much development
This commit is contained in:
parent
873fd3a121
commit
057291a561
5 changed files with 266 additions and 89 deletions
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@ -51,7 +51,7 @@ def format_mcsm_na_output(mcsm_na_output_tsv):
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print('Assigning meaningful colnames'
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, '\n=======================================================')
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my_colnames_dict = {'PDB_FILE': 'pdb_file' # relevant info from this col will be extracted and the column discarded
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, 'CHAIN': 'chain' # {wild_type}<position>{mutant_type}
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, 'CHAIN': 'chain'
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, 'WILD_RES': 'wild_type' # one letter amino acid code
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, 'RES_POS': 'position' # number
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, 'MUT_RES': 'mutant_type' # one letter amino acid code
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@ -65,8 +65,8 @@ def format_mcsm_na_output(mcsm_na_output_tsv):
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#############
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# create mutationinformation column
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#############
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mcsm_na_data['mutationinformation'] = mcsm_na_data['wild_type'] + mcsm_na_data.position.map(str) + mcsm_na_data['mutant_type']
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#mcsm_na_data['mutationinformation'] = mcsm_na_data['wild_type'] + mcsm_na_data.position.map(str) + mcsm_na_data['mutant_type']
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mcsm_na_data['mutationinformation'] = mcsm_na_data.loc[:,'wild_type'] + mcsm_na_data.loc[:,'position'].astype(int).apply(str) + mcsm_na_data.loc[:,'mutant_type']
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#%%=====================================================================
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#############
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# Create col: mcsm_na_outcome
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@ -132,4 +132,3 @@ def format_mcsm_na_output(mcsm_na_output_tsv):
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, 'pdb_file']]
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return(mcsm_na_dataf)
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#%%#####################################################################
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@ -34,6 +34,11 @@ Created on Tue Aug 6 12:56:03 2019
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# Output: single csv of all 8 dfs combined
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# useful link
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# https://stackoverflow.com/questions/23668427/pandas-three-way-joining-multiple-dataframes-on-columns
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#%% FIXME: let the script proceed even if files don't exist!
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# i.e example below
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# '/home/tanu/git/Data/ethambutol/output/dynamut_results/embb_complex_dynamut_norm.csv'
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#=======================================================================
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#%% load packages
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import sys, os
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@ -48,7 +53,7 @@ homedir = os.path.expanduser('~')
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# set working dir
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os.getcwd()
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os.chdir(homedir + '/git/LSHTM_analysis/scripts')
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#os.chdir(homedir + '/git/LSHTM_analysis/scripts')
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os.getcwd()
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# FIXME: local imports
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@ -109,46 +114,80 @@ if not outdir:
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#=======
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# input
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#=======
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#in_filename_mcsm = gene.lower() + '_complex_mcsm_norm.csv'
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in_filename_mcsm = gene.lower() + '_complex_mcsm_norm_SAM.csv' # gidb
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in_filename_foldx = gene.lower() + '_foldx.csv'
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in_filename_deepddg = gene.lower() + '_ni_deepddg.csv' # change to decent filename and put it in the correct dir
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in_filename_dssp = gene.lower() + '_dssp.csv'
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in_filename_kd = gene.lower() + '_kd.csv'
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in_filename_rd = gene.lower() + '_rd.csv'
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#in_filename_snpinfo = 'ns' + gene.lower() + '_snp_info_f.csv' # gwas f info
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in_filename_afor = gene.lower() + '_af_or.csv'
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#in_filename_afor_kin = gene.lower() + '_af_or_kinship.csv'
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infilename_dynamut = gene.lower() + '_complex_dynamut_norm.csv'
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infilename_dynamut2 = gene.lower() + '_complex_dynamut2_norm.csv'
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infilename_mcsm_na = gene.lower() + '_complex_mcsm_na_norm.csv'
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infilename_mcsm_f_snps = gene.lower() + '_mcsm_formatted_snps.csv'
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gene_list_normal = ["pnca", "katg", "rpob", "alr"]
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if gene.lower() == "gid":
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print("\nReading mCSM file for gene:", gene)
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in_filename_mcsm = gene.lower() + '_complex_mcsm_norm_SAM.csv'
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if gene.lower() == "embb":
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print("\nReading mCSM file for gene:", gene)
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in_filename_mcsm = gene.lower() + '_complex_mcsm_norm1.csv'
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if gene.lower() in gene_list_normal:
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print("\nReading mCSM file for gene:", gene)
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in_filename_mcsm = gene.lower() + '_complex_mcsm_norm.csv'
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infile_mcsm = outdir + in_filename_mcsm
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infile_foldx = outdir + in_filename_foldx
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infile_deepddg = outdir + in_filename_deepddg
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infile_dssp = outdir + in_filename_dssp
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infile_kd = outdir + in_filename_kd
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infile_rd = outdir + in_filename_rd
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#infile_snpinfo = outdir + in_filename_snpinfo
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infile_afor = outdir + in_filename_afor
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#infile_afor_kin = outdir + in_filename_afor_kin
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infile_dynamut = outdir + 'dynamut_results/' + infilename_dynamut
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infile_dynamut2 = outdir + 'dynamut_results/dynamut2/' + infilename_dynamut2
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infile_mcsm_na = outdir + 'mcsm_na_results/' + infilename_mcsm_na
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infile_mcsm_f_snps = outdir + infilename_mcsm_f_snps
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# read csv
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mcsm_df = pd.read_csv(infile_mcsm, sep = ',')
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in_filename_foldx = gene.lower() + '_foldx.csv'
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infile_foldx = outdir + in_filename_foldx
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foldx_df = pd.read_csv(infile_foldx , sep = ',')
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in_filename_deepddg = gene.lower() + '_ni_deepddg.csv' # change to decent filename and put it in the correct dir
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infile_deepddg = outdir + in_filename_deepddg
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deepddg_df = pd.read_csv(infile_deepddg, sep = ',')
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in_filename_dssp = gene.lower() + '_dssp.csv'
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infile_dssp = outdir + in_filename_dssp
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dssp_df = pd.read_csv(infile_dssp, sep = ',')
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in_filename_kd = gene.lower() + '_kd.csv'
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infile_kd = outdir + in_filename_kd
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kd_df = pd.read_csv(infile_kd, sep = ',')
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in_filename_rd = gene.lower() + '_rd.csv'
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infile_rd = outdir + in_filename_rd
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rd_df = pd.read_csv(infile_rd, sep = ',')
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#in_filename_snpinfo = 'ns' + gene.lower() + '_snp_info_f.csv' # gwas f info
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#infile_snpinfo = outdir + in_filename_snpinfo
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in_filename_afor = gene.lower() + '_af_or.csv'
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infile_afor = outdir + in_filename_afor
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afor_df = pd.read_csv(infile_afor, sep = ',')
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dynamut_df = pd.read_csv(infile_dynamut, sep = ',')
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#in_filename_afor_kin = gene.lower() + '_af_or_kinship.csv'
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#infile_afor_kin = outdir + in_filename_afor_kin
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infilename_dynamut2 = gene.lower() + '_dynamut2_norm.csv'
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infile_dynamut2 = outdir + 'dynamut_results/dynamut2/' + infilename_dynamut2
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dynamut2_df = pd.read_csv(infile_dynamut2, sep = ',')
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mcsm_na_df = pd.read_csv(infile_mcsm_na, sep = ',')
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#------------------------------------------------------------
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# ONLY:for gene pnca and gid: End logic should pick this up!
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geneL_dy_na = ["pnca", "gid"]
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#if gene.lower() == "pnca" or "gid" :
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if gene.lower() in geneL_dy_na :
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print("\nGene:", gene.lower()
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, "\nReading Dynamut and mCSM_na files")
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infilename_dynamut = gene.lower() + '_dynamut_norm.csv' # gid
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infile_dynamut = outdir + 'dynamut_results/' + infilename_dynamut
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dynamut_df = pd.read_csv(infile_dynamut, sep = ',')
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infilename_mcsm_na = gene.lower() + '_complex_mcsm_na_norm.csv' # gid
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infile_mcsm_na = outdir + 'mcsm_na_results/' + infilename_mcsm_na
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mcsm_na_df = pd.read_csv(infile_mcsm_na, sep = ',')
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# ONLY:for gene embb and alr: End logic should pick this up!
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geneL_ppi2 = ["embb", "alr"]
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#if gene.lower() == "embb" or "alr":
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if gene.lower() in "embb" or "alr":
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infilename_mcsm_ppi2 = gene.lower() + '_complex_mcsm_ppi2_norm.csv'
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infile_mcsm_ppi2 = outdir + 'mcsm_ppi2/' + infilename_mcsm_ppi2
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mcsm_ppi2_df = pd.read_csv(infile_mcsm_ppi2, sep = ',')
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#--------------------------------------------------------------
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infilename_mcsm_f_snps = gene.lower() + '_mcsm_formatted_snps.csv'
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infile_mcsm_f_snps = outdir + infilename_mcsm_f_snps
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mcsm_f_snps = pd.read_csv(infile_mcsm_f_snps, sep = ',', names = ['mutationinformation'], header = None)
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#=======
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@ -158,12 +197,6 @@ out_filename_comb = gene.lower() + '_all_params.csv'
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outfile_comb = outdir + out_filename_comb
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print('Output filename:', outfile_comb
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, '\n===================================================================')
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o_join = 'outer'
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l_join = 'left'
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r_join = 'right'
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i_join = 'inner'
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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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@ -292,6 +325,44 @@ else:
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, '\n======================================================')
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sys.exit()
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#--------------------------
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# check if >1 chain
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#--------------------------
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deepddg_df.loc[:,'chain_id'].value_counts()
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if len(deepddg_df.loc[:,'chain_id'].value_counts()) > 1:
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print("\nChains detected: >1"
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, "\nGene:", gene
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, "\nChains:", deepddg_df.loc[:,'chain_id'].value_counts().index)
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#--------------------------
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# subset chain
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#--------------------------
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if gene.lower() == "embb":
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sel_chain = "B"
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else:
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sel_chain = "A"
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deepddg_df = deepddg_df[deepddg_df['chain_id'] == sel_chain]
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#--------------------------
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# Check for duplicates
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#--------------------------
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if len(deepddg_df['mutationinformation'].duplicated().value_counts())> 1:
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print("\nFAIL: Duplicates detected in DeepDDG infile"
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, "\nNo. of duplicates:"
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, deepddg_df['mutationinformation'].duplicated().value_counts()[1]
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, "\nformat deepDDG infile before proceeding")
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sys.exit()
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else:
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print("\nPASS: No duplicates detected in DeepDDG infile")
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#--------------------------
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# Drop chain id col as other targets don't have itCheck for duplicates
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#--------------------------
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col_to_drop = ['chain_id']
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deepddg_df = deepddg_df.drop(col_to_drop, axis = 1)
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#%%=============================================================================
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# Now merges begin
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#%%=============================================================================
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@ -311,28 +382,83 @@ get_aa_3lower(df = mcsm_df
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#mcsm_df.columns = mcsm_df.columns.str.lower()
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# foldx_df.shape
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#mcsm_foldx_dfs = combine_dfs_with_checks(mcsm_df, foldx_df, my_join = o_join)
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#mcsm_foldx_dfs = combine_dfs_with_checks(mcsm_df, foldx_df, my_join = "outer")
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merging_cols_m1 = detect_common_cols(mcsm_df, foldx_df)
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mcsm_foldx_dfs = pd.merge(mcsm_df, foldx_df, on = merging_cols_m1, how = o_join)
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mcsm_foldx_dfs = pd.merge(mcsm_df
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, foldx_df
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, on = merging_cols_m1
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, how = "outer")
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ncols_m1 = len(mcsm_foldx_dfs.columns)
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print('\n\nResult of first merge:', mcsm_foldx_dfs.shape
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, '\n===================================================================')
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mcsm_foldx_dfs[merging_cols_m1].apply(len)
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mcsm_foldx_dfs[merging_cols_m1].apply(len) == len(mcsm_foldx_dfs)
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#%% for embB and any other targets where mCSM-lig hasn't run for
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# get the empty cells to be full of meaningful info
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if mcsm_foldx_dfs.loc[:,'wild_type': 'mut_aa_3lower'].isnull().values.any():
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print ("NAs detected in mcsm cols after merge")
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##############################
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# Extract relevant col values
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# code to one
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##############################
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# wt_reg = r'(^[A-Z]{1})'
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# print('wild_type:', wt_reg)
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# mut_reg = r'[0-9]+(\w{1})$'
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# print('mut type:', mut_reg)
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mcsm_foldx_dfs['wild_type'] = mcsm_foldx_dfs.loc[:,'mutationinformation'].str.extract(r'(^[A-Z]{1})')
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mcsm_foldx_dfs['position'] = mcsm_foldx_dfs.loc[:,'mutationinformation'].str.extract(r'([0-9]+)')
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mcsm_foldx_dfs['mutant_type'] = mcsm_foldx_dfs.loc[:,'mutationinformation'].str.extract(r'[0-9]+([A-Z]{1})$')
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# BEWARE: Bit of logic trap i.e if nan comes first
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# in chain column, then nan will be populated!
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#df['foo'] = df['chain'].unique()[0]
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mcsm_foldx_dfs['chain'] = np.where(mcsm_foldx_dfs[['chain']].isnull().all(axis=1)
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, mcsm_foldx_dfs['chain'].unique()[0]
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, mcsm_foldx_dfs['chain'])
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mcsm_foldx_dfs['ligand_id'] = np.where(mcsm_foldx_dfs[['ligand_id']].isnull().all(axis=1)
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, mcsm_foldx_dfs['ligand_id'].unique()[0]
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, mcsm_foldx_dfs['ligand_id'])
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#--------------------------------------------------------------------------
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mcsm_foldx_dfs['wild_pos'] = mcsm_foldx_dfs.loc[:,'wild_type'] + mcsm_foldx_dfs.loc[:,'position'].astype(int).apply(str)
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mcsm_foldx_dfs['wild_chain_pos'] = mcsm_foldx_dfs.loc[:,'wild_type'] + mcsm_foldx_dfs.loc[:,'chain'] + mcsm_foldx_dfs.loc[:,'position'].astype(int).apply(str)
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#############
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# Map 1 letter
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# code to 3Upper
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#############
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# initialise a sub dict that is lookup dict for
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# 3-LETTER aa code to 1-LETTER aa code
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lookup_dict = dict()
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for k, v in oneletter_aa_dict.items():
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lookup_dict[k] = v['three_letter_code_lower']
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wt = mcsm_foldx_dfs['wild_type'].squeeze() # converts to a series that map works on
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mcsm_foldx_dfs['wt_aa_3lower'] = wt.map(lookup_dict)
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mut = mcsm_foldx_dfs['mutant_type'].squeeze()
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mcsm_foldx_dfs['mut_aa_3lower'] = mut.map(lookup_dict)
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#%%
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print('==================================='
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, '\nSecond merge: mcsm_foldx_dfs + deepddg'
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, '\n===================================')
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#deepddg_df = pd.read_csv(infile_deepddg, sep = ',')
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#deepddg_df.columns
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# merge with mcsm_foldx_dfs and deepddg_df
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mcsm_foldx_deepddg_dfs = pd.merge(mcsm_foldx_dfs, deepddg_df, on = 'mutationinformation', how = l_join)
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mcsm_foldx_deepddg_dfs = pd.merge(mcsm_foldx_dfs
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, deepddg_df
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, on = 'mutationinformation'
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, how = "left")
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mcsm_foldx_deepddg_dfs['deepddg_outcome'].value_counts()
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ncols_deepddg_merge = len(mcsm_foldx_deepddg_dfs.columns)
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mcsm_foldx_deepddg_dfs['position'] = mcsm_foldx_deepddg_dfs['position'].astype('int64')
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#%%============================================================================
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print('==================================='
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, '\Third merge: dssp + kd'
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@ -342,9 +468,12 @@ dssp_df.shape
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kd_df.shape
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rd_df.shape
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#dssp_kd_dfs = combine_dfs_with_checks(dssp_df, kd_df, my_join = o_join)
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#dssp_kd_dfs = combine_dfs_with_checks(dssp_df, kd_df, my_join = "outer")
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merging_cols_m2 = detect_common_cols(dssp_df, kd_df)
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dssp_kd_dfs = pd.merge(dssp_df, kd_df, on = merging_cols_m2, how = o_join)
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dssp_kd_dfs = pd.merge(dssp_df
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, kd_df
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, on = merging_cols_m2
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, how = "outer")
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print('\n\nResult of third merge:', dssp_kd_dfs.shape
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, '\n===================================================================')
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@ -353,10 +482,12 @@ print('==================================='
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, '\nFourth merge: third merge + rd_df'
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, '\ndssp_kd_dfs + rd_df'
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, '\n===================================')
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#dssp_kd_rd_dfs = combine_dfs_with_checks(dssp_kd_dfs, rd_df, my_join = o_join)
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#dssp_kd_rd_dfs = combine_dfs_with_checks(dssp_kd_dfs, rd_df, my_join = "outer")
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merging_cols_m3 = detect_common_cols(dssp_kd_dfs, rd_df)
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dssp_kd_rd_dfs = pd.merge(dssp_kd_dfs, rd_df, on = merging_cols_m3
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, how = o_join)
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dssp_kd_rd_dfs = pd.merge(dssp_kd_dfs
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, rd_df
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, on = merging_cols_m3
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, how = "outer")
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ncols_m3 = len(dssp_kd_rd_dfs.columns)
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@ -369,23 +500,40 @@ print('======================================='
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, '\nFifth merge: Second merge + fourth merge'
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, '\nmcsm_foldx_dfs + dssp_kd_rd_dfs'
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, '\n=======================================')
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#combined_df = combine_dfs_with_checks(mcsm_foldx_dfs, dssp_kd_rd_dfs, my_join = i_join)
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#combined_df = combine_dfs_with_checks(mcsm_foldx_dfs, dssp_kd_rd_dfs, my_join = "inner")
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#merging_cols_m4 = detect_common_cols(mcsm_foldx_dfs, dssp_kd_rd_dfs)
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#combined_df = pd.merge(mcsm_foldx_dfs, dssp_kd_rd_dfs, on = merging_cols_m4, how = i_join)
|
||||
#combined_df = pd.merge(mcsm_foldx_dfs, dssp_kd_rd_dfs, on = merging_cols_m4, how = "inner")
|
||||
#combined_df_expected_cols = ncols_m1 + ncols_m3 - len(merging_cols_m4)
|
||||
|
||||
# with deepddg values
|
||||
merging_cols_m4 = detect_common_cols(mcsm_foldx_deepddg_dfs, dssp_kd_rd_dfs)
|
||||
combined_df = pd.merge(mcsm_foldx_deepddg_dfs, dssp_kd_rd_dfs, on = merging_cols_m4, how = i_join)
|
||||
combined_df = pd.merge(mcsm_foldx_deepddg_dfs
|
||||
, dssp_kd_rd_dfs
|
||||
, on = merging_cols_m4
|
||||
, how = "inner")
|
||||
|
||||
combined_df_expected_cols = ncols_deepddg_merge + ncols_m3 - len(merging_cols_m4)
|
||||
|
||||
if len(combined_df) == len(mcsm_df) and len(combined_df.columns) == combined_df_expected_cols:
|
||||
# FIXME: check logic, doesn't effect anything else!
|
||||
if not gene == "embB":
|
||||
print("\nGene is:", gene)
|
||||
if len(combined_df) == len(mcsm_df) and len(combined_df.columns) == combined_df_expected_cols:
|
||||
print('PASS: successfully combined 5 dfs'
|
||||
, '\nNo. of rows combined_df:', len(combined_df)
|
||||
, '\nNo. of cols combined_df:', len(combined_df.columns))
|
||||
else:
|
||||
sys.exit('FAIL: check individual df merges')
|
||||
else:
|
||||
#sys.exit('FAIL: check individual df merges')
|
||||
print("\nGene is:", gene
|
||||
, "\ncombined_df length:", len(combined_df)
|
||||
, "\nmcsm_df_length:", len(mcsm_df)
|
||||
)
|
||||
if len(combined_df.columns) == combined_df_expected_cols:
|
||||
print('PASS: successfully combined 5 dfs'
|
||||
, '\nNo. of rows combined_df:', len(combined_df)
|
||||
, '\nNo. of cols combined_df:', len(combined_df.columns))
|
||||
else:
|
||||
sys.exit('FAIL: check individual merges')
|
||||
|
||||
print('\nResult of Fourth merge:', combined_df.shape
|
||||
, '\n===================================================================')
|
||||
|
|
@ -401,7 +549,7 @@ combined_df['chain'].equals(combined_df['chain_id'])
|
|||
combined_df['wild_type'].equals(combined_df['wild_type_kd']) # has nan
|
||||
combined_df['wild_type'].equals(combined_df['wild_type_dssp'])
|
||||
|
||||
#sanity check
|
||||
# sanity check
|
||||
foo = combined_df[['wild_type', 'wild_type_kd', 'wt_3letter_caps', 'wt_aa_3lower', 'mut_aa_3lower']]
|
||||
|
||||
# Drop cols
|
||||
|
|
@ -455,7 +603,11 @@ afor_df = afor_df.drop(['position'], axis = 1)
|
|||
afor_cols = afor_df.columns
|
||||
|
||||
# merge
|
||||
combined_stab_afor = pd.merge(combined_df_clean, afor_df, on = merging_cols_m5, how = l_join)
|
||||
combined_stab_afor = pd.merge(combined_df_clean
|
||||
, afor_df
|
||||
, on = merging_cols_m5
|
||||
, how = "left")
|
||||
|
||||
comb_afor_df_cols = combined_stab_afor.columns
|
||||
|
||||
comb_afor_expected_cols = len(combined_df_clean.columns) + len(afor_df.columns) - len(merging_cols_m5)
|
||||
|
|
@ -467,15 +619,23 @@ if len(combined_stab_afor) == len(combined_df_clean) and len(combined_stab_afor.
|
|||
else:
|
||||
sys.exit('\nFAIL: check individual df merges')
|
||||
|
||||
print('\n\nResult of Fourth merge:', combined_stab_afor.shape
|
||||
print('\n\nResult of Fifth merge:', combined_stab_afor.shape
|
||||
, '\n===================================================================')
|
||||
|
||||
combined_stab_afor[merging_cols_m5].apply(len)
|
||||
combined_stab_afor[merging_cols_m5].apply(len) == len(combined_stab_afor)
|
||||
|
||||
if len(combined_stab_afor) - combined_stab_afor['mutation'].isna().sum() == len(afor_df):
|
||||
print('\nPASS: Merge successful for af and or'
|
||||
, '\nNo. of nsSNPs with valid ORs: ', len(afor_df))
|
||||
if (len(combined_stab_afor) - combined_stab_afor['mutation'].isna().sum()) == len(afor_df):
|
||||
print('\nPASS: Merge successful for af and or with matched numbers')
|
||||
|
||||
if len(combined_stab_afor) - combined_stab_afor['mutation'].isna().sum() == len(afor_df)-len(afor_df[~afor_df['mutation'].isin(combined_stab_afor['mutation'])]):
|
||||
print("\nMismatched numbers, OR df has extra snps not found in mcsm df"
|
||||
, "\nNo. of nsSNPs with valid ORs:", len(afor_df)
|
||||
, "\nNo. of mcsm nsSNPs: ", len(combined_df_clean)
|
||||
, "\nNo. of OR nsSNPs not in mCSM df:"
|
||||
, len(afor_df[~afor_df['mutation'].isin(combined_stab_afor['mutation'])])
|
||||
, "\nWriting these mutations to file:")
|
||||
orsnps_notmcsm = afor_df[~afor_df['mutation'].isin(combined_stab_afor['mutation'])]
|
||||
else:
|
||||
sys.exit('\nFAIL: merge unsuccessful for af and or')
|
||||
|
||||
|
|
@ -486,7 +646,7 @@ outfile_comb_afor = outdir + '/' + out_filename_comb_afor
|
|||
print('Output filename:', outfile_comb_afor
|
||||
, '\n===================================================================')
|
||||
|
||||
# # write csv
|
||||
# write csv
|
||||
print('Writing file: combined stability and afor')
|
||||
combined_stab_afor.to_csv(outfile_comb_afor, index = False)
|
||||
print('\nFinished writing file:'
|
||||
|
|
@ -494,7 +654,20 @@ print('\nFinished writing file:'
|
|||
, '\nNo. of cols:', combined_stab_afor.shape[1])
|
||||
#%%============================================================================
|
||||
# combine dynamut, dynamut2, and mcsm_na
|
||||
dfs_list = [dynamut_df, dynamut2_df, mcsm_na_df]
|
||||
#dfs_list = [dynamut_df, dynamut2_df, mcsm_na_df] # gid
|
||||
|
||||
if gene.lower() == "pnca":
|
||||
dfs_list = [dynamut_df, dynamut2_df]
|
||||
if gene.lower() == "gid":
|
||||
dfs_list = [dynamut_df, dynamut2_df, mcsm_na_df]
|
||||
if gene.lower() == "embb":
|
||||
dfs_list = [dynamut2_df, mcsm_ppi2_df]
|
||||
if gene.lower() == "katg":
|
||||
dfs_list = [dynamut2_df]
|
||||
if gene.lower() == "rpob":
|
||||
dfs_list = [dynamut2_df]
|
||||
if gene.lower() == "alr":
|
||||
dfs_list = [dynamut2_df, mcsm_ppi2_df]
|
||||
|
||||
dfs_merged = reduce(lambda left,right: pd.merge(left
|
||||
, right
|
||||
|
|
@ -514,7 +687,7 @@ len(combined_stab_afor.columns)
|
|||
combined_all_params = pd.merge(combined_stab_afor
|
||||
, dfs_merged_clean
|
||||
, on = merging_cols_m6
|
||||
, how = i_join)
|
||||
, how = "inner")
|
||||
|
||||
expected_ncols = len(dfs_merged_clean.columns) + len(combined_stab_afor.columns) - len(merging_cols_m6)
|
||||
expected_nrows = len(combined_stab_afor)
|
||||
|
|
|
|||
|
|
@ -70,7 +70,6 @@ arg_parser.add_argument('-m', '--make_dirs', help = 'Make dir for input and outp
|
|||
|
||||
arg_parser.add_argument('--debug', action ='store_true', help = 'Debug Mode')
|
||||
|
||||
|
||||
args = arg_parser.parse_args()
|
||||
#=======================================================================
|
||||
#%% variable assignment: input and output paths & filenames
|
||||
|
|
|
|||
|
|
@ -117,12 +117,20 @@ deepddg_df['deepddg_outcome'].value_counts()
|
|||
len(deepddg_df.loc[deepddg_df['deepddg'] < 0])
|
||||
len(deepddg_df.loc[deepddg_df['deepddg'] >= 0])
|
||||
|
||||
#----------------------------------------------
|
||||
# drop extra columns to allow clean merging
|
||||
deepddg_short_df = deepddg_df.drop(['chain_id', 'wild_type_deepddg', 'position', 'mutant_type_deepddg'], axis = 1)
|
||||
#----------------------------------------------
|
||||
#deepddg_short_df = deepddg_df.drop(['chain_id', 'wild_type_deepddg', 'position', 'mutant_type_deepddg'], axis = 1)
|
||||
|
||||
#----------------------------------------------
|
||||
# embb (where gene-target has > 1 chain)
|
||||
# include chain else the numbering will be messed up!
|
||||
#----------------------------------------------
|
||||
deepddg_short_df = deepddg_df.drop(['wild_type_deepddg', 'position', 'mutant_type_deepddg'], axis = 1)
|
||||
|
||||
# rearrange columns
|
||||
deepddg_short_df.columns
|
||||
deepddg_short_df = deepddg_short_df[["mutationinformation", "deepddg", "deepddg_outcome"]]
|
||||
deepddg_short_df = deepddg_short_df[["chain_id", "mutationinformation", "deepddg", "deepddg_outcome"]]
|
||||
|
||||
#%% combine with mcsm snps
|
||||
deepddg_mcsm_muts_dfs = pd.merge(deepddg_short_df
|
||||
|
|
|
|||
|
|
@ -45,8 +45,6 @@ arg_parser.add_argument('--debug', action='store_true', help = 'Debug Mode')
|
|||
args = arg_parser.parse_args()
|
||||
#=======================================================================
|
||||
#%% variable assignment: input and output
|
||||
#drug = 'pyrazinamide'
|
||||
#gene = 'pncA'
|
||||
drug = args.drug
|
||||
gene = args.gene
|
||||
gene_match = gene + '_p.'
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue