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tidy code and saving work for the day

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Tanushree Tunstall 2020-03-26 17:58:39 +00:00
parent f0becbe386
commit 0eaff73114
6 changed files with 307 additions and 284 deletions
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meta_data_analysis/data_extraction.py
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@ -1,10 +1,10 @@
#!/usr/bin/env python3 #!/usr/bin/env python3
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" '''
Created on Tue Aug 6 12:56:03 2019 Created on Tue Aug 6 12:56:03 2019
@author: tanu @author: tanu
""" '''
# FIXME: include error checking to enure you only # FIXME: include error checking to enure you only
# concentrate on positions that have structural info? # concentrate on positions that have structural info?
@ -67,7 +67,8 @@ datadir = homedir + '/' + 'git/Data'
in_filename = 'original_tanushree_data_v2.csv' in_filename = 'original_tanushree_data_v2.csv'
infile = datadir + '/' + in_filename infile = datadir + '/' + in_filename
print('Input filename: ', in_filename print('Input filename: ', in_filename
, '\nInput path: ', indir) , '\nInput path: ', indir
, '\n============================================================')
#======= #=======
# output # output
@ -76,7 +77,8 @@ print('Input filename: ', in_filename
# output filenames in respective sections at the time of outputting files # output filenames in respective sections at the time of outputting files
outdir = datadir + '/' + drug + '/' + 'output' outdir = datadir + '/' + drug + '/' + 'output'
print('Output filename: in the respective sections' print('Output filename: in the respective sections'
, '\nOutput path: ', outdir) , '\nOutput path: ', outdir
, '\n=============================================================')
#%%end of variable assignment for input and output files #%%end of variable assignment for input and output files
#======================================================================= #=======================================================================
@ -101,21 +103,22 @@ del(master_data)
# checks and results # checks and results
total_samples = meta_data['id'].nunique() total_samples = meta_data['id'].nunique()
print('RESULT: Total samples:', total_samples) print('RESULT: Total samples:', total_samples
print('======================================================================') , '\n===========================================================')
# counts NA per column # counts NA per column
meta_data.isna().sum() meta_data.isna().sum()
print('No. of NAs/column:' + '\n', meta_data.isna().sum()) print('No. of NAs/column:' + '\n', meta_data.isna().sum()
print('======================================================================') , '\n===========================================================')
# glance # glance
meta_data.head() meta_data.head()
# equivalent of table in R # equivalent of table in R
# pyrazinamide counts # pyrazinamide counts
meta_data.pyrazinamide.value_counts() meta_data.pyrazinamide.value_counts()
print('RESULT: Sus and Res samples:\n', meta_data.pyrazinamide.value_counts()) print('RESULT: Sus and Res samples:\n', meta_data.pyrazinamide.value_counts()
print('======================================================================') , '\n===========================================================')
# clear variables # clear variables
del(indir, in_filename,infile) 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() na_count = meta_data['dr_mutations_pyrazinamide'].isna().sum()
if len(clean_df) == (total_samples - na_count): if len(clean_df) == (total_samples - na_count):
print('PASS: clean_df extracted: length is', len(clean_df), print('PASS: clean_df extracted: length is', len(clean_df)
'\nNo.of NA s=', na_count, '/', total_samples) , '\nNo.of NAs =', na_count, '/', total_samples
, '\n==========================================================')
else: else:
print('FAIL: dropping NA failed') print('FAIL: dropping NA failed'
print('======================================================================') , '\n==========================================================')
dr_pnca_count = 0 dr_pnca_count = 0
wt = 0 wt = 0
@ -169,7 +173,7 @@ print('RESULTS:')
print('Total WT in dr_mutations_pyrazinamide:', wt) print('Total WT in dr_mutations_pyrazinamide:', wt)
print('Total matches of', gene_match, 'in dr_mutations_pyrazinamide:', dr_pnca_count) 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('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) 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() na_count = meta_data['other_mutations_pyrazinamide'].isna().sum()
if len(clean_df) == (total_samples - na_count): if len(clean_df) == (total_samples - na_count):
print('PASS: clean_df extracted: length is', len(clean_df), print('PASS: clean_df extracted: length is', len(clean_df)
'\nNo.of NA s=', na_count, '/', total_samples) , '\nNo.of NA s=', na_count, '/', total_samples
, '\n=========================================================')
else: else:
print('FAIL: dropping NA failed') print('FAIL: dropping NA failed'
print('======================================================================') , '\n=========================================================')
other_pnca_count = 0 other_pnca_count = 0
wt_other = 0 wt_other = 0
@ -213,7 +218,7 @@ print('RESULTS:')
print('Total WT in other_mutations_pyrazinamide:', wt_other) print('Total WT in other_mutations_pyrazinamide:', wt_other)
print('Total matches of', gene_match, 'in other_mutations_pyrazinamide:', other_pnca_count) 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('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 ) print('Predicting total no. of rows in your curated df:', dr_pnca_count + other_pnca_count )
expected_rows = 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 # 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('Extracting dr_muts in a dr_mutations_pyrazinamide with other meta_data')
print('gene to extract:', gene_match ) print('gene to extract:', gene_match )
@ -241,9 +246,9 @@ meta_data_dr = meta_data[['id'
, 'pyrazinamide' , 'pyrazinamide'
, 'dr_mutations_pyrazinamide' , 'dr_mutations_pyrazinamide'
]] ]]
print("expected dim should be:", len(meta_data), (len(meta_data.columns)-1) ) print('expected dim should be:', len(meta_data), (len(meta_data.columns)-1) )
print("actual dim:", meta_data_dr.shape ) print('actual dim:', meta_data_dr.shape
print('======================================================================') , '\n===============================================================')
# Extract within this the gene of interest using string match # 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)] #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) ) '\nactual len =', len(meta_pnca_dr) )
if len(id_dr) == len(meta_pnca_dr): if len(id_dr) == len(meta_pnca_dr):
print('PASS: lengths match') print('PASS: lengths match'
, '\n===============================================================')
else: else:
print('FAIL: length mismatch') print('FAIL: length mismatch'
print('======================================================================') , '\n===============================================================')
dr_id = pd.Series(dr_id) dr_id = pd.Series(dr_id)
#================= #=================
# other mutations: extract pncA_p. entries # other mutations: extract pncA_p. entries
#================== #==================
print('======================================================================')
print('Extracting dr_muts in a other_mutations_pyrazinamide with other meta_data') print('Extracting dr_muts in a other_mutations_pyrazinamide with other meta_data')
# FIXME: replace pyrazinamide with variable containing the drug name # FIXME: replace pyrazinamide with variable containing the drug name
# !!! important !!! # !!! important !!!
@ -279,9 +284,9 @@ meta_data_other = meta_data[['id'
, 'other_mutations_pyrazinamide' , 'other_mutations_pyrazinamide'
]] ]]
print("expected dim should be:", len(meta_data), (len(meta_data.columns)-1) ) print('expected dim should be:', len(meta_data), (len(meta_data.columns)-1) )
print("actual dim:", meta_data_other.shape ) print('actual dim:', meta_data_other.shape
print('======================================================================') , '\n===============================================================')
# Extract within this the gene of interest using string match # 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)] 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) ) '\nactual len =', len(meta_pnca_other) )
if len(id_other) == len(meta_pnca_other): if len(id_other) == len(meta_pnca_other):
print('PASS: lengths match') print('PASS: lengths match'
, '\n==============================================================')
else: else:
print('FAIL: length mismatch') print('FAIL: length mismatch'
print('======================================================================') , '\n===============================================================')
other_id = pd.Series(other_id) other_id = pd.Series(other_id)
#%% Find common IDs #%% 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 # 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 ) expected_pnca_samples = ( len(meta_pnca_dr) + len(meta_pnca_other) - common_mut_ids )
print("expected no. of pnca samples:", expected_pnca_samples) print('expected no. of pnca samples:', expected_pnca_samples)
print('======================================================================') print('=================================================================')
#%% write file #%% write file
#print(outdir) #print(outdir)
out_filename0 = gene.lower() + '_' + 'common_ids.csv' out_filename0 = gene.lower() + '_' + 'common_ids.csv'
outfile0 = outdir + '/' + out_filename0 outfile0 = outdir + '/' + out_filename0
#FIXME: CHECK line len(common_ids) #FIXME: CHECK line len(common_ids)
print('Writing file: common ids:', print('Writing file: common ids:'
'\nFilename:', out_filename0, , '\nFilename:', out_filename0
'\nPath:', outdir, , '\nPath:', outdir
'\nExpected no. of rows:', len(common_ids) ) , '\nExpected no. of rows:', len(common_ids)
, '\n=============================================================')
common_ids.to_csv(outfile0) common_ids.to_csv(outfile0)
print('======================================================================')
del(out_filename0) del(out_filename0)
# clear variables # clear variables
del(dr_id, other_id, meta_data_dr, meta_data_other, common_ids, common_mut_ids, common_ids2) 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.*' #%% Now extract 'all' pncA mutations: i.e 'pncA_p.*'
print("extracting all pncA mutations from dr_ and other_ cols using string match:", gene_match) 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) | 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) ] 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() 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('======================================================================') print('======================================================================')
# sanity check: length of pnca samples # sanity check: length of pnca samples
print('Performing sanity check:') print('Performing sanity check:')
if extracted_pnca_samples == expected_pnca_samples: if extracted_pnca_samples == expected_pnca_samples:
print('No. of pnca samples:', len(meta_pnca_all), print('No. of pnca samples:', len(meta_pnca_all)
'\nPASS: expected & actual no. of pnca samples match') , '\nPASS: expected & actual no. of pnca samples match'
, '\n=========================================================')
else: else:
print("FAIL: Debug please!") print('FAIL: Debug please!'
print('======================================================================') , '\n===============================================================')
# count NA in pyrazinamide column # count NA in pyrazinamide column
pnca_na = meta_pnca_all['pyrazinamide'].isna().sum() 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 # use it later to check number of complete samples from LF data
comp_pnca_samples = len(meta_pnca_all) - pnca_na comp_pnca_samples = len(meta_pnca_all) - pnca_na
print("comp pnca samples tested for pza:", comp_pnca_samples) print('comp pnca samples tested for pza:', comp_pnca_samples)
print('======================================================================') print('=================================================================')
# Comment: This is still dirty data since these # Comment: This is still dirty data since these
# are samples that have pncA_p. muts, but can have others as well # are samples that have pncA_p. muts, but can have others as well
# since the format for mutations is mut1; mut2, etc. # 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', 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.') , '\nsince the format for mutations is mut1; mut2, etc.'
print('======================================================================') , '\n=============================================================')
#%% tidy_split():Function to split mutations on specified delimiter: ';' #%% tidy_split():Function to split mutations on specified delimiter: ';'
#https://stackoverflow.com/questions/41476150/removing-space-from-dataframe-columns-in-pandas #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') print('Performing tidy_spllit(): to separate the mutations into indivdual rows')
# define the split function # define the split function
def tidy_split(df, column, sep='|', keep=False): def tidy_split(df, column, sep='|', keep=False):
""" '''
Split the values of a column and expand so the new DataFrame has one split 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. value per row. Filters rows where the column is missing.
@ -404,7 +410,7 @@ def tidy_split(df, column, sep='|', keep=False):
------- -------
pandas.DataFrame pandas.DataFrame
Returns a dataframe with the same columns as `df`. Returns a dataframe with the same columns as `df`.
""" '''
indexes = list() indexes = list()
new_values = list() new_values = list()
#df = df.dropna(subset=[column])#!!!!-----see this incase you need to uncomment based on use case #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 # tidy_split(): on 'dr_mutations_pyrazinamide' column and remove leading white spaces
######## ########
col_to_split1 = 'dr_mutations_pyrazinamide' col_to_split1 = 'dr_mutations_pyrazinamide'
print ('Firstly, applying tidy split on dr df:', meta_pnca_dr.shape, print ('Firstly, applying tidy split on dr df:', meta_pnca_dr.shape
'\ncolumn name:', col_to_split1) , '\ncolumn name:', col_to_split1
print('======================================================================') , '\n============================================================')
# apply tidy_split() # apply tidy_split()
dr_WF0 = tidy_split(meta_pnca_dr, col_to_split1, sep = ';') dr_WF0 = tidy_split(meta_pnca_dr, col_to_split1, sep = ';')
# remove leading white space else these are counted as distinct mutations as well # 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) , '\nexpected len:', dr_pnca_count)
if len(dr_pnca_WF0) == 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: 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']] 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 # 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.groupby('id')['id'].transform('count')
#dr_muts_df['dr_sample_freq'] = dr_muts_df.loc[dr_muts_df.groupby('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() c1 = dr_muts_df.dr_sample_freq.value_counts()
print('counting no. of sample frequency:\n', c1) print('counting no. of sample frequency:\n', c1)
print('======================================================================') print('=================================================================')
# sanity check: length of pnca samples # sanity check: length of pnca samples
if len(dr_pnca_WF0) == c1.sum(): if len(dr_pnca_WF0) == c1.sum():
print('PASS: WF data has expected length', print('PASS: WF data has expected length'
'\nlength of dr_pnca WFO:', c1.sum() ) , '\nlength of dr_pnca WFO:', c1.sum()
, '\n===============================================================')
else: else:
print("FAIL: Debug please!") print('FAIL: Debug please!'
, '\n===============================================================')
print('======================================================================')
#!!! Important !!! #!!! 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 # 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') dr_df = dr_pnca_WF0.assign(mutation_info = 'dr_mutations_pyrazinamide')
print("dim of dr_df:", dr_df.shape) print('dim of dr_df:', dr_df.shape
print('======================================================================') , '\n=============================================================='
print('End of tidy split() on dr_muts, and added an extra column relecting mut_category') , '\nEnd of tidy split() on dr_muts, and added an extra column relecting mut_category'
print('======================================================================') , '\n===============================================================')
#%% #%%
#========= #=========
# DF2: other_mutations_pyrazinamdie # DF2: other_mutations_pyrazinamdie
@ -491,9 +496,9 @@ print('======================================================================')
# tidy_split(): on 'other_mutations_pyrazinamide' column and remove leading white spaces # tidy_split(): on 'other_mutations_pyrazinamide' column and remove leading white spaces
######## ########
col_to_split2 = 'other_mutations_pyrazinamide' col_to_split2 = 'other_mutations_pyrazinamide'
print ("applying second tidy split separately on df:", meta_pnca_other.shape, '\n' print ('applying second tidy split separately on df:', meta_pnca_other.shape
, "column name:", col_to_split2) , '\ncolumn name:', col_to_split2
print('======================================================================') , '\n============================================================')
# apply tidy_split() # apply tidy_split()
other_WF1 = tidy_split(meta_pnca_other, col_to_split2, sep = ';') 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) '\nexpected len:', other_pnca_count)
if len(other_pnca_WF1) == 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: else:
print('FAIL: lengths mismatch') print('FAIL: lengths mismatch
, '\n===============================================================')
print('======================================================================') # count the freq of 'other muts' samples
# count the freq of "other muts" samples
other_muts_df = other_pnca_WF1 [['id', 'other_mutations_pyrazinamide']] 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 # add freq column
other_muts_df['other_sample_freq'] = other_muts_df.groupby('id')['id'].transform('count') 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() c2 = other_muts_df.other_sample_freq.value_counts()
print('counting no. of sample frequency:\n', c2) print('counting no. of sample frequency:\n', c2)
print('======================================================================') print('=================================================================')
# sanity check: length of pnca samples # sanity check: length of pnca samples
if len(other_pnca_WF1) == c2.sum(): if len(other_pnca_WF1) == c2.sum():
print('PASS: WF data has expected length', print('PASS: WF data has expected length'
'\nlength of other_pnca WFO:', c2.sum() ) , '\nlength of other_pnca WFO:', c2.sum()
, '\n===============================================================')
else: else:
print("FAIL: Debug please!") print('FAIL: Debug please!'
print('======================================================================') , '\n===============================================================')
#!!! Important !!! #!!! 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 # 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') other_df = other_pnca_WF1.assign(mutation_info = 'other_mutations_pyrazinamide')
print("dim of other_df:", other_df.shape) print('dim of other_df:', other_df.shape
print('======================================================================') , '\n==============================================================='
print('End of tidy split() on other_muts, and added an extra column relecting mut_category') , '\nEnd of tidy split() on other_muts, and added an extra column relecting mut_category'
print('======================================================================') , '\n===============================================================')
#%% #%%
#========== #==========
# Concatentating the two dfs: equivalent of rbind in R # Concatentating the two dfs: equivalent of rbind in R
#========== #==========
#!!! important !!! #!!! important !!!
# change column names to allow concat: # change column names to allow concat:
# dr_muts.. & other_muts : "mutation" # dr_muts.. & other_muts : 'mutation'
print('Now concatenating the two dfs by row') print('Now concatenating the two dfs by row')
dr_df.columns dr_df.columns
@ -559,39 +565,40 @@ other_df.columns
other_df.rename(columns = {'other_mutations_pyrazinamide': 'mutation'}, inplace = True) other_df.rename(columns = {'other_mutations_pyrazinamide': 'mutation'}, inplace = True)
other_df.columns other_df.columns
print('======================================================================') print('=================================================================')
print('Now appending the two dfs:', print('Now appending the two dfs:'
'\ndr_df dim:', dr_df.shape, , '\ndr_df dim:', dr_df.shape
'\nother_df dim:', other_df.shape, , '\nother_df dim:', other_df.shape
'\ndr_df length:', len(dr_df), , '\ndr_df length:', len(dr_df)
'\nother_df length:', len(other_df), , '\nother_df length:', len(other_df)
'\nexpected length:', len(dr_df) + len(other_df) ) , '\nexpected length:', len(dr_df) + len(other_df)
print('======================================================================') , '\n=============================================================')
# checking colnames before concat # 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)): if (set(dr_df.columns) == set(other_df.columns)):
print('PASS: column names match necessary for merging two dfs') print('PASS: column names match necessary for merging two dfs')
else: else:
print("FAIL: Debug please!") print('FAIL: Debug please!')
# concatenate (axis = 0): Rbind # concatenate (axis = 0): Rbind
pnca_LF0 = pd.concat([dr_df, other_df], ignore_index = True, axis = 0) pnca_LF0 = pd.concat([dr_df, other_df], ignore_index = True, axis = 0)
# checking colnames and length after concat # 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)): if (set(dr_df.columns) == set(pnca_LF0.columns)):
print('PASS: column names match') print('PASS: column names match')
else: 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): 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: else:
print("FAIL: length mismatch") print('FAIL: length mismatch'
print('======================================================================') , '\n===============================================================')
#%% #%%
########### ###########
# This is hopefully clean data, but just double check # 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', print('PASS: length of pnca_LF0 and pnca_LF1 match',
'\nconfirming extraction and concatenating worked correctly') '\nconfirming extraction and concatenating worked correctly')
else: else:
print('FAIL: BUT NOT FATAL!', print('FAIL: BUT NOT FATAL!'
'\npnca_LF0 and pnca_LF1 lengths differ', , '\npnca_LF0 and pnca_LF1 lengths differ'
'\nsuggesting error in extraction process' , '\nsuggesting error in extraction process'
' use pnca_LF1 for downstreama analysis') , ' use pnca_LF1 for downstreama analysis'
print('======================================================================') , '\n=========================================================')
print('length of dfs pre and post processing...', print('length of dfs pre and post processing...'
'\npnca WF data (unique samples in each row):', extracted_pnca_samples, , '\npnca WF data (unique samples in each row):', extracted_pnca_samples
'\npnca LF data (unique mutation in each row):', len(pnca_LF1)) , '\npnca LF data (unique mutation in each row):', len(pnca_LF1)
print('======================================================================') , '\n=============================================================')
#%% #%%
# final sanity check # final sanity check
print('Verifying whether extraction process worked correctly...') print('Verifying whether extraction process worked correctly...')
if len(pnca_LF1) == expected_rows: if len(pnca_LF1) == expected_rows:
print('PASS: extraction process performed correctly', print('PASS: extraction process performed correctly'
'\nexpected length:', expected_rows, , '\nexpected length:', expected_rows
'\ngot:', len(pnca_LF1), , '\ngot:', len(pnca_LF1)
'\nRESULT: Total no. of mutant sequences for logo plot:', len(pnca_LF1) ) , '\nRESULT: Total no. of mutant sequences for logo plot:', len(pnca_LF1)
, '\n=========================================================')
else: else:
print('FAIL: extraction process has bugs', print('FAIL: extraction process has bugs'
'\nexpected length:', expected_rows, , '\nexpected length:', expected_rows
'\ngot:', len(pnca_LF1), , '\ngot:', len(pnca_LF1)
'\Debug please') , ', \Debug please'
, '\n=========================================================')
#%% #%%
print('Perfmorning some more sanity checks...') print('Perfmorning some more sanity checks...')
# From LF1: # From LF1:
# no. of unique muts # no. of unique muts
distinct_muts = pnca_LF1.mutation.value_counts() 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 # no. of samples contributing the unique muta
pnca_LF1.id.nunique() 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 # no. of dr and other muts
mut_grouped = pnca_LF1.groupby('mutation_info').mutation.nunique() 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 # sanity check
if len(distinct_muts) == mut_grouped.sum() : 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: else:
print('FAIL: Mistmatch in count of muts', print('FAIL: Mistmatch in count of muts'
'\nexpected count:', len(distinct_muts), , '\nexpected count:', len(distinct_muts)
'\nactual count:', mut_grouped.sum(), , '\nactual count:', mut_grouped.sum()
'\nmuts should be distinct within dr* and other* type', , '\nmuts should be distinct within dr* and other* type'
'\ninspecting ...') , '\ninspecting ...'
, '\n=========================================================')
muts_split = list(pnca_LF1.groupby('mutation_info')) muts_split = list(pnca_LF1.groupby('mutation_info'))
dr_muts = muts_split[0][1].mutation dr_muts = muts_split[0][1].mutation
other_muts = muts_split[1][1].mutation other_muts = muts_split[1][1].mutation
@ -670,36 +681,40 @@ else:
# sanity check: There should not be any common muts # sanity check: There should not be any common muts
# i.e the same mutation cannot be classed as a 'drug' AND 'others' # 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: 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: else:
print('PASS: NO ambiguous muts detected', print('PASS: NO ambiguous muts detected'
'\nMuts are unique to dr_ and other_ mutation class') , '\nMuts are unique to dr_ and other_ mutation class'
, '\n=========================================================')
# inspect dr_muts and other muts # inspect dr_muts and other muts
if dr_muts.isin(other_muts).sum() & other_muts.isin(dr_muts).sum() > 0: if dr_muts.isin(other_muts).sum() & other_muts.isin(dr_muts).sum() > 0:
print('Finding ambiguous muts...', print('Finding ambiguous muts...'
'\n==========================================================', , '\n========================================================='
'\nTotal no. of samples in dr_muts present in other_muts:', dr_muts.isin(other_muts).sum(), , '\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)], , '\nThese are:\n', dr_muts[dr_muts.isin(other_muts)]
'\n==========================================================', , '\n========================================================='
'\nTotal no. of samples in other_muts present in dr_muts:', other_muts.isin(dr_muts).sum(), , '\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)], , '\nThese are:\n', other_muts[other_muts.isin(dr_muts)],
'\n==========================================================') , '\n=========================================================')
else: 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...') print('Counting no. of ambiguous muts...')
if dr_muts[dr_muts.isin(other_muts)].nunique() == other_muts[other_muts.isin(dr_muts)].nunique(): 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() common_muts = dr_muts[dr_muts.isin(other_muts)].value_counts().keys().tolist()
print('Distinct no. of ambigiuous muts detected:'+ str(len(common_muts)), 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: else:
print('Error: ambiguous muts detected, but extraction failed. Debug!', 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 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())) , '\nNo. of ambiguous muts in other:', len(other_muts[other_muts.isin(dr_muts)].value_counts().keys().tolist())
print('======================================================================') , '\n=========================================================')
#%% clear variables #%% 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) 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 = pnca_LF1[pnca_LF1['mutation'].isin(common_muts)]
inspect.to_csv(outfile1) inspect.to_csv(outfile1)
print('Finished writing:', out_filename1, print('Finished writing:', out_filename1
'\nNo. of rows:', len(inspect), , '\nNo. of rows:', len(inspect)
'\nNo. of cols:', len(inspect.columns), , '\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()) , '\nNo. of rows = no. of samples with the ambiguous muts present:'
print('======================================================================') , dr_muts.isin(other_muts).sum() + other_muts.isin(dr_muts).sum()
, '\n=============================================================')
del(out_filename1) del(out_filename1)
#%% read aa dict and pull relevant info #%% read aa dict and pull relevant info
print('Reading aa dict and fetching1 letter aa code', print('Reading aa dict and fetching1 letter aa code'
'\nFormatting mutation in mCSM style format: {WT}<POS>{MUT}', , '\nFormatting mutation in mCSM style format: {WT}<POS>{MUT}'
'\nAdding aa properties') , '\nAdding aa properties'
, '\n============================================================')
#=========== #===========
# Split 'mutation' column into three: wild_type, position and # Split 'mutation' column into three: wild_type, position and
# mutant_type separately. Then map three letter code to one using # 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 # Second: convert to mutation to lowercase for compatibility with dict
#=========== #===========
pnca_LF1['mutation'] = pnca_LF1.loc[:, 'mutation'].str.lower() pnca_LF1['mutation'] = pnca_LF1.loc[:, 'mutation'].str.lower()
#======= #=======
# Iterate through the dict, create a lookup dict i.e # Iterate through the dict, create a lookup dict i.e
# lookup_dict = {three_letter_code: one_letter_code}. # 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 # 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 # to 'pandas series'since map only works in pandas series
#======= #=======
lookup_dict = dict() lookup_dict = dict()
for k, v in my_aa_dict.items(): for k, v in my_aa_dict.items():
lookup_dict[k] = v['one_letter_code'] lookup_dict[k] = v['one_letter_code']
@ -764,7 +784,7 @@ pnca_LF1['position'] = pnca_LF1['mutation'].str.extract(r'(\d+)')
# clear variables # clear variables
del(k, v, wt, mut, lookup_dict) del(k, v, wt, mut, lookup_dict)
#print('======================================================================')
#========= #=========
# iterate through the dict, create a lookup dict that i.e # iterate through the dict, create a lookup dict that i.e
# lookup_dict = {three_letter_code: aa_prop_water} # lookup_dict = {three_letter_code: aa_prop_water}
@ -785,7 +805,7 @@ for k, v in my_aa_dict.items():
# clear variables # clear variables
del(k, v, wt, mut, lookup_dict) del(k, v, wt, mut, lookup_dict)
#print('======================================================================')
#======== #========
# iterate through the dict, create a lookup dict that i.e # iterate through the dict, create a lookup dict that i.e
# lookup_dict = {three_letter_code: aa_prop_polarity} # lookup_dict = {three_letter_code: aa_prop_polarity}
@ -806,7 +826,6 @@ for k, v in my_aa_dict.items():
# clear variables # clear variables
del(k, v, wt, mut, lookup_dict) del(k, v, wt, mut, lookup_dict)
#print('======================================================================')
#======== #========
# iterate through the dict, create a lookup dict that i.e # 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 # added two more cols
# clear variables # clear variables
#del(k, v, wt, mut, lookup_dict) #del(k, v, wt, mut, lookup_dict)
#print('======================================================================')
#======== #========
# iterate through the dict, create a lookup dict that i.e # 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 # added two more cols
# clear variables # clear variables
del(k, v, wt, mut, lookup_dict) del(k, v, wt, mut, lookup_dict)
print('======================================================================')
######## ########
# combine the wild_type+poistion+mutant_type columns to generate # combine the wild_type+poistion+mutant_type columns to generate
# Mutationinformation (matches mCSM output field) # Mutationinformation (matches mCSM output field)
# Remember to use .map(str) for int col types to allow string concatenation # 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'] pnca_LF1['Mutationinformation'] = pnca_LF1['wild_type'] + pnca_LF1.position.map(str) + pnca_LF1['mutant_type']
print('Created column: Mutationinformation') print('Created column: Mutationinformation'
print('======================================================================') , '\n===============================================================')
#%% Write file: mCSM muts #%% Write file: mCSM muts
snps_only = pd.DataFrame(pnca_LF1['Mutationinformation'].unique()) snps_only = pd.DataFrame(pnca_LF1['Mutationinformation'].unique())
snps_only.head() snps_only.head()
@ -867,47 +886,48 @@ pos_only = pd.DataFrame(pnca_LF1['position'].unique())
print('Checking NA in snps...')# should be 0 print('Checking NA in snps...')# should be 0
if snps_only.Mutationinformation.isna().sum() == 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: else:
print('FAIL: SNP has NA, Possible mapping issues from dict?', print('FAIL: SNP has NA, Possible mapping issues from dict?'
'\nDebug please!') , '\nDebug please!'
print('======================================================================') , '\n=========================================================')
out_filename2 = gene.lower() + '_' + 'mcsm_snps.csv' out_filename2 = gene.lower() + '_' + 'mcsm_snps.csv'
outfile2 = outdir + '/' + out_filename2 outfile2 = outdir + '/' + out_filename2
print('Writing file: mCSM style muts', print('Writing file: mCSM style muts'
'\nFilename:', out_filename2, , '\nFilename:', out_filename2
'\nPath:', outdir, , '\nPath:', outdir
'\nmutation format (SNP): {WT}<POS>{MUT}', , '\nmutation format (SNP): {WT}<POS>{MUT}'
'\nNo. of distinct muts:', len(snps_only), , '\nNo. of distinct muts:', len(snps_only)
'\nNo. of distinct positions:', len(pos_only)) , '\nNo. of distinct positions:', len(pos_only)
, '\n=============================================================')
snps_only.to_csv(outfile2, header = False, index = False) snps_only.to_csv(outfile2, header = False, index = False)
print('Finished writing:', out_filename2, print('Finished writing:', out_filename2
'\nNo. of rows:', len(snps_only), , '\nNo. of rows:', len(snps_only)
'\nNo. of cols:', len(snps_only.columns)) , '\nNo. of cols:', len(snps_only.columns)
print('======================================================================') , '\n=============================================================')
del(out_filename2) del(out_filename2)
#%% Write file: pnca_metadata (i.e pnca_LF1) #%% Write file: pnca_metadata (i.e pnca_LF1)
# where each row has UNIQUE mutations NOT unique sample ids # where each row has UNIQUE mutations NOT unique sample ids
out_filename3 = gene.lower() + '_' + 'metadata.csv' out_filename3 = gene.lower() + '_' + 'metadata.csv'
outfile3 = outdir + '/' + out_filename3 outfile3 = outdir + '/' + out_filename3
print('Writing file: LF formatted data', print('Writing file: LF formatted data'
'\nFilename:', out_filename3, , '\nFilename:', out_filename3
'\nPath:', outdir) , '\nPath:', outdir
, '\n============================================================')
pnca_LF1.to_csv(outfile3, header = True, index = False) pnca_LF1.to_csv(outfile3, header = True, index = False)
print('Finished writing:', out_filename3, print('Finished writing:', out_filename3
'\nNo. of rows:', len(pnca_LF1), , '\nNo. of rows:', len(pnca_LF1)
'\nNo. of cols:', len(pnca_LF1.columns) ) , '\nNo. of cols:', len(pnca_LF1.columns)
print('======================================================================') , '\n=============================================================')
del(out_filename3) del(out_filename3)
#%% write file: mCSM style but with repitions for MSA and logo plots #%% write file: mCSM style but with repitions for MSA and logo plots
all_muts_msa = pd.DataFrame(pnca_LF1['Mutationinformation']) all_muts_msa = pd.DataFrame(pnca_LF1['Mutationinformation'])
all_muts_msa.head() all_muts_msa.head()
@ -930,11 +950,12 @@ all_muts_msa_sorted.head()
print('Checking NA in snps...')# should be 0 print('Checking NA in snps...')# should be 0
if all_muts_msa.Mutationinformation.isna().sum() == 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: else:
print('FAIL: SNP has NA, Possible mapping issues from dict?', print('FAIL: SNP has NA, Possible mapping issues from dict?'
'\nDebug please!') , '\nDebug please!'
print('======================================================================') , '\n=========================================================')
out_filename4 = gene.lower() + '_' + 'all_muts_msa.csv' out_filename4 = gene.lower() + '_' + 'all_muts_msa.csv'
outfile4 = outdir + '/' + out_filename4 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) all_muts_msa_sorted.to_csv(outfile4, header = False, index = False)
print('Finished writing:', out_filename4, print('Finished writing:', out_filename4
'\nNo. of rows:', len(all_muts_msa), , '\nNo. of rows:', len(all_muts_msa)
'\nNo. of cols:', len(all_muts_msa.columns) ) , '\nNo. of cols:', len(all_muts_msa.columns)
print('======================================================================') , '\n=============================================================')
del(out_filename4)
del(out_filename4)
#%% write file for mutational positions #%% write file for mutational positions
# count how many positions this corresponds to # 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' out_filename5 = gene.lower() + '_' + 'mutational_positons.csv'
outfile5 = outdir + '/' + out_filename5 outfile5 = outdir + '/' + out_filename5
print('Writing file: mutational positions', print('Writing file: mutational positions'
'\nNo. of distinct positions:', len(pos_only_sorted), , '\nNo. of distinct positions:', len(pos_only_sorted)
'\nFilename:', out_filename5, , '\nFilename:', out_filename5
'\nPath:', outdir) , '\nPath:', outdir
, '\n=============================================================')
pos_only_sorted.to_csv(outfile5, header = True, index = False) pos_only_sorted.to_csv(outfile5, header = True, index = False)
print('Finished writing:', out_filename5, print('Finished writing:', out_filename5
'\nNo. of rows:', len(pos_only_sorted), , '\nNo. of rows:', len(pos_only_sorted)
'\nNo. of cols:', len(pos_only_sorted.columns) ) , '\nNo. of cols:', len(pos_only_sorted.columns)
print('======================================================================') , '\n=============================================================')
del(out_filename5) del(out_filename5)
#=======================================================================
#%% end of script
print(u'\u2698' * 50, print(u'\u2698' * 50,
'\nEnd of script: Data extraction and writing files' '\nEnd of script: Data extraction and writing files'
'\n' + u'\u2698' * 50 ) '\n' + u'\u2698' * 50 )
#%% #%% end of script

16
meta_data_analysis/dssp_df.py
View file

@ -55,13 +55,12 @@ indir = datadir + '/' + drug + '/' + 'output'
in_filename = gene.lower() +'.dssp' in_filename = gene.lower() +'.dssp'
infile = indir + '/' + in_filename infile = indir + '/' + in_filename
print('Input filename:', in_filename print('Input filename:', in_filename
, '\nInput path:', indir) , '\nInput path:', indir
, '\n============================================================')
# specify PDB chain # specify PDB chain
my_chain = 'A' my_chain = 'A'
print('======================================================================')
#======= #=======
# output # output
#======= #=======
@ -70,9 +69,9 @@ out_filename = gene.lower() + '_dssp.csv'
outfile = outdir + '/' + out_filename outfile = outdir + '/' + out_filename
print('Output filename:', out_filename print('Output filename:', out_filename
, '\nOutput path:', outdir , '\nOutput path:', outdir
,'\nOutfile: ', outfile) , '\nOutfile: ', outfile
, '\n=============================================================')
print('======================================================================')
#%% end of variable assignment for input and output files #%% end of variable assignment for input and output files
#======================================================================= #=======================================================================
# Process dssp output and extract into df # Process dssp output and extract into df
@ -96,14 +95,15 @@ dssp_df.columns
#%% Write ouput csv file #%% Write ouput csv file
print('Writing file:', outfile print('Writing file:', outfile
, '\nFilename:', out_filename , '\nFilename:', out_filename
, '\nPath:', outdir) , '\nPath:', outdir
, '\n=============================================================')
# write to csv # write to csv
dssp_df.to_csv(outfile, header=True, index = False) dssp_df.to_csv(outfile, header=True, index = False)
print('Finished writing:', out_filename print('Finished writing:', out_filename
, '\nNo. of rows:', len(dssp_df) , '\nNo. of rows:', len(dssp_df)
, '\nNo. of cols:', len(dssp_df.columns)) , '\nNo. of cols:', len(dssp_df.columns)
print('======================================================================') , '\n==============================================================')
#%% end of script #%% end of script
#======================================================================= #=======================================================================

36
meta_data_analysis/kd_df.py
View file

@ -56,9 +56,8 @@ indir = datadir + '/' + drug + '/' + 'input'
in_filename = '3pl1.fasta.txt' in_filename = '3pl1.fasta.txt'
infile = indir + '/' + in_filename infile = indir + '/' + in_filename
print('Input filename:', in_filename print('Input filename:', in_filename
, '\nInput path:', indir) , '\nInput path:', indir
, '\n============================================================')
print('======================================================================')
#======= #=======
# output # output
@ -67,9 +66,8 @@ outdir = datadir + '/' + drug + '/' + 'output'
out_filename = gene.lower() + '_kd.csv' out_filename = gene.lower() + '_kd.csv'
outfile = outdir + '/' + out_filename outfile = outdir + '/' + out_filename
print('Output filename:', out_filename print('Output filename:', out_filename
, '\nOutput path:', outdir) , '\nOutput path:', outdir
, '\n=============================================================')
print('======================================================================')
#%% end of variable assignment for input and output files #%% end of variable assignment for input and output files
#======================================================================= #=======================================================================
#%%specify window size for hydropathy profile computation #%%specify window size for hydropathy profile computation
@ -96,7 +94,7 @@ print('Sequence Length:', num_residues)
print('kd_values Length:',len(kd_values)) print('kd_values Length:',len(kd_values))
print('Window Length:', my_window) print('Window Length:', my_window)
print('Window Offset:', offset) print('Window Offset:', offset)
print('======================================================================') print('=================================================================')
print('Checking:len(kd values) is as expected for the given window size & offset...') print('Checking:len(kd values) is as expected for the given window size & offset...')
expected_length = num_residues - (my_window - offset) expected_length = num_residues - (my_window - offset)
if len(kd_values) == expected_length: if len(kd_values) == expected_length:
@ -104,9 +102,8 @@ if len(kd_values) == expected_length:
else: else:
print('FAIL: length mismatch' print('FAIL: length mismatch'
,'\nExpected length:', expected_length ,'\nExpected length:', expected_length
,'\nActual length:', len(kd_values)) ,'\nActual length:', len(kd_values)
, '\n=========================================================')
print('======================================================================')
#%% make 2 dfs; 1) aa sequence and 2) kd_values. Then reset index for each df #%% 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. # 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 = kd_df.rename_axis('position')
kd_df.head 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': if kd_df.index.dtype == 'int64':
print('PASS: position col is numeric' print('PASS: position col is numeric'
, '\ndtype is:', kd_df.index.dtype) , '\ndtype is:', kd_df.index.dtype)
@ -150,19 +148,20 @@ else:
, '\nConverting to numeric') , '\nConverting to numeric')
kd_df.index.astype('int64') kd_df.index.astype('int64')
print('Checking dtype for after conversion:\n' print('Checking dtype for after conversion:\n'
,'\ndtype is:', kd_df.index.dtype) , '\ndtype is:', kd_df.index.dtype
, '\n=========================================================')
print('======================================================================')
#%% write file #%% write file
print('Writing file:', out_filename print('Writing file:', out_filename
, '\nFilename:', out_filename , '\nFilename:', out_filename
, '\nPath:', outdir) , '\nPath:', outdir
, '\n=============================================================')
kd_df.to_csv(outfile, header = True, index = True) kd_df.to_csv(outfile, header = True, index = True)
print('Finished writing:', out_filename print('Finished writing:', out_filename
, '\nNo. of rows:', len(kd_df) , '\nNo. of rows:', len(kd_df)
, '\nNo. of cols:', len(kd_df.columns)) , '\nNo. of cols:', len(kd_df.columns)
, '\n=============================================================')
#%% plot #%% plot
# http://www.dalkescientific.com/writings/NBN/plotting.html # http://www.dalkescientific.com/writings/NBN/plotting.html
@ -176,7 +175,6 @@ xlabel('Residue Number')
ylabel('Hydrophobicity') ylabel('Hydrophobicity')
title('K&D Hydrophobicity for ' + id) title('K&D Hydrophobicity for ' + id)
show() show()
print('======================================================================') print('======================================================================')
#%% end of script #%% end of script
#======================================================================= #=======================================================================

28
meta_data_analysis/mut_electrostatic_changes.py
View file

@ -46,7 +46,8 @@ indir = datadir + '/' + drug + '/' + 'input'
in_filename = 'merged_df3.csv' in_filename = 'merged_df3.csv'
infile = outdir + '/' + in_filename infile = outdir + '/' + in_filename
print('Input filename: ', in_filename print('Input filename: ', in_filename
, '\nInput path: ', indir) , '\nInput path: ', indir
, '\n============================================================')
#======= #=======
# output # output
@ -56,7 +57,8 @@ outdir = datadir + '/' + drug + '/' + 'output'
out_filename = 'mut_elec_changes.txt' out_filename = 'mut_elec_changes.txt'
outfile = outdir + '/' + out_filename outfile = outdir + '/' + out_filename
print('Output filename: ', out_filename print('Output filename: ', out_filename
, '\nOutput path: ', outdir) , '\nOutput path: ', outdir
, '\n============================================================')
#%% end of variable assignment for input and output files #%% 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 = ',') comb_df = pd.read_csv(infile, sep = ',')
print('Input filename: ', in_filename, print('Input filename: ', in_filename
'\nPath :', outdir, , '\nPath :', outdir
'\nNo. of rows: ', len(comb_df), , '\nNo. of rows: ', len(comb_df)
'\nNo. of cols: ', infile) , '\nNo. of cols: ', infile
, '\n============================================================')
# column names # column names
list(comb_df.columns) list(comb_df.columns)
@ -81,15 +84,18 @@ df = comb_df.drop_duplicates(['Mutationinformation'], keep = 'first')
total_muts = df.Mutationinformation.nunique() total_muts = df.Mutationinformation.nunique()
#df.Mutationinformation.count() #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 #%% combine aa_calcprop cols so that you can count the changes as value_counts
# check if all muts have been categorised # check if all muts have been categorised
print('Checking 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(): 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: 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['wt_calcprop'].head()
df['mut_calcprop'].head() df['mut_calcprop'].head()
@ -151,11 +157,11 @@ print('======================\n'
, '\n============================\n' , '\n============================\n'
, all_prop_change) , all_prop_change)
print('========================================================================' print('================================================================='
, '\nTotal number of electrostatic changes resulting from Mtation is (%):', elec_changes , '\nTotal number of electrostatic changes resulting from Mtation is (%):', elec_changes
, '\nTotal no. of muts: ', total_muts , '\nTotal no. of muts: ', total_muts
, '\nTotal no. of changed muts: ', all_prop_change.mut_count.sum() , '\nTotal no. of changed muts: ', all_prop_change.mut_count.sum()
, '\nTotal no. of unchanged muts: ', no_change_muts.mut_count.sum() , '\nTotal no. of unchanged muts: ', no_change_muts.mut_count.sum()
, '\n=========================================================================') , '\n===================================================================')
#%% end of script #%% end of script
#======================================================================= #=======================================================================

35
meta_data_analysis/rd_df.py
View file

@ -48,9 +48,8 @@ indir = datadir + '/' + drug + '/' + 'output'
in_filename = '3pl1_rd.tsv' in_filename = '3pl1_rd.tsv'
infile = indir + '/' + in_filename infile = indir + '/' + in_filename
print('Input filename:', in_filename print('Input filename:', in_filename
, '\nInput path:', indir) , '\nInput path:', indir
, '\n=============================================================')
print('======================================================================')
#======= #=======
# output # output
#======= #=======
@ -58,9 +57,9 @@ outdir = datadir + '/' + drug + '/' + 'output'
out_filename = gene.lower() + '_rd.csv' out_filename = gene.lower() + '_rd.csv'
outfile = outdir + '/' + out_filename outfile = outdir + '/' + out_filename
print('Output filename:', out_filename print('Output filename:', out_filename
, '\nOutput path:', outdir) , '\nOutput path:', outdir
, '\n=============================================================')
print('======================================================================')
#%% end of variable assignment for input and output files #%% end of variable assignment for input and output files
#======================================================================= #=======================================================================
#%% Read input file #%% Read input file
@ -69,9 +68,8 @@ print('Reading input file:', infile
, '\nNo. of rows:', len(rd_data) , '\nNo. of rows:', len(rd_data)
, '\nNo. of cols:', len(rd_data.columns)) , '\nNo. of cols:', len(rd_data.columns))
print('Column names:', rd_data.columns) print('Column names:', rd_data.columns
, '\n===============================================================')
print('======================================================================')
#======================== #========================
# creating position col # creating position col
#======================== #========================
@ -85,8 +83,8 @@ rd_data['position'].dtype
print('Extracted residue num from index and assigned as a column:' print('Extracted residue num from index and assigned as a column:'
, '\ncolumn name: position' , '\ncolumn name: position'
, '\ntotal no. of cols now:', len(rd_data.columns)) , '\ntotal no. of cols now:', len(rd_data.columns)
print('======================================================================') , '\n=============================================================')
#======================== #========================
# Renaming amino-acid # Renaming amino-acid
@ -95,9 +93,8 @@ print('======================================================================')
print('Renaming columns:' print('Renaming columns:'
, '\ncolname==> # chain:residue: wt_3letter_caps' , '\ncolname==> # chain:residue: wt_3letter_caps'
, '\nYES... the column name *actually* contains a # ..!' , '\nYES... the column name *actually* contains a # ..!'
,'\ncolname==> all-atom: rd_values') , '\ncolname==> all-atom: rd_values'
, '\n=============================================================')
print('======================================================================')
rd_data.rename(columns = {'# chain:residue':'wt_3letter_caps', 'all-atom':'rd_values'}, inplace = True) rd_data.rename(columns = {'# chain:residue':'wt_3letter_caps', 'all-atom':'rd_values'}, inplace = True)
print('Column names:', rd_data.columns) print('Column names:', rd_data.columns)
@ -118,21 +115,21 @@ if len(rd_df) == len(rd_data):
else: else:
print('FAIL: no. of rows mimatch' print('FAIL: no. of rows mimatch'
, '\nExpected no. of rows:', len(rd_data) , '\nExpected no. of rows:', len(rd_data)
,'\nGot no. of rows:', len(rd_df)) , '\nGot no. of rows:', len(rd_df)
, '\n=========================================================')
print('======================================================================')
#%% write file #%% write file
print('Writing file:' print('Writing file:'
, '\nFilename:', out_filename , '\nFilename:', out_filename
, '\nPath:', outdir) , '\nPath:', outdir
, '\n=============================================================')
rd_df.to_csv(outfile, header = True, index = False) rd_df.to_csv(outfile, header = True, index = False)
print('======================================================================')
print('Finished writing:', out_filename print('Finished writing:', out_filename
, '\nNo. of rows:', len(rd_df) , '\nNo. of rows:', len(rd_df)
, '\nNo. of cols:', len(rd_df.columns)) , '\nNo. of cols:', len(rd_df.columns)
, '\n=============================================================')
#%% end of script #%% end of script
#======================================================================= #=======================================================================

3
meta_data_analysis/reference_dict.py
View file

@ -44,7 +44,8 @@ indir = datadir + '/' + drug + 'input'
in_filename = 'aa_codes.csv' in_filename = 'aa_codes.csv'
infile = indir + '/' + in_filename infile = indir + '/' + in_filename
print('Input filename:', in_filename print('Input filename:', in_filename
, '\nInput path:', indir) , '\nInput path:', indir
, '\n============================================================')
#======= #=======
# output: No output # output: No output