added lambda func to normalise duet and aff values
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398eccd246
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7d2241ad81
1 changed files with 123 additions and 49 deletions
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@ -37,7 +37,7 @@ gene_match = gene + '_p.'
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# data dir
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#==========
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datadir = homedir + '/' + 'git/Data'
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#5am0chod
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#=======
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# input:
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#=======
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@ -53,22 +53,25 @@ print('Input filename:', in_filename
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# output
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#=======
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outdir = datadir + '/' + drug + '/' + 'output'
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#out_filename = gene.lower() + '.csv'
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#outfile = outdir + '/' + out_filename
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#print('Output filename:', out_filename
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# , '\nOutput path:', outdir
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# , '\n=============================================================')
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out_filename = gene.lower() + '_complex_mcsm_norm.csv'
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outfile = outdir + '/' + out_filename
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print('Output filename:', out_filename
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, '\nOutput path:', outdir
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, '\n=============================================================')
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#=======================================================================
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print('Reading input file')
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mcsm_data = pd.read_csv(infile, sep = ',')
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mcsm_data.columns
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# PredAffLog = affinity_change_log
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# "DUETStability_Kcalpermol = DUET_change_kcalpermol
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dforig_shape = mcsm_data.shape
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print('dim of infile:', dforig_shape)
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# change colnames to reflect units and no spaces, and replace '-' with '-'
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print('Assigning meaningful colnames i.e without spaces and hyphen and reflecting units'
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, '\n===================================================================')
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my_colnames_dict = {'Predicted Affinity Change': 'PredAffLog'
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, 'Mutation information': 'Mutationinformation'
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, 'Wild-type': 'Wild_type'
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@ -81,44 +84,33 @@ my_colnames_dict = {'Predicted Affinity Change': 'PredAffLog'
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mcsm_data.rename(columns = my_colnames_dict, inplace = True)
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mcsm_data.columns
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#%%===========================================================================
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# Extract only the numeric part from col: Dis_lig_Ang
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# number: '-?\d+\.?\d*'
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mcsm_data['Dis_lig_Ang']
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print('extracting numeric part of col: Dis_lig_Ang')
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mcsm_data['Dis_lig_Ang'] = mcsm_data['Dis_lig_Ang'].str.extract('(\d+\.?\d*)')
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mcsm_data['Dis_lig_Ang']
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# changing dtype to numeric
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#if is_numeric_dtype(mcsm_data['Dis_lig_Ang']):
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# print('Data type is already numeric, doing nothing')
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#else:
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# print('Changing dtype in col: Dis_lig_Ang to numeric since Distance should be numeric')
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## FIXME: either do it here, or in the end for all the required cols at once
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#%%===========================================================================
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# populate mutationinformation column:mcsm style muts {WT}<POS>{MUT}
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print('populating column : Mutationinformation which is currently empty\n', mcsm_data['Mutationinformation'])
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mcsm_data['Mutationinformation'] = mcsm_data['Wild_type'] + mcsm_data['Position'].astype(str) + mcsm_data['Mutant_type']
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print('checking after populating:\n', mcsm_data['Mutationinformation'])
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print('checking after populating:\n', mcsm_data['Mutationinformation']
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, '\n===================================================================')
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# Remove spaces b/w pasted columns
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print('removing white space within column: \Mutationinformation')
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mcsm_data['Mutationinformation'] = mcsm_data['Mutationinformation'].str.replace(' ', '')
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print('Correctly formatted column: Mutationinformation\n', mcsm_data['Mutationinformation'])
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print('Correctly formatted column: Mutationinformation\n', mcsm_data['Mutationinformation']
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, '\n===================================================================')
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#%%===========================================================================
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# very important
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print('Sanity check:'
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, '\nChecking duplicate mutations')
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if mcsm_data['Mutationinformation'].duplicated().sum() == 0:
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print('PASS: No duplicate mutations detected (as expected)'
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, '\nDim of data:', mcsm_data.shape)
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, '\nDim of data:', mcsm_data.shape
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, '\n===============================================================')
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else:
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print('FAIL (but not fatal): Duplicate mutations detected'
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, '\nDim of df with duplicates:', mcsm_data.shape
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, 'Removing duplicate entries')
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mcsm_data = mcsm_data.drop_duplicates(['Mutationinformation'])
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print('Dim of data after removing duplicate muts:', mcsm_data.shape)
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print('Dim of data after removing duplicate muts:', mcsm_data.shape
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, '\n===============================================================')
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#%%===========================================================================
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# create DUET_outcome column: classification based on DUET stability values
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print('Assigning col: DUET_outcome based on DUET stability values')
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@ -135,25 +127,41 @@ if DUET_pos == mcsm_data['DUET_outcome'].value_counts()['Stabilising']:
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else:
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print('FAIL: DUET outcome assigned incorrectly'
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, '\nExpected no. of stabilising mutations:', DUET_pos
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, '\nGot no. of stabilising mutations', mcsm_data['DUET_outcome'].value_counts()['Stabilising'])
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, '\nGot no. of stabilising mutations', mcsm_data['DUET_outcome'].value_counts()['Stabilising']
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, '\n===============================================================')
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#%%===========================================================================
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# Extract only the numeric part from col: Dis_lig_Ang
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# number: '-?\d+\.?\d*'
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mcsm_data['Dis_lig_Ang']
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print('extracting numeric part of col: Dis_lig_Ang')
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mcsm_data['Dis_lig_Ang'] = mcsm_data['Dis_lig_Ang'].str.extract('(\d+\.?\d*)')
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mcsm_data['Dis_lig_Ang']
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# changing dtype to numeric
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#if is_numeric_dtype(mcsm_data['Dis_lig_Ang']):
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# print('Data type is already numeric, doing nothing')
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#else:
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# print('Changing dtype in col: Dis_lig_Ang to numeric since Distance should be numeric')
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## FIXME: either do it here, or in the end for all the required cols at once
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#%%===========================================================================
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# create Lig_outcome column: classification based on affinity change values
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# the numerical and categorical parts need to be extracted from column: PredAffLog
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# regex used
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# number: '-?\d+\.?\d*'
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# category: '\b(\w+ing)\b'
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print('Creating affinity change columns...')
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print('Extracting numerical and categorical parts from the col: PredAffLog')
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#aff_regex = re.compile(r'\b(\w+ing)\b')
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print('to create two columns: affinity_change_log and Lig_outcome'
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, '\n===================================================================')
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# Extracting the predicted affinity change (numerical part)
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mcsm_data['affinity_change_log'] = mcsm_data['PredAffLog'].str.extract('(-?\d+\.?\d*)', expand = True)
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print(mcsm_data['affinity_change_log'])
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# Extracting the categorical part (Destabillizing and Stabilizing) using word boundary ('ing')
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#aff_regex = re.compile(r'\b(\w+ing)\b')
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mcsm_data['Lig_outcome']= mcsm_data['PredAffLog'].str.extract(r'(\b\w+ing\b)', expand = True)
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print(mcsm_data['Lig_outcome'])
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print(mcsm_data['Lig_outcome'].value_counts())
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american_spl = mcsm_data['Lig_outcome'].value_counts()
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print('Changing to Bristish spellings for col: Lig_outcome')
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mcsm_data['Lig_outcome'].replace({'Destabilizing': 'Destabilising', 'Stabilizing': 'Stabilising'}, inplace = True)
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print(mcsm_data['Lig_outcome'].value_counts())
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@ -161,34 +169,100 @@ british_spl = mcsm_data['Lig_outcome'].value_counts()
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# since series object will have different names on account of our spelling change
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# use .equals
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if american_spl.equals(british_spl):
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print('PASS: spelling change successfull')
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check = american_spl.values == british_spl.values
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if check.all():
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print('PASS: spelling change successfull'
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, '\nNo. of predicted affinity changes:\n', british_spl
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, '\n===============================================================')
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else:
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print('FAIL: spelling change unsucessfull'
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, '\nExpected:\n', american_spl
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, '\nGot:\n', british_spl)
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, '\nGot:\n', british_spl
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, '\n===============================================================')
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#%%===========================================================================
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# check dtype in cols
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print(mcsm_data.dtypes)
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print('Checking dtypes in all columns:\n', mcsm_data.dtypes
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, '\n===================================================================')
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print('Converting the following cols to numeric:'
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, '\nDis_lig_Ang'
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, '\nDUET_change_kcalpermol'
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, '\naffinity_change_log'
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, '\n===================================================================')
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# using apply method to change stabilty and affinity values to numeric
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mcsm_data[['affinity_change_log'
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, 'DUET_change_kcalpermol'
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, 'Dis_lig_Ang']] = mcsm_data[['affinity_change_log'
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, 'DUET_change_kcalpermol'
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, 'Dis_lig_Ang']].apply(pd.to_numeric)
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numeric_cols = ['DUET_change_kcalpermol', 'affinity_change_log', 'Dis_lig_Ang']
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mcsm_data[numeric_cols] = mcsm_data[numeric_cols].apply(pd.to_numeric)
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# check dtype in cols
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print('checking dtype after conversion')
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cols_check = mcsm_data.select_dtypes(include='float64').columns.isin(numeric_cols)
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if cols_check.all():
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print('PASS: dtypes for selected cols:', numeric_cols
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, '\nchanged to numeric'
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, '\n===============================================================')
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else:
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print('FAIL:dtype change to numeric for selected cols unsuccessful'
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, '\n===============================================================')
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#mcsm_data['Dis_lig_Ang', 'affinity_change_log'].apply(is_numeric_dtype(mcsm_data['Dis_lig_Ang', 'affinity_change_log']))
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print(mcsm_data.dtypes)
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#%%===========================================================================
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#%%===========================================================================
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# Normalise the DUET and affinity change cols
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#converter = lambda x : x*2 if x < 10 else (x*3 if x < 20 else x)
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duet_min = mcsm_data['DUET_change_kcalpermol'].min()
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duet_max = mcsm_data['DUET_change_kcalpermol'].max()
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converter = lambda x : x/abs(duet_min) if x < 0 else (x/duet_max if x >= 0 else 'failed')
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mcsm_data['DUET_change_kcalpermol']
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mcsm_data['ratioDUET'] = mcsm_data['DUET_change_kcalpermol'].apply(converter)
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mcsm_data['ratioDUET']
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#%%===========================================================================
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# Normalise the affinity change cols
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aff_min = mcsm_data['affinity_change_log'].min()
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aff_max = mcsm_data['affinity_change_log'].max()
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converter = lambda x : x/abs(aff_min) if x < 0 else (x/aff_max if x >= 0 else 'failed')
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#converter(mcsm_data['affinity_change_log'])
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mcsm_data['affinity_change_log']
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mcsm_data['ratioPredAff'] = mcsm_data['affinity_change_log'].apply(converter)
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mcsm_data['ratioPredAff']
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#=============================================================================
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# Removing PredAff log column as it is not needed?
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print('Removing col: PredAffLog since relevant info has been extracted from it')
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mcsm_dataf = mcsm_data.drop(columns = ['PredAffLog'])
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#%%===========================================================================
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expected_cols_toadd = 4
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dforig_len = dforig_shape[1]
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expected_cols = dforig_len + expected_cols_toadd
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if len(mcsm_dataf.columns) == expected_cols:
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print('PASS: formatting successful'
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, '\nformatted df has expected no. of cols:', expected_cols
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, '\n---------------------------------------------------------------'
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, '\ncolnames:', mcsm_dataf.columns
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, '\n----------------------------------------------------------------'
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, '\ndtypes in cols:', mcsm_dataf.dtypes
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, '\n----------------------------------------------------------------'
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, '\norig data shape:', dforig_shape
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, '\nformatted df shape:', mcsm_dataf.shape
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, '\n===============================================================')
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else:
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print('FAIL: something went wrong in formatting df'
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, '\nExpected no. of cols:', expected_cols
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, '\nGot no. of cols:', len(mcsm_dataf.columns)
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, '\nCheck formatting'
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, '\n===============================================================')
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#%%============================================================================
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# writing file
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print('Writing formatted df to csv')
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mcsm_dataf.to_csv(outfile, index = False)
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#%%===========================================================================
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# Normalise the DUET and affinity change cols:
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print('Finished writing file:'
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, '\nFilename:', out_filename
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, '\nPath:', outdir
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, '\nExpected no. of rows:', len(mcsm_dataf)
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, '\nExpected no. of cols:', len(mcsm_dataf.columns)
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, '\n=============================================================')
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#%%
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#End of script
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