tidy code and saving work for the day
This commit is contained in:
parent
69e2567ffc
commit
afd6ca8881
6 changed files with 307 additions and 284 deletions
|
|
@ -1,10 +1,10 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
'''
|
||||
Created on Tue Aug 6 12:56:03 2019
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
'''
|
||||
|
||||
# FIXME: include error checking to enure you only
|
||||
# concentrate on positions that have structural info?
|
||||
|
|
@ -67,7 +67,8 @@ datadir = homedir + '/' + 'git/Data'
|
|||
in_filename = 'original_tanushree_data_v2.csv'
|
||||
infile = datadir + '/' + in_filename
|
||||
print('Input filename: ', in_filename
|
||||
, '\nInput path: ', indir)
|
||||
, '\nInput path: ', indir
|
||||
, '\n============================================================')
|
||||
|
||||
#=======
|
||||
# output
|
||||
|
|
@ -76,7 +77,8 @@ print('Input filename: ', in_filename
|
|||
# output filenames in respective sections at the time of outputting files
|
||||
outdir = datadir + '/' + drug + '/' + 'output'
|
||||
print('Output filename: in the respective sections'
|
||||
, '\nOutput path: ', outdir)
|
||||
, '\nOutput path: ', outdir
|
||||
, '\n=============================================================')
|
||||
|
||||
#%%end of variable assignment for input and output files
|
||||
#=======================================================================
|
||||
|
|
@ -101,21 +103,22 @@ del(master_data)
|
|||
|
||||
# checks and results
|
||||
total_samples = meta_data['id'].nunique()
|
||||
print('RESULT: Total samples:', total_samples)
|
||||
print('======================================================================')
|
||||
print('RESULT: Total samples:', total_samples
|
||||
, '\n===========================================================')
|
||||
|
||||
# counts NA per column
|
||||
meta_data.isna().sum()
|
||||
print('No. of NAs/column:' + '\n', meta_data.isna().sum())
|
||||
print('======================================================================')
|
||||
print('No. of NAs/column:' + '\n', meta_data.isna().sum()
|
||||
, '\n===========================================================')
|
||||
|
||||
# glance
|
||||
meta_data.head()
|
||||
|
||||
# equivalent of table in R
|
||||
# pyrazinamide counts
|
||||
meta_data.pyrazinamide.value_counts()
|
||||
print('RESULT: Sus and Res samples:\n', meta_data.pyrazinamide.value_counts())
|
||||
print('======================================================================')
|
||||
print('RESULT: Sus and Res samples:\n', meta_data.pyrazinamide.value_counts()
|
||||
, '\n===========================================================')
|
||||
|
||||
# clear variables
|
||||
del(indir, in_filename,infile)
|
||||
|
|
@ -140,11 +143,12 @@ clean_df = meta_data.dropna(subset=['dr_mutations_pyrazinamide'])
|
|||
na_count = meta_data['dr_mutations_pyrazinamide'].isna().sum()
|
||||
|
||||
if len(clean_df) == (total_samples - na_count):
|
||||
print('PASS: clean_df extracted: length is', len(clean_df),
|
||||
'\nNo.of NA s=', na_count, '/', total_samples)
|
||||
print('PASS: clean_df extracted: length is', len(clean_df)
|
||||
, '\nNo.of NAs =', na_count, '/', total_samples
|
||||
, '\n==========================================================')
|
||||
else:
|
||||
print('FAIL: dropping NA failed')
|
||||
print('======================================================================')
|
||||
print('FAIL: dropping NA failed'
|
||||
, '\n==========================================================')
|
||||
|
||||
dr_pnca_count = 0
|
||||
wt = 0
|
||||
|
|
@ -169,7 +173,7 @@ print('RESULTS:')
|
|||
print('Total WT in dr_mutations_pyrazinamide:', wt)
|
||||
print('Total matches of', gene_match, 'in dr_mutations_pyrazinamide:', dr_pnca_count)
|
||||
print('Total samples with > 1', gene_match, 'muts in dr_mutations_pyrazinamide:', len(id2_dr) )
|
||||
print('======================================================================')
|
||||
print('=================================================================')
|
||||
|
||||
del(i, id, wt, id2_dr, clean_df, na_count, count_pnca_dr, count_wt)
|
||||
|
||||
|
|
@ -185,11 +189,12 @@ clean_df = meta_data.dropna(subset=['other_mutations_pyrazinamide'])
|
|||
na_count = meta_data['other_mutations_pyrazinamide'].isna().sum()
|
||||
|
||||
if len(clean_df) == (total_samples - na_count):
|
||||
print('PASS: clean_df extracted: length is', len(clean_df),
|
||||
'\nNo.of NA s=', na_count, '/', total_samples)
|
||||
print('PASS: clean_df extracted: length is', len(clean_df)
|
||||
, '\nNo.of NA s=', na_count, '/', total_samples
|
||||
, '\n=========================================================')
|
||||
else:
|
||||
print('FAIL: dropping NA failed')
|
||||
print('======================================================================')
|
||||
print('FAIL: dropping NA failed'
|
||||
, '\n=========================================================')
|
||||
|
||||
other_pnca_count = 0
|
||||
wt_other = 0
|
||||
|
|
@ -213,7 +218,7 @@ print('RESULTS:')
|
|||
print('Total WT in other_mutations_pyrazinamide:', wt_other)
|
||||
print('Total matches of', gene_match, 'in other_mutations_pyrazinamide:', other_pnca_count)
|
||||
print('Total samples with > 1', gene_match, 'muts in other_mutations_pyrazinamide:', len(id2_other) )
|
||||
print('======================================================================')
|
||||
print('=================================================================')
|
||||
|
||||
print('Predicting total no. of rows in your curated df:', dr_pnca_count + other_pnca_count )
|
||||
expected_rows = dr_pnca_count + other_pnca_count
|
||||
|
|
@ -224,7 +229,7 @@ del(i, id, wt_other, clean_df, na_count, id2_other, count_pnca_other, count_wt)
|
|||
############
|
||||
# extracting dr and other muts separately along with the common cols
|
||||
#############
|
||||
print('======================================================================')
|
||||
print('=================================================================')
|
||||
print('Extracting dr_muts in a dr_mutations_pyrazinamide with other meta_data')
|
||||
print('gene to extract:', gene_match )
|
||||
|
||||
|
|
@ -241,9 +246,9 @@ meta_data_dr = meta_data[['id'
|
|||
, 'pyrazinamide'
|
||||
, 'dr_mutations_pyrazinamide'
|
||||
]]
|
||||
print("expected dim should be:", len(meta_data), (len(meta_data.columns)-1) )
|
||||
print("actual dim:", meta_data_dr.shape )
|
||||
print('======================================================================')
|
||||
print('expected dim should be:', len(meta_data), (len(meta_data.columns)-1) )
|
||||
print('actual dim:', meta_data_dr.shape
|
||||
, '\n===============================================================')
|
||||
|
||||
# Extract within this the gene of interest using string match
|
||||
#meta_pnca_dr = meta_data.loc[meta_data.dr_mutations_pyrazinamide.str.contains('pncA_p.*', na = False)]
|
||||
|
|
@ -256,17 +261,17 @@ print('checking RESULT:',
|
|||
'\nactual len =', len(meta_pnca_dr) )
|
||||
|
||||
if len(id_dr) == len(meta_pnca_dr):
|
||||
print('PASS: lengths match')
|
||||
print('PASS: lengths match'
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print('FAIL: length mismatch')
|
||||
print('======================================================================')
|
||||
print('FAIL: length mismatch'
|
||||
, '\n===============================================================')
|
||||
|
||||
dr_id = pd.Series(dr_id)
|
||||
|
||||
#=================
|
||||
# other mutations: extract pncA_p. entries
|
||||
#==================
|
||||
print('======================================================================')
|
||||
print('Extracting dr_muts in a other_mutations_pyrazinamide with other meta_data')
|
||||
# FIXME: replace pyrazinamide with variable containing the drug name
|
||||
# !!! important !!!
|
||||
|
|
@ -279,9 +284,9 @@ meta_data_other = meta_data[['id'
|
|||
, 'other_mutations_pyrazinamide'
|
||||
]]
|
||||
|
||||
print("expected dim should be:", len(meta_data), (len(meta_data.columns)-1) )
|
||||
print("actual dim:", meta_data_other.shape )
|
||||
print('======================================================================')
|
||||
print('expected dim should be:', len(meta_data), (len(meta_data.columns)-1) )
|
||||
print('actual dim:', meta_data_other.shape
|
||||
, '\n===============================================================')
|
||||
|
||||
# Extract within this the gene of interest using string match
|
||||
meta_pnca_other = meta_data_other.loc[meta_data_other.other_mutations_pyrazinamide.str.contains(gene_match, na = False)]
|
||||
|
|
@ -293,10 +298,11 @@ print('checking RESULT:',
|
|||
'\nactual len =', len(meta_pnca_other) )
|
||||
|
||||
if len(id_other) == len(meta_pnca_other):
|
||||
print('PASS: lengths match')
|
||||
print('PASS: lengths match'
|
||||
, '\n==============================================================')
|
||||
else:
|
||||
print('FAIL: length mismatch')
|
||||
print('======================================================================')
|
||||
print('FAIL: length mismatch'
|
||||
, '\n===============================================================')
|
||||
|
||||
other_id = pd.Series(other_id)
|
||||
#%% Find common IDs
|
||||
|
|
@ -323,61 +329,61 @@ print(common_ids['id'].equals(common_ids2['id2']))
|
|||
|
||||
# good sanity check: use it later to check pnca_sample_counts
|
||||
expected_pnca_samples = ( len(meta_pnca_dr) + len(meta_pnca_other) - common_mut_ids )
|
||||
print("expected no. of pnca samples:", expected_pnca_samples)
|
||||
print('======================================================================')
|
||||
print('expected no. of pnca samples:', expected_pnca_samples)
|
||||
print('=================================================================')
|
||||
#%% write file
|
||||
#print(outdir)
|
||||
out_filename0 = gene.lower() + '_' + 'common_ids.csv'
|
||||
outfile0 = outdir + '/' + out_filename0
|
||||
|
||||
#FIXME: CHECK line len(common_ids)
|
||||
print('Writing file: common ids:',
|
||||
'\nFilename:', out_filename0,
|
||||
'\nPath:', outdir,
|
||||
'\nExpected no. of rows:', len(common_ids) )
|
||||
print('Writing file: common ids:'
|
||||
, '\nFilename:', out_filename0
|
||||
, '\nPath:', outdir
|
||||
, '\nExpected no. of rows:', len(common_ids)
|
||||
, '\n=============================================================')
|
||||
|
||||
common_ids.to_csv(outfile0)
|
||||
print('======================================================================')
|
||||
del(out_filename0)
|
||||
|
||||
|
||||
# clear variables
|
||||
del(dr_id, other_id, meta_data_dr, meta_data_other, common_ids, common_mut_ids, common_ids2)
|
||||
|
||||
#%% Now extract "all" pncA mutations: i.e 'pncA_p.*'
|
||||
print("extracting all pncA mutations from dr_ and other_ cols using string match:", gene_match)
|
||||
#%% Now extract 'all' pncA mutations: i.e 'pncA_p.*'
|
||||
print('extracting all pncA mutations from dr_ and other_ cols using string match:', gene_match
|
||||
, '\n===============================================================')
|
||||
#meta_pnca_all = meta_data.loc[meta_data.dr_mutations_pyrazinamide.str.contains(gene_match) | meta_data.other_mutations_pyrazinamide.str.contains(gene_match) ]
|
||||
meta_pnca_all = meta_data.loc[meta_data.dr_mutations_pyrazinamide.str.contains(gene_match, na = False) | meta_data.other_mutations_pyrazinamide.str.contains(gene_match, na = False) ]
|
||||
print('======================================================================')
|
||||
|
||||
extracted_pnca_samples = meta_pnca_all['id'].nunique()
|
||||
print("RESULT: actual no. of pnca samples extracted:", extracted_pnca_samples)
|
||||
print('RESULT: actual no. of pnca samples extracted:', extracted_pnca_samples)
|
||||
print('======================================================================')
|
||||
|
||||
# sanity check: length of pnca samples
|
||||
print('Performing sanity check:')
|
||||
if extracted_pnca_samples == expected_pnca_samples:
|
||||
print('No. of pnca samples:', len(meta_pnca_all),
|
||||
'\nPASS: expected & actual no. of pnca samples match')
|
||||
print('No. of pnca samples:', len(meta_pnca_all)
|
||||
, '\nPASS: expected & actual no. of pnca samples match'
|
||||
, '\n=========================================================')
|
||||
else:
|
||||
print("FAIL: Debug please!")
|
||||
print('======================================================================')
|
||||
print('FAIL: Debug please!'
|
||||
, '\n===============================================================')
|
||||
|
||||
# count NA in pyrazinamide column
|
||||
pnca_na = meta_pnca_all['pyrazinamide'].isna().sum()
|
||||
print("No. of pnca samples without pza testing i.e NA in pza column:",pnca_na)
|
||||
print('No. of pnca samples without pza testing i.e NA in pza column:', pnca_na)
|
||||
|
||||
# use it later to check number of complete samples from LF data
|
||||
comp_pnca_samples = len(meta_pnca_all) - pnca_na
|
||||
print("comp pnca samples tested for pza:", comp_pnca_samples)
|
||||
print('======================================================================')
|
||||
print('comp pnca samples tested for pza:', comp_pnca_samples)
|
||||
print('=================================================================')
|
||||
|
||||
# Comment: This is still dirty data since these
|
||||
# are samples that have pncA_p. muts, but can have others as well
|
||||
# since the format for mutations is mut1; mut2, etc.
|
||||
print('This is still dirty data: samples have pncA_p. muts, but may have others as well',
|
||||
'\nsince the format for mutations is mut1; mut2, etc.')
|
||||
print('======================================================================')
|
||||
print('This is still dirty data: samples have pncA_p. muts, but may have others as well'
|
||||
, '\nsince the format for mutations is mut1; mut2, etc.'
|
||||
, '\n=============================================================')
|
||||
|
||||
#%% tidy_split():Function to split mutations on specified delimiter: ';'
|
||||
#https://stackoverflow.com/questions/41476150/removing-space-from-dataframe-columns-in-pandas
|
||||
|
|
@ -385,7 +391,7 @@ print('======================================================================')
|
|||
print('Performing tidy_spllit(): to separate the mutations into indivdual rows')
|
||||
# define the split function
|
||||
def tidy_split(df, column, sep='|', keep=False):
|
||||
"""
|
||||
'''
|
||||
Split the values of a column and expand so the new DataFrame has one split
|
||||
value per row. Filters rows where the column is missing.
|
||||
|
||||
|
|
@ -404,7 +410,7 @@ def tidy_split(df, column, sep='|', keep=False):
|
|||
-------
|
||||
pandas.DataFrame
|
||||
Returns a dataframe with the same columns as `df`.
|
||||
"""
|
||||
'''
|
||||
indexes = list()
|
||||
new_values = list()
|
||||
#df = df.dropna(subset=[column])#!!!!-----see this incase you need to uncomment based on use case
|
||||
|
|
@ -428,9 +434,9 @@ def tidy_split(df, column, sep='|', keep=False):
|
|||
# tidy_split(): on 'dr_mutations_pyrazinamide' column and remove leading white spaces
|
||||
########
|
||||
col_to_split1 = 'dr_mutations_pyrazinamide'
|
||||
print ('Firstly, applying tidy split on dr df:', meta_pnca_dr.shape,
|
||||
'\ncolumn name:', col_to_split1)
|
||||
print('======================================================================')
|
||||
print ('Firstly, applying tidy split on dr df:', meta_pnca_dr.shape
|
||||
, '\ncolumn name:', col_to_split1
|
||||
, '\n============================================================')
|
||||
# apply tidy_split()
|
||||
dr_WF0 = tidy_split(meta_pnca_dr, col_to_split1, sep = ';')
|
||||
# remove leading white space else these are counted as distinct mutations as well
|
||||
|
|
@ -446,43 +452,42 @@ print('lengths after tidy split and extracting', gene_match, 'muts:'
|
|||
, '\nexpected len:', dr_pnca_count)
|
||||
|
||||
if len(dr_pnca_WF0) == dr_pnca_count:
|
||||
print('PASS: length of dr_pnca_WF0 match with expected length')
|
||||
print('PASS: length of dr_pnca_WF0 match with expected length'
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print('FAIL: lengths mismatch')
|
||||
print('FAIL: lengths mismatch'
|
||||
, '\n===============================================================')
|
||||
|
||||
print('======================================================================')
|
||||
|
||||
# count the freq of "dr_muts" samples
|
||||
# count the freq of 'dr_muts' samples
|
||||
dr_muts_df = dr_pnca_WF0 [['id', 'dr_mutations_pyrazinamide']]
|
||||
print("dim of dr_muts_df:", dr_muts_df.shape)
|
||||
print('dim of dr_muts_df:', dr_muts_df.shape)
|
||||
|
||||
# add freq column
|
||||
dr_muts_df['dr_sample_freq'] = dr_muts_df.groupby('id')['id'].transform('count')
|
||||
#dr_muts_df['dr_sample_freq'] = dr_muts_df.loc[dr_muts_df.groupby('id')].transform('count')
|
||||
print("revised dim of dr_muts_df:", dr_muts_df.shape)
|
||||
print('revised dim of dr_muts_df:', dr_muts_df.shape)
|
||||
|
||||
c1 = dr_muts_df.dr_sample_freq.value_counts()
|
||||
print('counting no. of sample frequency:\n', c1)
|
||||
print('======================================================================')
|
||||
print('=================================================================')
|
||||
|
||||
# sanity check: length of pnca samples
|
||||
if len(dr_pnca_WF0) == c1.sum():
|
||||
print('PASS: WF data has expected length',
|
||||
'\nlength of dr_pnca WFO:', c1.sum() )
|
||||
print('PASS: WF data has expected length'
|
||||
, '\nlength of dr_pnca WFO:', c1.sum()
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print("FAIL: Debug please!")
|
||||
|
||||
print('======================================================================')
|
||||
print('FAIL: Debug please!'
|
||||
, '\n===============================================================')
|
||||
|
||||
#!!! Important !!!
|
||||
# Assign "column name" on which split was performed as an extra column
|
||||
# Assign 'column name' on which split was performed as an extra column
|
||||
# This is so you can identify if mutations are dr_type or other in the final df
|
||||
dr_df = dr_pnca_WF0.assign(mutation_info = 'dr_mutations_pyrazinamide')
|
||||
print("dim of dr_df:", dr_df.shape)
|
||||
print('======================================================================')
|
||||
print('End of tidy split() on dr_muts, and added an extra column relecting mut_category')
|
||||
print('======================================================================')
|
||||
|
||||
print('dim of dr_df:', dr_df.shape
|
||||
, '\n=============================================================='
|
||||
, '\nEnd of tidy split() on dr_muts, and added an extra column relecting mut_category'
|
||||
, '\n===============================================================')
|
||||
#%%
|
||||
#=========
|
||||
# DF2: other_mutations_pyrazinamdie
|
||||
|
|
@ -491,9 +496,9 @@ print('======================================================================')
|
|||
# tidy_split(): on 'other_mutations_pyrazinamide' column and remove leading white spaces
|
||||
########
|
||||
col_to_split2 = 'other_mutations_pyrazinamide'
|
||||
print ("applying second tidy split separately on df:", meta_pnca_other.shape, '\n'
|
||||
, "column name:", col_to_split2)
|
||||
print('======================================================================')
|
||||
print ('applying second tidy split separately on df:', meta_pnca_other.shape
|
||||
, '\ncolumn name:', col_to_split2
|
||||
, '\n============================================================')
|
||||
|
||||
# apply tidy_split()
|
||||
other_WF1 = tidy_split(meta_pnca_other, col_to_split2, sep = ';')
|
||||
|
|
@ -510,45 +515,46 @@ print('lengths after tidy split and extracting', gene_match, 'muts:',
|
|||
'\nexpected len:', other_pnca_count)
|
||||
|
||||
if len(other_pnca_WF1) == other_pnca_count:
|
||||
print('PASS: length of dr_pnca_WF0 match with expected length')
|
||||
print('PASS: length of dr_pnca_WF0 match with expected length
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print('FAIL: lengths mismatch')
|
||||
|
||||
print('======================================================================')
|
||||
# count the freq of "other muts" samples
|
||||
print('FAIL: lengths mismatch
|
||||
, '\n===============================================================')
|
||||
# count the freq of 'other muts' samples
|
||||
other_muts_df = other_pnca_WF1 [['id', 'other_mutations_pyrazinamide']]
|
||||
print("dim of other_muts_df:", other_muts_df.shape)
|
||||
print('dim of other_muts_df:', other_muts_df.shape)
|
||||
|
||||
# add freq column
|
||||
other_muts_df['other_sample_freq'] = other_muts_df.groupby('id')['id'].transform('count')
|
||||
print("revised dim of other_muts_df:", other_muts_df.shape)
|
||||
print('revised dim of other_muts_df:', other_muts_df.shape)
|
||||
|
||||
c2 = other_muts_df.other_sample_freq.value_counts()
|
||||
print('counting no. of sample frequency:\n', c2)
|
||||
print('======================================================================')
|
||||
print('=================================================================')
|
||||
# sanity check: length of pnca samples
|
||||
if len(other_pnca_WF1) == c2.sum():
|
||||
print('PASS: WF data has expected length',
|
||||
'\nlength of other_pnca WFO:', c2.sum() )
|
||||
print('PASS: WF data has expected length'
|
||||
, '\nlength of other_pnca WFO:', c2.sum()
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print("FAIL: Debug please!")
|
||||
print('======================================================================')
|
||||
print('FAIL: Debug please!'
|
||||
, '\n===============================================================')
|
||||
|
||||
#!!! Important !!!
|
||||
# Assign "column name" on which split was performed as an extra column
|
||||
# Assign 'column name' on which split was performed as an extra column
|
||||
# This is so you can identify if mutations are dr_type or other in the final df
|
||||
other_df = other_pnca_WF1.assign(mutation_info = 'other_mutations_pyrazinamide')
|
||||
print("dim of other_df:", other_df.shape)
|
||||
print('======================================================================')
|
||||
print('End of tidy split() on other_muts, and added an extra column relecting mut_category')
|
||||
print('======================================================================')
|
||||
print('dim of other_df:', other_df.shape
|
||||
, '\n==============================================================='
|
||||
, '\nEnd of tidy split() on other_muts, and added an extra column relecting mut_category'
|
||||
, '\n===============================================================')
|
||||
#%%
|
||||
#==========
|
||||
# Concatentating the two dfs: equivalent of rbind in R
|
||||
#==========
|
||||
#!!! important !!!
|
||||
# change column names to allow concat:
|
||||
# dr_muts.. & other_muts : "mutation"
|
||||
# dr_muts.. & other_muts : 'mutation'
|
||||
print('Now concatenating the two dfs by row')
|
||||
|
||||
dr_df.columns
|
||||
|
|
@ -559,39 +565,40 @@ other_df.columns
|
|||
other_df.rename(columns = {'other_mutations_pyrazinamide': 'mutation'}, inplace = True)
|
||||
other_df.columns
|
||||
|
||||
print('======================================================================')
|
||||
print('Now appending the two dfs:',
|
||||
'\ndr_df dim:', dr_df.shape,
|
||||
'\nother_df dim:', other_df.shape,
|
||||
'\ndr_df length:', len(dr_df),
|
||||
'\nother_df length:', len(other_df),
|
||||
'\nexpected length:', len(dr_df) + len(other_df) )
|
||||
print('======================================================================')
|
||||
print('=================================================================')
|
||||
print('Now appending the two dfs:'
|
||||
, '\ndr_df dim:', dr_df.shape
|
||||
, '\nother_df dim:', other_df.shape
|
||||
, '\ndr_df length:', len(dr_df)
|
||||
, '\nother_df length:', len(other_df)
|
||||
, '\nexpected length:', len(dr_df) + len(other_df)
|
||||
, '\n=============================================================')
|
||||
|
||||
# checking colnames before concat
|
||||
print("checking colnames BEFORE concatenating the two dfs...")
|
||||
print('checking colnames BEFORE concatenating the two dfs...')
|
||||
if (set(dr_df.columns) == set(other_df.columns)):
|
||||
print('PASS: column names match necessary for merging two dfs')
|
||||
else:
|
||||
print("FAIL: Debug please!")
|
||||
print('FAIL: Debug please!')
|
||||
|
||||
# concatenate (axis = 0): Rbind
|
||||
pnca_LF0 = pd.concat([dr_df, other_df], ignore_index = True, axis = 0)
|
||||
|
||||
# checking colnames and length after concat
|
||||
print("checking colnames AFTER concatenating the two dfs...")
|
||||
print('checking colnames AFTER concatenating the two dfs...')
|
||||
if (set(dr_df.columns) == set(pnca_LF0.columns)):
|
||||
print('PASS: column names match')
|
||||
else:
|
||||
print("FAIL: Debug please!")
|
||||
print('FAIL: Debug please!')
|
||||
|
||||
print("checking length AFTER concatenating the two dfs...")
|
||||
print('checking length AFTER concatenating the two dfs...')
|
||||
|
||||
if len(pnca_LF0) == len(dr_df) + len(other_df):
|
||||
print("PASS:length of df after concat match")
|
||||
print('PASS:length of df after concat match'
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print("FAIL: length mismatch")
|
||||
print('======================================================================')
|
||||
print('FAIL: length mismatch'
|
||||
, '\n===============================================================')
|
||||
#%%
|
||||
###########
|
||||
# This is hopefully clean data, but just double check
|
||||
|
|
@ -612,53 +619,57 @@ if len(pnca_LF0) == len(pnca_LF1):
|
|||
print('PASS: length of pnca_LF0 and pnca_LF1 match',
|
||||
'\nconfirming extraction and concatenating worked correctly')
|
||||
else:
|
||||
print('FAIL: BUT NOT FATAL!',
|
||||
'\npnca_LF0 and pnca_LF1 lengths differ',
|
||||
'\nsuggesting error in extraction process'
|
||||
' use pnca_LF1 for downstreama analysis')
|
||||
print('======================================================================')
|
||||
print('length of dfs pre and post processing...',
|
||||
'\npnca WF data (unique samples in each row):', extracted_pnca_samples,
|
||||
'\npnca LF data (unique mutation in each row):', len(pnca_LF1))
|
||||
print('======================================================================')
|
||||
print('FAIL: BUT NOT FATAL!'
|
||||
, '\npnca_LF0 and pnca_LF1 lengths differ'
|
||||
, '\nsuggesting error in extraction process'
|
||||
, ' use pnca_LF1 for downstreama analysis'
|
||||
, '\n=========================================================')
|
||||
print('length of dfs pre and post processing...'
|
||||
, '\npnca WF data (unique samples in each row):', extracted_pnca_samples
|
||||
, '\npnca LF data (unique mutation in each row):', len(pnca_LF1)
|
||||
, '\n=============================================================')
|
||||
|
||||
#%%
|
||||
# final sanity check
|
||||
print('Verifying whether extraction process worked correctly...')
|
||||
if len(pnca_LF1) == expected_rows:
|
||||
print('PASS: extraction process performed correctly',
|
||||
'\nexpected length:', expected_rows,
|
||||
'\ngot:', len(pnca_LF1),
|
||||
'\nRESULT: Total no. of mutant sequences for logo plot:', len(pnca_LF1) )
|
||||
|
||||
print('PASS: extraction process performed correctly'
|
||||
, '\nexpected length:', expected_rows
|
||||
, '\ngot:', len(pnca_LF1)
|
||||
, '\nRESULT: Total no. of mutant sequences for logo plot:', len(pnca_LF1)
|
||||
, '\n=========================================================')
|
||||
else:
|
||||
print('FAIL: extraction process has bugs',
|
||||
'\nexpected length:', expected_rows,
|
||||
'\ngot:', len(pnca_LF1),
|
||||
'\Debug please')
|
||||
print('FAIL: extraction process has bugs'
|
||||
, '\nexpected length:', expected_rows
|
||||
, '\ngot:', len(pnca_LF1)
|
||||
, ', \Debug please'
|
||||
, '\n=========================================================')
|
||||
#%%
|
||||
print('Perfmorning some more sanity checks...')
|
||||
# From LF1:
|
||||
# no. of unique muts
|
||||
distinct_muts = pnca_LF1.mutation.value_counts()
|
||||
print("Distinct mutations:", len(distinct_muts))
|
||||
print('Distinct mutations:', len(distinct_muts))
|
||||
|
||||
# no. of samples contributing the unique muta
|
||||
pnca_LF1.id.nunique()
|
||||
print("No.of samples contributing to distinct muts:", pnca_LF1.id.nunique() )
|
||||
print('No.of samples contributing to distinct muts:', pnca_LF1.id.nunique() )
|
||||
|
||||
# no. of dr and other muts
|
||||
mut_grouped = pnca_LF1.groupby('mutation_info').mutation.nunique()
|
||||
print("No.of unique dr and other muts:", pnca_LF1.groupby('mutation_info').mutation.nunique() )
|
||||
print('No.of unique dr and other muts:', pnca_LF1.groupby('mutation_info').mutation.nunique() )
|
||||
|
||||
# sanity check
|
||||
if len(distinct_muts) == mut_grouped.sum() :
|
||||
print("PASS:length of LF1 is as expected ")
|
||||
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 ...')
|
||||
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 ...'
|
||||
, '\n=========================================================')
|
||||
muts_split = list(pnca_LF1.groupby('mutation_info'))
|
||||
dr_muts = muts_split[0][1].mutation
|
||||
other_muts = muts_split[1][1].mutation
|
||||
|
|
@ -670,36 +681,40 @@ else:
|
|||
# sanity check: There should not be any common muts
|
||||
# i.e 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')
|
||||
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')
|
||||
print('PASS: NO ambiguous muts detected'
|
||||
, '\nMuts are unique to dr_ and other_ mutation class'
|
||||
, '\n=========================================================')
|
||||
|
||||
# inspect dr_muts and other muts
|
||||
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:\n', 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('Finding ambiguous muts...'
|
||||
, '\n========================================================='
|
||||
, '\nTotal no. of samples in dr_muts present in other_muts:', dr_muts.isin(other_muts).sum()
|
||||
, '\nThese are:\n', 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=========================================================')
|
||||
else:
|
||||
print('Error: ambiguous muts present, but extraction failed. Debug!')
|
||||
print('Error: ambiguous muts present, but extraction failed. Debug!'
|
||||
, '\n===============================================================')
|
||||
|
||||
print('======================================================================')
|
||||
print('Counting no. of ambiguous muts...')
|
||||
|
||||
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)),
|
||||
'list of ambiguous mutations (see below):', *common_muts, sep = '\n')
|
||||
'list of ambiguous mutations (see below):', *common_muts, sep = '\n'
|
||||
, '\n=========================================================')
|
||||
else:
|
||||
print('Error: ambiguous muts detected, but extraction failed. Debug!',
|
||||
'\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()))
|
||||
print('======================================================================')
|
||||
print('Error: ambiguous muts detected, but extraction failed. Debug!'
|
||||
, '\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=========================================================')
|
||||
|
||||
#%% clear variables
|
||||
del(id_dr, id_other, meta_data, meta_pnca_dr, meta_pnca_other, mut_grouped, muts_split, other_WF1, other_df, other_muts_df, other_pnca_count, pnca_LF0, pnca_na)
|
||||
|
||||
|
|
@ -723,18 +738,21 @@ print('Writing file: ambiguous muts',
|
|||
inspect = pnca_LF1[pnca_LF1['mutation'].isin(common_muts)]
|
||||
inspect.to_csv(outfile1)
|
||||
|
||||
print('Finished writing:', out_filename1,
|
||||
'\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())
|
||||
print('======================================================================')
|
||||
print('Finished writing:', out_filename1
|
||||
, '\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_filename1)
|
||||
|
||||
|
||||
#%% read aa dict and pull relevant info
|
||||
print('Reading aa dict and fetching1 letter aa code',
|
||||
'\nFormatting mutation in mCSM style format: {WT}<POS>{MUT}',
|
||||
'\nAdding aa properties')
|
||||
print('Reading aa dict and fetching1 letter aa code'
|
||||
, '\nFormatting mutation in mCSM style format: {WT}<POS>{MUT}'
|
||||
, '\nAdding aa properties'
|
||||
, '\n============================================================')
|
||||
|
||||
#===========
|
||||
# Split 'mutation' column into three: wild_type, position and
|
||||
# mutant_type separately. Then map three letter code to one using
|
||||
|
|
@ -743,6 +761,7 @@ print('Reading aa dict and fetching1 letter aa code',
|
|||
# Second: convert to mutation to lowercase for compatibility with dict
|
||||
#===========
|
||||
pnca_LF1['mutation'] = pnca_LF1.loc[:, 'mutation'].str.lower()
|
||||
|
||||
#=======
|
||||
# Iterate through the dict, create a lookup dict i.e
|
||||
# lookup_dict = {three_letter_code: one_letter_code}.
|
||||
|
|
@ -751,6 +770,7 @@ pnca_LF1['mutation'] = pnca_LF1.loc[:, 'mutation'].str.lower()
|
|||
# The three letter code is extracted using a string match match from the dataframe and then converted
|
||||
# to 'pandas series'since map only works in pandas series
|
||||
#=======
|
||||
|
||||
lookup_dict = dict()
|
||||
for k, v in my_aa_dict.items():
|
||||
lookup_dict[k] = v['one_letter_code']
|
||||
|
|
@ -764,7 +784,7 @@ pnca_LF1['position'] = pnca_LF1['mutation'].str.extract(r'(\d+)')
|
|||
|
||||
# clear variables
|
||||
del(k, v, wt, mut, lookup_dict)
|
||||
#print('======================================================================')
|
||||
|
||||
#=========
|
||||
# iterate through the dict, create a lookup dict that i.e
|
||||
# lookup_dict = {three_letter_code: aa_prop_water}
|
||||
|
|
@ -785,7 +805,7 @@ for k, v in my_aa_dict.items():
|
|||
|
||||
# clear variables
|
||||
del(k, v, wt, mut, lookup_dict)
|
||||
#print('======================================================================')
|
||||
|
||||
#========
|
||||
# iterate through the dict, create a lookup dict that i.e
|
||||
# lookup_dict = {three_letter_code: aa_prop_polarity}
|
||||
|
|
@ -806,7 +826,6 @@ for k, v in my_aa_dict.items():
|
|||
|
||||
# clear variables
|
||||
del(k, v, wt, mut, lookup_dict)
|
||||
#print('======================================================================')
|
||||
|
||||
#========
|
||||
# iterate through the dict, create a lookup dict that i.e
|
||||
|
|
@ -827,7 +846,6 @@ del(k, v, wt, mut, lookup_dict)
|
|||
# added two more cols
|
||||
# clear variables
|
||||
#del(k, v, wt, mut, lookup_dict)
|
||||
#print('======================================================================')
|
||||
|
||||
#========
|
||||
# iterate through the dict, create a lookup dict that i.e
|
||||
|
|
@ -847,15 +865,16 @@ for k, v in my_aa_dict.items():
|
|||
# added two more cols
|
||||
# clear variables
|
||||
del(k, v, wt, mut, lookup_dict)
|
||||
print('======================================================================')
|
||||
|
||||
########
|
||||
# combine the wild_type+poistion+mutant_type columns to generate
|
||||
# Mutationinformation (matches mCSM output field)
|
||||
# Remember to use .map(str) for int col types to allow string concatenation
|
||||
#########
|
||||
pnca_LF1['Mutationinformation'] = pnca_LF1['wild_type'] + pnca_LF1.position.map(str) + pnca_LF1['mutant_type']
|
||||
print('Created column: Mutationinformation')
|
||||
print('======================================================================')
|
||||
print('Created column: Mutationinformation'
|
||||
, '\n===============================================================')
|
||||
|
||||
#%% Write file: mCSM muts
|
||||
snps_only = pd.DataFrame(pnca_LF1['Mutationinformation'].unique())
|
||||
snps_only.head()
|
||||
|
|
@ -867,47 +886,48 @@ pos_only = pd.DataFrame(pnca_LF1['position'].unique())
|
|||
|
||||
print('Checking NA in snps...')# should be 0
|
||||
if snps_only.Mutationinformation.isna().sum() == 0:
|
||||
print ('PASS: NO NAs/missing entries for SNPs')
|
||||
print ('PASS: NO NAs/missing entries for SNPs'
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print('FAIL: SNP has NA, Possible mapping issues from dict?',
|
||||
'\nDebug please!')
|
||||
print('======================================================================')
|
||||
print('FAIL: SNP has NA, Possible mapping issues from dict?'
|
||||
, '\nDebug please!'
|
||||
, '\n=========================================================')
|
||||
|
||||
out_filename2 = gene.lower() + '_' + 'mcsm_snps.csv'
|
||||
outfile2 = outdir + '/' + out_filename2
|
||||
|
||||
print('Writing file: mCSM style muts',
|
||||
'\nFilename:', out_filename2,
|
||||
'\nPath:', outdir,
|
||||
'\nmutation format (SNP): {WT}<POS>{MUT}',
|
||||
'\nNo. of distinct muts:', len(snps_only),
|
||||
'\nNo. of distinct positions:', len(pos_only))
|
||||
print('Writing file: mCSM style muts'
|
||||
, '\nFilename:', out_filename2
|
||||
, '\nPath:', outdir
|
||||
, '\nmutation format (SNP): {WT}<POS>{MUT}'
|
||||
, '\nNo. of distinct muts:', len(snps_only)
|
||||
, '\nNo. of distinct positions:', len(pos_only)
|
||||
, '\n=============================================================')
|
||||
|
||||
snps_only.to_csv(outfile2, header = False, index = False)
|
||||
|
||||
print('Finished writing:', out_filename2,
|
||||
'\nNo. of rows:', len(snps_only),
|
||||
'\nNo. of cols:', len(snps_only.columns))
|
||||
print('======================================================================')
|
||||
print('Finished writing:', out_filename2
|
||||
, '\nNo. of rows:', len(snps_only)
|
||||
, '\nNo. of cols:', len(snps_only.columns)
|
||||
, '\n=============================================================')
|
||||
del(out_filename2)
|
||||
|
||||
|
||||
#%% Write file: pnca_metadata (i.e pnca_LF1)
|
||||
# where each row has UNIQUE mutations NOT unique sample ids
|
||||
out_filename3 = gene.lower() + '_' + 'metadata.csv'
|
||||
outfile3 = outdir + '/' + out_filename3
|
||||
print('Writing file: LF formatted data',
|
||||
'\nFilename:', out_filename3,
|
||||
'\nPath:', outdir)
|
||||
print('Writing file: LF formatted data'
|
||||
, '\nFilename:', out_filename3
|
||||
, '\nPath:', outdir
|
||||
, '\n============================================================')
|
||||
|
||||
pnca_LF1.to_csv(outfile3, header = True, index = False)
|
||||
print('Finished writing:', out_filename3,
|
||||
'\nNo. of rows:', len(pnca_LF1),
|
||||
'\nNo. of cols:', len(pnca_LF1.columns) )
|
||||
print('======================================================================')
|
||||
print('Finished writing:', out_filename3
|
||||
, '\nNo. of rows:', len(pnca_LF1)
|
||||
, '\nNo. of cols:', len(pnca_LF1.columns)
|
||||
, '\n=============================================================')
|
||||
del(out_filename3)
|
||||
|
||||
|
||||
#%% write file: mCSM style but with repitions for MSA and logo plots
|
||||
all_muts_msa = pd.DataFrame(pnca_LF1['Mutationinformation'])
|
||||
all_muts_msa.head()
|
||||
|
|
@ -930,11 +950,12 @@ all_muts_msa_sorted.head()
|
|||
|
||||
print('Checking NA in snps...')# should be 0
|
||||
if all_muts_msa.Mutationinformation.isna().sum() == 0:
|
||||
print ('PASS: NO NAs/missing entries for SNPs')
|
||||
print ('PASS: NO NAs/missing entries for SNPs'
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print('FAIL: SNP has NA, Possible mapping issues from dict?',
|
||||
'\nDebug please!')
|
||||
print('======================================================================')
|
||||
print('FAIL: SNP has NA, Possible mapping issues from dict?'
|
||||
, '\nDebug please!'
|
||||
, '\n=========================================================')
|
||||
|
||||
out_filename4 = gene.lower() + '_' + 'all_muts_msa.csv'
|
||||
outfile4 = outdir + '/' + out_filename4
|
||||
|
|
@ -948,12 +969,12 @@ print('Writing file: mCSM style muts for msa',
|
|||
|
||||
all_muts_msa_sorted.to_csv(outfile4, header = False, index = False)
|
||||
|
||||
print('Finished writing:', out_filename4,
|
||||
'\nNo. of rows:', len(all_muts_msa),
|
||||
'\nNo. of cols:', len(all_muts_msa.columns) )
|
||||
print('======================================================================')
|
||||
del(out_filename4)
|
||||
print('Finished writing:', out_filename4
|
||||
, '\nNo. of rows:', len(all_muts_msa)
|
||||
, '\nNo. of cols:', len(all_muts_msa.columns)
|
||||
, '\n=============================================================')
|
||||
|
||||
del(out_filename4)
|
||||
|
||||
#%% write file for mutational positions
|
||||
# count how many positions this corresponds to
|
||||
|
|
@ -971,22 +992,22 @@ pos_only_sorted = pos_only.sort_values(by = 'position', ascending = True)
|
|||
out_filename5 = gene.lower() + '_' + 'mutational_positons.csv'
|
||||
outfile5 = outdir + '/' + out_filename5
|
||||
|
||||
print('Writing file: mutational positions',
|
||||
'\nNo. of distinct positions:', len(pos_only_sorted),
|
||||
'\nFilename:', out_filename5,
|
||||
'\nPath:', outdir)
|
||||
print('Writing file: mutational positions'
|
||||
, '\nNo. of distinct positions:', len(pos_only_sorted)
|
||||
, '\nFilename:', out_filename5
|
||||
, '\nPath:', outdir
|
||||
, '\n=============================================================')
|
||||
|
||||
pos_only_sorted.to_csv(outfile5, header = True, index = False)
|
||||
|
||||
print('Finished writing:', out_filename5,
|
||||
'\nNo. of rows:', len(pos_only_sorted),
|
||||
'\nNo. of cols:', len(pos_only_sorted.columns) )
|
||||
print('======================================================================')
|
||||
print('Finished writing:', out_filename5
|
||||
, '\nNo. of rows:', len(pos_only_sorted)
|
||||
, '\nNo. of cols:', len(pos_only_sorted.columns)
|
||||
, '\n=============================================================')
|
||||
|
||||
del(out_filename5)
|
||||
|
||||
|
||||
#%% end of script
|
||||
#=======================================================================
|
||||
print(u'\u2698' * 50,
|
||||
'\nEnd of script: Data extraction and writing files'
|
||||
'\n' + u'\u2698' * 50 )
|
||||
#%%
|
||||
#%% end of script
|
||||
|
|
|
|||
|
|
@ -55,13 +55,12 @@ indir = datadir + '/' + drug + '/' + 'output'
|
|||
in_filename = gene.lower() +'.dssp'
|
||||
infile = indir + '/' + in_filename
|
||||
print('Input filename:', in_filename
|
||||
, '\nInput path:', indir)
|
||||
, '\nInput path:', indir
|
||||
, '\n============================================================')
|
||||
|
||||
# specify PDB chain
|
||||
my_chain = 'A'
|
||||
|
||||
print('======================================================================')
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
|
|
@ -70,9 +69,9 @@ out_filename = gene.lower() + '_dssp.csv'
|
|||
outfile = outdir + '/' + out_filename
|
||||
print('Output filename:', out_filename
|
||||
, '\nOutput path:', outdir
|
||||
,'\nOutfile: ', outfile)
|
||||
, '\nOutfile: ', outfile
|
||||
, '\n=============================================================')
|
||||
|
||||
print('======================================================================')
|
||||
#%% end of variable assignment for input and output files
|
||||
#=======================================================================
|
||||
# Process dssp output and extract into df
|
||||
|
|
@ -96,14 +95,15 @@ dssp_df.columns
|
|||
#%% Write ouput csv file
|
||||
print('Writing file:', outfile
|
||||
, '\nFilename:', out_filename
|
||||
, '\nPath:', outdir)
|
||||
, '\nPath:', outdir
|
||||
, '\n=============================================================')
|
||||
|
||||
# write to csv
|
||||
dssp_df.to_csv(outfile, header=True, index = False)
|
||||
|
||||
print('Finished writing:', out_filename
|
||||
, '\nNo. of rows:', len(dssp_df)
|
||||
, '\nNo. of cols:', len(dssp_df.columns))
|
||||
print('======================================================================')
|
||||
, '\nNo. of cols:', len(dssp_df.columns)
|
||||
, '\n==============================================================')
|
||||
#%% end of script
|
||||
#=======================================================================
|
||||
|
|
|
|||
|
|
@ -56,9 +56,8 @@ indir = datadir + '/' + drug + '/' + 'input'
|
|||
in_filename = '3pl1.fasta.txt'
|
||||
infile = indir + '/' + in_filename
|
||||
print('Input filename:', in_filename
|
||||
, '\nInput path:', indir)
|
||||
|
||||
print('======================================================================')
|
||||
, '\nInput path:', indir
|
||||
, '\n============================================================')
|
||||
|
||||
#=======
|
||||
# output
|
||||
|
|
@ -67,9 +66,8 @@ outdir = datadir + '/' + drug + '/' + 'output'
|
|||
out_filename = gene.lower() + '_kd.csv'
|
||||
outfile = outdir + '/' + out_filename
|
||||
print('Output filename:', out_filename
|
||||
, '\nOutput path:', outdir)
|
||||
|
||||
print('======================================================================')
|
||||
, '\nOutput path:', outdir
|
||||
, '\n=============================================================')
|
||||
#%% end of variable assignment for input and output files
|
||||
#=======================================================================
|
||||
#%%specify window size for hydropathy profile computation
|
||||
|
|
@ -96,7 +94,7 @@ print('Sequence Length:', num_residues)
|
|||
print('kd_values Length:',len(kd_values))
|
||||
print('Window Length:', my_window)
|
||||
print('Window Offset:', offset)
|
||||
print('======================================================================')
|
||||
print('=================================================================')
|
||||
print('Checking:len(kd values) is as expected for the given window size & offset...')
|
||||
expected_length = num_residues - (my_window - offset)
|
||||
if len(kd_values) == expected_length:
|
||||
|
|
@ -104,9 +102,8 @@ if len(kd_values) == expected_length:
|
|||
else:
|
||||
print('FAIL: length mismatch'
|
||||
,'\nExpected length:', expected_length
|
||||
,'\nActual length:', len(kd_values))
|
||||
|
||||
print('======================================================================')
|
||||
,'\nActual length:', len(kd_values)
|
||||
, '\n=========================================================')
|
||||
#%% make 2 dfs; 1) aa sequence and 2) kd_values. Then reset index for each df
|
||||
# which will allow easy merging of the two dfs.
|
||||
|
||||
|
|
@ -138,10 +135,11 @@ kd_df = pd.concat([dfSeq, dfVals], axis = 1)
|
|||
#============================
|
||||
kd_df = kd_df.rename_axis('position')
|
||||
kd_df.head
|
||||
print('======================================================================')
|
||||
print('=================================================================')
|
||||
|
||||
print('position col i.e. index should be numeric
|
||||
, '\n===============================================================')
|
||||
|
||||
print('position col i.e. index should be numeric')
|
||||
print('======================================================================')
|
||||
if kd_df.index.dtype == 'int64':
|
||||
print('PASS: position col is numeric'
|
||||
, '\ndtype is:', kd_df.index.dtype)
|
||||
|
|
@ -150,19 +148,20 @@ else:
|
|||
, '\nConverting to numeric')
|
||||
kd_df.index.astype('int64')
|
||||
print('Checking dtype for after conversion:\n'
|
||||
,'\ndtype is:', kd_df.index.dtype)
|
||||
|
||||
print('======================================================================')
|
||||
, '\ndtype is:', kd_df.index.dtype
|
||||
, '\n=========================================================')
|
||||
#%% write file
|
||||
print('Writing file:', out_filename
|
||||
, '\nFilename:', out_filename
|
||||
, '\nPath:', outdir)
|
||||
, '\nPath:', outdir
|
||||
, '\n=============================================================')
|
||||
|
||||
kd_df.to_csv(outfile, header = True, index = True)
|
||||
|
||||
print('Finished writing:', out_filename
|
||||
, '\nNo. of rows:', len(kd_df)
|
||||
, '\nNo. of cols:', len(kd_df.columns))
|
||||
, '\nNo. of cols:', len(kd_df.columns)
|
||||
, '\n=============================================================')
|
||||
|
||||
#%% plot
|
||||
# http://www.dalkescientific.com/writings/NBN/plotting.html
|
||||
|
|
@ -176,7 +175,6 @@ xlabel('Residue Number')
|
|||
ylabel('Hydrophobicity')
|
||||
title('K&D Hydrophobicity for ' + id)
|
||||
show()
|
||||
|
||||
print('======================================================================')
|
||||
#%% end of script
|
||||
#=======================================================================
|
||||
|
|
|
|||
|
|
@ -46,7 +46,8 @@ indir = datadir + '/' + drug + '/' + 'input'
|
|||
in_filename = 'merged_df3.csv'
|
||||
infile = outdir + '/' + in_filename
|
||||
print('Input filename: ', in_filename
|
||||
, '\nInput path: ', indir)
|
||||
, '\nInput path: ', indir
|
||||
, '\n============================================================')
|
||||
|
||||
#=======
|
||||
# output
|
||||
|
|
@ -56,7 +57,8 @@ outdir = datadir + '/' + drug + '/' + 'output'
|
|||
out_filename = 'mut_elec_changes.txt'
|
||||
outfile = outdir + '/' + out_filename
|
||||
print('Output filename: ', out_filename
|
||||
, '\nOutput path: ', outdir)
|
||||
, '\nOutput path: ', outdir
|
||||
, '\n============================================================')
|
||||
|
||||
#%% end of variable assignment for input and output files
|
||||
#=======================================================================
|
||||
|
|
@ -65,10 +67,11 @@ print('Reading input file (merged file):', infile)
|
|||
|
||||
comb_df = pd.read_csv(infile, sep = ',')
|
||||
|
||||
print('Input filename: ', in_filename,
|
||||
'\nPath :', outdir,
|
||||
'\nNo. of rows: ', len(comb_df),
|
||||
'\nNo. of cols: ', infile)
|
||||
print('Input filename: ', in_filename
|
||||
, '\nPath :', outdir
|
||||
, '\nNo. of rows: ', len(comb_df)
|
||||
, '\nNo. of cols: ', infile
|
||||
, '\n============================================================')
|
||||
|
||||
# column names
|
||||
list(comb_df.columns)
|
||||
|
|
@ -81,15 +84,18 @@ df = comb_df.drop_duplicates(['Mutationinformation'], keep = 'first')
|
|||
|
||||
total_muts = df.Mutationinformation.nunique()
|
||||
#df.Mutationinformation.count()
|
||||
print('Total mutations associated with structure: ', total_muts)
|
||||
print('Total mutations associated with structure: ', total_muts
|
||||
, '\n===============================================================')
|
||||
|
||||
#%% combine aa_calcprop cols so that you can count the changes as value_counts
|
||||
# check if all muts have been categorised
|
||||
print('Checking if all muts have been categorised: ')
|
||||
if df['wt_calcprop'].isna().sum() == 0 & df['mut_calcprop'].isna().sum():
|
||||
print('PASS: No. NA detected i.e all muts have aa prop associated')
|
||||
print('PASS: No. NA detected i.e all muts have aa prop associated'
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print('FAIL: NAs detected i.e some muts remain unclassified')
|
||||
print('FAIL: NAs detected i.e some muts remain unclassified'
|
||||
, '\n===============================================================')
|
||||
|
||||
df['wt_calcprop'].head()
|
||||
df['mut_calcprop'].head()
|
||||
|
|
@ -151,11 +157,11 @@ print('======================\n'
|
|||
, '\n============================\n'
|
||||
, all_prop_change)
|
||||
|
||||
print('========================================================================'
|
||||
print('================================================================='
|
||||
, '\nTotal number of electrostatic changes resulting from Mtation is (%):', elec_changes
|
||||
, '\nTotal no. of muts: ', total_muts
|
||||
, '\nTotal no. of changed muts: ', all_prop_change.mut_count.sum()
|
||||
, '\nTotal no. of unchanged muts: ', no_change_muts.mut_count.sum()
|
||||
, '\n=========================================================================')
|
||||
, '\n===================================================================')
|
||||
#%% end of script
|
||||
#=======================================================================
|
||||
|
|
|
|||
|
|
@ -48,9 +48,8 @@ indir = datadir + '/' + drug + '/' + 'output'
|
|||
in_filename = '3pl1_rd.tsv'
|
||||
infile = indir + '/' + in_filename
|
||||
print('Input filename:', in_filename
|
||||
, '\nInput path:', indir)
|
||||
|
||||
print('======================================================================')
|
||||
, '\nInput path:', indir
|
||||
, '\n=============================================================')
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
|
|
@ -58,9 +57,9 @@ outdir = datadir + '/' + drug + '/' + 'output'
|
|||
out_filename = gene.lower() + '_rd.csv'
|
||||
outfile = outdir + '/' + out_filename
|
||||
print('Output filename:', out_filename
|
||||
, '\nOutput path:', outdir)
|
||||
, '\nOutput path:', outdir
|
||||
, '\n=============================================================')
|
||||
|
||||
print('======================================================================')
|
||||
#%% end of variable assignment for input and output files
|
||||
#=======================================================================
|
||||
#%% Read input file
|
||||
|
|
@ -69,9 +68,8 @@ print('Reading input file:', infile
|
|||
, '\nNo. of rows:', len(rd_data)
|
||||
, '\nNo. of cols:', len(rd_data.columns))
|
||||
|
||||
print('Column names:', rd_data.columns)
|
||||
|
||||
print('======================================================================')
|
||||
print('Column names:', rd_data.columns
|
||||
, '\n===============================================================')
|
||||
#========================
|
||||
# creating position col
|
||||
#========================
|
||||
|
|
@ -85,8 +83,8 @@ rd_data['position'].dtype
|
|||
|
||||
print('Extracted residue num from index and assigned as a column:'
|
||||
, '\ncolumn name: position'
|
||||
, '\ntotal no. of cols now:', len(rd_data.columns))
|
||||
print('======================================================================')
|
||||
, '\ntotal no. of cols now:', len(rd_data.columns)
|
||||
, '\n=============================================================')
|
||||
|
||||
#========================
|
||||
# Renaming amino-acid
|
||||
|
|
@ -95,9 +93,8 @@ print('======================================================================')
|
|||
print('Renaming columns:'
|
||||
, '\ncolname==> # chain:residue: wt_3letter_caps'
|
||||
, '\nYES... the column name *actually* contains a # ..!'
|
||||
,'\ncolname==> all-atom: rd_values')
|
||||
|
||||
print('======================================================================')
|
||||
, '\ncolname==> all-atom: rd_values'
|
||||
, '\n=============================================================')
|
||||
|
||||
rd_data.rename(columns = {'# chain:residue':'wt_3letter_caps', 'all-atom':'rd_values'}, inplace = True)
|
||||
print('Column names:', rd_data.columns)
|
||||
|
|
@ -118,21 +115,21 @@ if len(rd_df) == len(rd_data):
|
|||
else:
|
||||
print('FAIL: no. of rows mimatch'
|
||||
, '\nExpected no. of rows:', len(rd_data)
|
||||
,'\nGot no. of rows:', len(rd_df))
|
||||
|
||||
print('======================================================================')
|
||||
, '\nGot no. of rows:', len(rd_df)
|
||||
, '\n=========================================================')
|
||||
|
||||
#%% write file
|
||||
print('Writing file:'
|
||||
, '\nFilename:', out_filename
|
||||
, '\nPath:', outdir)
|
||||
, '\nPath:', outdir
|
||||
, '\n=============================================================')
|
||||
|
||||
rd_df.to_csv(outfile, header = True, index = False)
|
||||
|
||||
print('======================================================================')
|
||||
print('Finished writing:', out_filename
|
||||
, '\nNo. of rows:', len(rd_df)
|
||||
, '\nNo. of cols:', len(rd_df.columns))
|
||||
, '\nNo. of cols:', len(rd_df.columns)
|
||||
, '\n=============================================================')
|
||||
|
||||
#%% end of script
|
||||
#=======================================================================
|
||||
|
|
|
|||
|
|
@ -44,7 +44,8 @@ indir = datadir + '/' + drug + 'input'
|
|||
in_filename = 'aa_codes.csv'
|
||||
infile = indir + '/' + in_filename
|
||||
print('Input filename:', in_filename
|
||||
, '\nInput path:', indir)
|
||||
, '\nInput path:', indir
|
||||
, '\n============================================================')
|
||||
|
||||
#=======
|
||||
# output: No output
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue