|
||||
print('\n----------------------------------'
|
||||
, '\nWriting file: ambiguous muts'
|
||||
, '\n----------------------------------'
|
||||
, '\nFilename:', outfile_ambig_mut_counts)
|
||||
|
||||
ambiguous_muts_value_counts.to_csv(outfile_ambig_mut_counts, index = True)
|
||||
#%%FIXME: TODO: Add sanity check to make sure you can add value_count checks
|
||||
#%% Resolving ambiguous muts
|
||||
# Merging ambiguous muts
|
||||
#=================
|
||||
# Merge ambig muts
|
||||
# with gene_LF1
|
||||
#===================
|
||||
ambig_muts_rev_df.index
|
||||
gene_LF1.index
|
||||
all(ambig_muts_rev_df.index.isin(gene_LF1.index))
|
||||
|
||||
gene_LF1.loc[ambig_muts_rev_df.index, 'mutation_info_v1'] = ambig_muts_rev_df['mutation_info_REV']
|
||||
gene_LF1['mutation_info_orig'].value_counts()
|
||||
gene_LF1['mutation_info_v1'].value_counts()
|
||||
foo = gene_LF1.iloc[ambig_muts_rev_df.index]
|
||||
|
||||
# Sanity check1: if there are still any ambiguous muts
|
||||
muts_split_rev = list(gene_LF1.groupby('mutation_info_v1'))
|
||||
dr_muts_rev = muts_split_rev[0][1].mutation
|
||||
other_muts_rev = muts_split_rev[1][1].mutation
|
||||
print('splitting muts by mut_info:', muts_split_rev)
|
||||
print('no.of dr_muts samples:', len(dr_muts_rev))
|
||||
print('no. of other_muts samples', len(other_muts_rev))
|
||||
|
||||
if not dr_muts_rev.isin(other_muts_rev).sum() & other_muts_rev.isin(dr_muts_rev).sum() > 0:
|
||||
print('\nAmbiguous muts corrected. Proceeding with downstream analysis')
|
||||
else:
|
||||
print('\nAmbiguous muts NOT corrected. Quitting!')
|
||||
sys.exit()
|
||||
|
||||
gene_LF1['mutation_info_v1'].value_counts()
|
||||
#%% PHEW! Good to go for downstream stuff
|
||||
Loading…
Add table
Add a link
Reference in a new issue