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Tanu 2020-02-27 15:16:20 +00:00
parent 77cc5bf42c
commit 8df0b7d920
2 changed files with 33 additions and 9 deletions
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29
mcsm_analysis/pyrazinamide/scripts/generate_mut_sequences.py
View file

@ -9,6 +9,7 @@ Created on Tue Jun 25 08:46:36 2019
# load libraries
import os
import pandas as pd
import numpy as np
from Bio import SeqIO
############################################
#********************************************************************
@ -47,7 +48,7 @@ print("Input file is:", infile_meta_data)
#=======
# output
#=======
outdir = 'git/Data/pyrazinamide/output'
outdir = 'git/Data/pyrazinamide/output'
# filenames in respective sections
################## end of variable assignment for input and output files
@ -86,7 +87,7 @@ print("new length:", len(my_fasta))
#############
# read mutant_info file and extract cols with positions and mutant_info
# This should be all samples with pncA muts
#my_data = pd.read_csv('mcsm_complex1_normalised.csv') #335, 15
#my_data = pd.read_csv('mcsm_complex1_normalised.csv')
my_data = pd.read_csv(infile_meta_data)
list(my_data.columns)
#my_data['OR'].value_counts()
@ -95,12 +96,34 @@ list(my_data.columns)
#FIXME: You need a better way to identify this
# ideally this file should not contain any non_struc pos
# remove positions not in the structure
my_data = my_data[my_data.position != ns_pos_o] #3092, 22
my_data = my_data[my_data.position != ns_pos_o]
# if multiple positions, then try the example below;
# https://stackoverflow.com/questions/29017525/deleting-rows-based-on-multiple-conditions-python-pandas
#df = df[(df.one > 0) | (df.two > 0) | (df.three > 0) & (df.four < 1)]
# count mutations per sample
mut_info = my_data[['id', 'Mutationinformation', 'wild_type', 'position', 'mutant_type']]
# test
foo = mut_info[mut_info.Mutationinformation.str.contains('C72Y')]
foo = mut_info.pivot_table(values = ['Mutationinformation']
, index = ['Mutationinformation', 'id']
# , columns =
, aggfunc = 'count')
# table
foo_tab = mut_info.pivot_table(values = ['Mutationinformation']
# , index = ['Mutationinformation']
, columns = ['id', 'Mutationinformation']
, aggfunc = 'count'
# , margins = True)
)
foo_tab.stack('id')
mut_info.to_csv('mutinfo.csv')
mut_info1 = my_data[['position', 'mutant_type']]
#%%
################

13
meta_data_analysis/pnca_data_extraction.py
View file

@ -118,14 +118,16 @@ clear variables
meta_pza = meta_data.loc[meta_data.dr_mutations_pyrazinamide.str.contains('pncA_p.*', na = False)]
#2163 {RESULT: samples with dr_muts}
dr_id = meta_pza['id'].unique()
dr_id = pd.Series(dr_id)
meta_pza = meta_data.loc[meta_data.other_mutations_pyrazinamide.str.contains('pncA_p.*', na = False)]
#526 (RESULT: samples with other_muts)
other_id = meta_pza['id'].unique()
other_id = pd.Series(other_id)
# FIXME: See if the sample ids are unique in each
# find any common IDs
dr_id.isin(other_id[1,1])
dr_id.isin(other_id).sum()
del(meta_pza)
@ -159,8 +161,7 @@ del(meta_pnca_other)
# Now extract "all" mutations
meta_pnca_all = meta_data_pnca.loc[meta_data_pnca.dr_mutations_pyrazinamide.str.contains('pncA_p.*') | meta_data_pnca.other_mutations_pyrazinamide.str.contains('pncA_p.*') ]
meta_pnca_all['id'].nunique() {#RESULT: pnca mutations in ALL samples}
meta_pnca_all['id'].nunique() #RESULT: pnca mutations in ALL samples}
pnca_samples = len(meta_pnca_all)
pnca_na = meta_pnca_all['pyrazinamide'].isna().sum()
comp_pnca_samples = pnca_samples - pnca_na
@ -170,8 +171,8 @@ comp_pnca_samples = pnca_samples - pnca_na
#=#=#=#=#=#=#
# sanity checks
meta_pnca_all.dr_mutations_pyrazinamide.value_counts()
meta_pnca_all.other_mutations_pyrazinamide.value_counts()
foo1 = meta_pnca_all.dr_mutations_pyrazinamide.value_counts()
foo2 = meta_pnca_all.other_mutations_pyrazinamide.value_counts()
# more sanity checks
# !CAUTION!: muts will change depending on your gene
@ -182,7 +183,7 @@ meta_pnca_all.loc[meta_pnca_all.dr_mutations_pyrazinamide.str.contains('pncA_p.P
meta_pnca_all.loc[meta_pnca_all.dr_mutations_pyrazinamide.str.contains('pncA_p.Val139Leu')]
meta_pnca_all.loc[meta_pnca_all.dr_mutations_pyrazinamide.str.contains('pncA_p.')] # exists # rows
m = meta_pnca_all.loc[meta_pnca_all.dr_mutations_pyrazinamide.str.contains('pncA_p.')] # exists # rows
meta_pnca_all.loc[meta_pnca_all.dr_mutations_pyrazinamide.str.contains('pncA_p.')] # exists # rows
# other_muts
meta_pnca_all.loc[meta_pnca_all.other_mutations_pyrazinamide.str.contains('pncA_p.Gln10Pro*')] # empty