saving previous work from home pc
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c56a5e4497
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0ebd8a6d4b
3 changed files with 146 additions and 106 deletions
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@ -23,11 +23,7 @@ import pandas as pd
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#my_dir = os.path.expanduser('~/some_dir')
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#make sure mcsm_analysis/ exists
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#or specify the output directory
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#%%
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#%%
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#%%
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#========================================================
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#====================================================
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# TASK: extract ALL pncA mutations from GWAS data
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#========================================================
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#%%
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@ -40,83 +36,45 @@ os.getcwd()
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#%%
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from reference_dict import my_aa_dict #CHECK DIR STRUC THERE!
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#%%
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#NOTE: Out_dir MUST exis
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# User defined dir strpyrazinamide
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drug = 'pyrazinamide'
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gene = 'pnca'
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out_dir = homedir + '/git/LSHTM_analysis/mcsm_analysis/'
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# = out_dir + drug
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data_dir = homedir + '/git/Data'
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############# specify variables for input and output paths and filenames
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drug = "pyrazinamide"
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gene = "pnca"
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if not os.path.exists(data_dir):
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print('Error!', data_dir, 'does not exist. Please ensure it exists and contains the appropriate raw data')
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os.makedirs(data_dir)
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datadir = homedir + "/git/Data"
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basedir = datadir + "/" + drug + "/input"
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# input
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inpath = "/original"
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in_filename = "/original_tanushree_data_v2.csv"
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infile = basedir + inpath + in_filename
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#print(infile)
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# output: several output files
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# output filenames in respective sections at the time of outputting files
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outpath = "/processed"
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outdir = basedir + outpath
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#print(outdir)
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if not os.path.exists(datadir):
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print('Error!', datadir, 'does not exist. Please ensure it exists. Dir struc specified in README.md')
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os.makedirs(datadir)
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die()
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if not os.path.exists(out_dir):
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print('Error!', out_dir, 'does not exist. Please create it')
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if not os.path.exists(outdir):
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print('Error!', outdir, 'does not exist.Please ensure it exists. Dir struc specified in README.md')
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exit()
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#if not os.path.exists(work_dir):
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# print('creating dir that does not exist', 'dir_name:', work_dir)
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# os.makedirs(work_dir)
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else:
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print('Dir exists: Carrying on')
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# now create sub dir structure within work_dir
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# pyrazinamide/mcsm_analysis
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# we need three dir
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# Data
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# Scripts
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# Plotting
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# Results
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# Plots
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# create a list of dir names
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#dir_names = ['Data', 'Scripts', 'Results']
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#for i in dir_names:
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# this_dir = (work_dir + '/' + i)
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# if not os.path.exists(this_dir):
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# print('creating dir that does not exist:', this_dir)
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# os.makedirs(this_dir)
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#else:
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# print('Dir exists: Carrying on')
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# Create sub dirs
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# 1)
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# Scripts
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# Plotting
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#subdir_plotting = work_dir + '/Scripts/Plotting'
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#if not os.path.exists(subdir_plotting):
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# print('creating dir that does not exist:', subdir_plotting)
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# os.makedirs(subdir_plotting)
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#else:
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# print('Dir exists: Carrying on')
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# 2)
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# Results
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# Plots
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#subdir_plots = work_dir + '/Results/Plots'
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#if not os.path.exists(subdir_plots):
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# print('creating dir that does not exist:', subdir_plots)
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# os.makedirs(subdir_plots)
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#else:
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# print('Dir exists: Carrying on')
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# clear varaibles
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#del(dir_names, drug, i, subdir_plots, subdir_plotting)
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#exit()
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################## end of variable assignment for input and output files
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#%%
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#==============================================================================
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############
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# STEP 1: Read file original_tanushree_data_v2.csv
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############
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data_file = data_dir + '/input/original/original_tanushree_data_v2.csv'
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meta_data = pd.read_csv(data_file, sep = ',')
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#data_file = data_dir + '/input/original/original_tanushree_data_v2.csv'
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meta_data = pd.read_csv(infile, sep = ',')
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# column names
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list(meta_data.columns)
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@ -130,7 +88,7 @@ meta_data = meta_data[['id'
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, 'pyrazinamide'
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, 'dr_mutations_pyrazinamide'
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, 'other_mutations_pyrazinamide'
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]]
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]] #19265, 67
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# checks
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total_samples = meta_data['id'].nunique() # 19265
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@ -143,7 +101,12 @@ meta_data.head()
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# equivalent of table in R
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# pyrazinamide counts
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meta_data.pyrazinamide.value_counts()
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meta_data.pyrazinamide.value_counts() #12511
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#0.0 10565
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#1.0 1946 {RESULT: No. of Resistant and Susceptible samples}
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clear variables
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#del(basedir, datadir, inpath, infile)
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#%%
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############
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@ -154,8 +117,17 @@ meta_data.pyrazinamide.value_counts()
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# (corresponding to a structure)
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# and drop the entries with NA
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#############
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meta_pza = meta_data.loc[meta_data.dr_mutations_pyrazinamide.str.contains('pncA_p.*', na = False)]
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meta_pza = meta_data.loc[meta_data.other_mutations_pyrazinamide.str.contains('pncA_p.*', na = False)]
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meta_pza = meta_data.loc[meta_data.dr_mutations_pyrazinamide.str.contains('pncA_p.*', na = False)]
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#2163 {RESULT: samples with dr_muts}
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dr_id = meta_pza['id'].unique()
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meta_pza = meta_data.loc[meta_data.other_mutations_pyrazinamide.str.contains('pncA_p.*', na = False)]
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#526 (RESULT: samples with other_muts)
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other_id = meta_pza['id'].unique()
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# FIXME: See if the sample ids are unique in each
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# find any common IDs
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dr_id.isin(other_id[1,1])
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del(meta_pza)
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@ -188,8 +160,9 @@ del(meta_pnca_other)
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# Now extract "all" mutations
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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.*') ]
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#2665, 8
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meta_pnca_all['id'].nunique()
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meta_pnca_all['id'].nunique() {#RESULT: pnca mutations in ALL samples}
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pnca_samples = len(meta_pnca_all)
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pnca_na = meta_pnca_all['pyrazinamide'].isna().sum()
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comp_pnca_samples = pnca_samples - pnca_na
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@ -468,8 +441,11 @@ meta_pnca_LF1['Mutationinformation'] = meta_pnca_LF1['wild_type'] + meta_pnca_LF
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#=========
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# Step 12a: all SNPs to run mCSM
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#=========
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snps_only = pd.DataFrame(meta_pnca_LF1['Mutationinformation'].unique())
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pos_only = pd.DataFrame(meta_pnca_LF1['position'].unique())
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snps_only = pd.DataFrame(meta_pnca_LF1['Mutationinformation'].unique()) #336
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pos_only = pd.DataFrame(meta_pnca_LF1['position'].unique()) #131
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# since 186 is not part of struc: it is one less
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#FIXME!
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# assign meaningful colnames
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#snps_only.rename({0 : 'all_pnca_snps'}, axis = 1, inplace = True)
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@ -480,24 +456,27 @@ snps_only.isna().sum() # should be 0
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# specify variable name for output file
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gene = 'pnca'
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#drug = 'pyrazinamide'
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my_fname1 = '_snps_'
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my_fname1 = '_snps'
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nrows = len(snps_only)
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#output_file_path = '/home/tanu/git/Data/input/processed/pyrazinamide/'
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#output_file_path = work_dir + '/Data/'
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output_file_path = data_dir + '/input/processed/' + drug + '/'
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#output_file_path = data_dir + '/input/processed/' + drug + '/'
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print(outdir)
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if not os.path.exists(output_file_path):
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print( output_file_path, 'does not exist. Creating')
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os.makedirs(output_file_path)
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if not os.path.exists(outdir):
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print( outdir, 'does not exist. Creating')
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os.makedirs(outdir)
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exit()
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output_filename = output_file_path + gene + my_fname1 + str(nrows) + '.csv'
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print(output_filename) #<<<- check
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#out_filename = gene + my_fname1 + str(nrows) + '.csv'
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out_filename = '/' + gene + my_fname1 + '.csv'
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outfile = outdir + out_filename
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print(outfile) #<<<- check
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# write to csv: without column or row names
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# Bad practice: numbers at the start of a filename
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snps_only.to_csv(output_filename, header = False, index = False)
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snps_only.to_csv(out_filename, header = False, index = False)
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#=========
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# Step 12b: all snps with annotation
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@ -519,12 +498,23 @@ snps_only.to_csv(output_filename, header = False, index = False)
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#my_fname2 = '_snps_with_metadata_'
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#nrows = len(pnca_snps_ALL)
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#output_file_path = '/home/tanu/git/Data/input/processed/pyrazinamide/'
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#output_file_path = work_dir + '/Data/'
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#output_filename = output_file_path + gene + my_fname2 + str(nrows) + '.csv'
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#print(output_filename) #<<<- check
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#output_file_path = data_dir + '/input/processed/' + drug + '/'
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print(outdir)
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if not os.path.exists(outdir):
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print( outdir, 'does not exist. Creating')
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os.makedirs(outdir)
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exit()
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#out_filename = gene + my_fname2 + str(nrows) + '.csv'
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out_filename = '/' + gene + my_fname1 + '.csv'
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outfile = outdir + out_filename
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print(outfile) #<<<- check
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# write out file
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#pnca_snps_ALL.to_csv(output_filename, header = True, index = False)
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#pnca_snps_ALL.to_csv(outfile, header = True, index = False)
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#=========
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# Step 12c: comp snps for OR calcs with annotation
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@ -547,7 +537,7 @@ meta_pnca_LF2['mutation'].nunique()
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meta_pnca_LF2.groupby('mutation_info').nunique()
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# sanity check
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meta_pnca_LF2['id'].nunique()
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meta_pnca_LF2['id'].nunique() #1908
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# should be True
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if meta_pnca_LF2['id'].nunique() == comp_pnca_samples:
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@ -569,15 +559,26 @@ len(pnca_snps_COMP)
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gene = 'pnca'
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#drug = 'pyrazinamide'
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my_fname3 = '_comp_snps_with_metadata_'
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my_fname3 = '_comp_snps_with_metadata'
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nrows = len(pnca_snps_COMP)
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#output_filename = output_file_path + gene + my_fname3 + str(nrows) + '.csv'
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#print(output_filename) #<<<-check
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#output_file_path = '/home/tanu/git/Data/input/processed/pyrazinamide/'
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#output_file_path = work_dir + '/Data/'
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#output_file_path = data_dir + '/input/processed/' + drug + '/'
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print(outdir)
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if not os.path.exists(outdir):
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print( outdir, 'does not exist. Creating')
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os.makedirs(outdir)
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exit()
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#out_filename = gene + my_fname3 + str(nrows) + '.csv'
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#out_filename = '/' + gene + my_fname3 + '.csv'
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#outfile = outdir + out_filename
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#print(outfile) #<<<- check
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# write out file
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#pnca_snps_COMP.to_csv(output_filename, header = True, index = False)
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#pnca_snps_COMP.to_csv(outfile, header = True, index = False)
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#=========
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# Step 12d: comp snps only
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@ -589,15 +590,26 @@ snps_only = pd.DataFrame(meta_pnca_LF2['Mutationinformation'].unique())
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gene = 'pnca'
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#drug = 'pyrazinamide'
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my_fname1 = '_comp_snps_'
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my_fname1 = '_comp_snps'
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nrows = len(snps_only)
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output_filename = output_file_path + gene + my_fname1 + str(nrows) + '.csv'
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print(output_filename) #<<<- check
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#output_file_path = '/home/tanu/git/Data/input/processed/pyrazinamide/'
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#output_file_path = work_dir + '/Data/'
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#output_file_path = data_dir + '/input/processed/' + drug + '/'
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print(outdir)
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if not os.path.exists(outdir):
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print( outdir, 'does not exist. Creating')
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os.makedirs(outdir)
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exit()
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#out_filename = gene + my_fname1 + str(nrows) + '.csv'
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out_filename = '/' + gene + my_fname1 + '.csv'
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outfile = outdir + out_filename
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print(outfile) #<<<- check
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# write to csv: without column or row names
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snps_only.to_csv(output_filename, header = False, index = False)
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snps_only.to_csv(outfile, header = False, index = False)
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#=#=#=#=#=#=#=#
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# COMMENT: LF1 is the file to extract all unique snps for mcsm
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@ -619,8 +631,21 @@ gene = 'pnca'
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#drug = 'pyrazinamide'
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my_fname4 = '_metadata'
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#nrows = len(meta_pnca_LF1)
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output_filename = output_file_path + gene + my_fname4 + '.csv'
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print(output_filename) #<<<-check
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#output_file_path = '/home/tanu/git/Data/input/processed/pyrazinamide/'
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#output_file_path = work_dir + '/Data/'
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#output_file_path = data_dir + '/input/processed/' + drug + '/'
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print(outdir)
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if not os.path.exists(outdir):
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print( outdir, 'does not exist. Creating')
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os.makedirs(outdir)
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exit()
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#out_filename = gene + my_fname1 + str(nrows) + '.csv'
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out_filename = '/' + gene + my_fname4 + '.csv'
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outfile = outdir + out_filename
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print(outfile) #<<<- check
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# write out file
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meta_pnca_LF1.to_csv(output_filename)
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meta_pnca_LF1.to_csv(outfile)
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@ -6,7 +6,7 @@ Created on Tue Jun 18 11:32:28 2019
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@author: tanushree
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"""
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############################################
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#load libraries
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# load libraries
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import pandas as pd
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import os
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#############################################
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@ -17,14 +17,29 @@ import os
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# containing GWAS data
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#!#########################!
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print(os.getcwd())
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homedir = os.path.expanduser('~') # spyder/python doesn't recognise tilde
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os.chdir(homedir + '/git/Data/input/original')
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#print(os.getcwd())
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#homedir = os.path.expanduser('~') # spyder/python doesn't recognise tilde
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#os.chdir(homedir + '/git/Data/pyrazinamide/input/original')
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print(os.getcwd())
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#%%
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############# specify variables for input and output paths and filenames
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drug = 'pyrazinamide'
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#gene = 'pnca'
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datadir = homedir + '/git/Data'
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basedir = datadir + '/' + drug + '/input'
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# input
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inpath = "/original"
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in_filename = "/aa_codes.csv"
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infile = basedir + inpath + in_filename
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print(infile)
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#==========
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#read file
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#==========
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my_aa = pd.read_csv('aa_codes.csv') #20, 6
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my_aa = pd.read_csv(infile) #20, 6
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#assign the one_letter code as the row names so that it is easier to create a dict of dicts using index
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#my_aa = pd.read_csv('aa_codes.csv', index_col = 0) #20, 6 #a way to it since it is the first column
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my_aa = my_aa.set_index('three_letter_code_lower') #20, 5
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