added foldx_scaled and deepddg_scaled values added to combine_df.py and also used that script to merge all the dfs so that merged_df2 and merged_df3 are infact what we need for downstream processing
This commit is contained in:
parent
dda5d1ea93
commit
4ba4ff602e
5 changed files with 354 additions and 977 deletions
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@ -41,6 +41,7 @@ import pandas as pd
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from pandas import DataFrame
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import numpy as np
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import argparse
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from functools import reduce
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#=======================================================================
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#%% specify input and curr dir
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homedir = os.path.expanduser('~')
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@ -92,19 +93,6 @@ outdir = args.output_dir
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gene_match = gene + '_p.'
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print('mut pattern for gene', gene, ':', gene_match)
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# !"Redundant, now that improvements have been made!
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# See section "REGEX"
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# nssnp_match = gene_match +'[A-Za-z]{3}[0-9]+[A-Za-z]{3}'
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# print('nsSNP for gene', gene, ':', nssnp_match)
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# wt_regex = gene_match.lower()+'([A-Za-z]{3})'
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# print('wt regex:', wt_regex)
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# mut_regex = r'[0-9]+(\w{3})$'
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# print('mt regex:', mut_regex)
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# pos_regex = r'([0-9]+)'
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# print('position regex:', pos_regex)
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#%%=======================================================================
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#==============
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# directories
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@ -122,49 +110,52 @@ if not outdir:
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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_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 = 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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infile_mcsm = outdir + in_filename_mcsm
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infile_foldx = outdir + in_filename_foldx
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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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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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print('\nInput path:', indir
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, '\nOutput path:', outdir, '\n'
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, '\nInput filename mcsm:', infile_mcsm
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, '\nInput filename foldx:', infile_foldx, '\n'
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, '\nInput filename deepddg', infile_deepddg , '\n'
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, '\nInput filename dssp:', infile_dssp
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, '\nInput filename kd:', infile_kd
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, '\nInput filename rd', infile_rd
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#, '\nInput filename snp info:', infile_snpinfo, '\n'
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, '\nInput filename af or:', infile_afor
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#, '\nInput filename afor kinship:', infile_afor_kin
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, '\n============================================================')
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# read csv
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mcsm_df = pd.read_csv(infile_mcsm, sep = ',')
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foldx_df = pd.read_csv(infile_foldx , sep = ',')
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deepddg_df = pd.read_csv(infile_deepddg, sep = ',')
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dssp_df = pd.read_csv(infile_dssp, sep = ',')
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kd_df = pd.read_csv(infile_kd, sep = ',')
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rd_df = pd.read_csv(infile_rd, sep = ',')
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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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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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mcsm_f_snps = pd.read_csv(infile_mcsm_f_snps, sep = ',', names = ['mutationinformation'], header = None)
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#=======
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# output
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#=======
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out_filename_comb = gene.lower() + '_all_params.csv'
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outfile_comb = outdir + '/' + out_filename_comb
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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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@ -174,12 +165,101 @@ 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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#=====================
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# some preprocessing
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#=====================
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#-------------
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# FoldX
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#-------------
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foldx_df.shape
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#=======================
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# scale foldx values
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#=======================
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# Rescale values in Foldx_change col b/w -1 and 1 so negative numbers
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# stay neg and pos numbers stay positive
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foldx_min = foldx_df['ddg'].min()
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foldx_max = foldx_df['ddg'].max()
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foldx_min
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foldx_max
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foldx_scale = lambda x : x/abs(foldx_min) if x < 0 else (x/foldx_max if x >= 0 else 'failed')
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foldx_df['foldx_scaled'] = foldx_df['ddg'].apply(foldx_scale)
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print('Raw foldx scores:\n', foldx_df['ddg']
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, '\n---------------------------------------------------------------'
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, '\nScaled foldx scores:\n', foldx_df['foldx_scaled'])
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# additional check added
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fsmi = foldx_df['foldx_scaled'].min()
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fsma = foldx_df['foldx_scaled'].max()
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c = foldx_df[foldx_df['ddg']>=0].count()
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foldx_pos = c.get(key = 'ddg')
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c2 = foldx_df[foldx_df['foldx_scaled']>=0].count()
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foldx_pos2 = c2.get(key = 'foldx_scaled')
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if foldx_pos == foldx_pos2 and fsmi == -1 and fsma == 1:
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print('\nPASS: Foldx values scaled correctly b/w -1 and 1')
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else:
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print('\nFAIL: Foldx values scaled numbers MISmatch'
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, '\nExpected number:', foldx_pos
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, '\nGot:', foldx_pos2
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, '\n======================================================')
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# rename ddg column to ddg_foldx
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foldx_df['ddg']
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foldx_df = foldx_df.rename(columns = {'ddg':'ddg_foldx'})
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foldx_df['ddg_foldx']
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#-------------
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# Deepddg
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#-------------
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deepddg_df.shape
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#=======================
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# scale Deepddg values
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#=======================
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# Rescale values in deepddg_change col b/w -1 and 1 so negative numbers
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# stay neg and pos numbers stay positive
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deepddg_min = deepddg_df['deepddg'].min()
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deepddg_max = deepddg_df['deepddg'].max()
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deepddg_scale = lambda x : x/abs(deepddg_min) if x < 0 else (x/deepddg_max if x >= 0 else 'failed')
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deepddg_df['deepddg_scaled'] = deepddg_df['deepddg'].apply(deepddg_scale)
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print('Raw deepddg scores:\n', deepddg_df['deepddg']
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, '\n---------------------------------------------------------------'
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, '\nScaled deepddg scores:\n', deepddg_df['deepddg_scaled'])
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# additional check added
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dsmi = deepddg_df['deepddg_scaled'].min()
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dsma = deepddg_df['deepddg_scaled'].max()
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c = deepddg_df[deepddg_df['deepddg']>=0].count()
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deepddg_pos = c.get(key = 'deepddg')
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c2 = deepddg_df[deepddg_df['deepddg_scaled']>=0].count()
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deepddg_pos2 = c2.get(key = 'deepddg_scaled')
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if deepddg_pos == deepddg_pos2 and dsmi == -1 and dsma == 1:
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print('\nPASS: deepddg values scaled correctly b/w -1 and 1')
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else:
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print('\nFAIL: deepddg values scaled numbers MISmatch'
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, '\nExpected number:', deepddg_pos
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, '\nGot:', deepddg_pos2
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, '\n======================================================')
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#%%=============================================================================
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# Now merges begin
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#%%=============================================================================
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print('==================================='
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, '\nFirst merge: mcsm + foldx'
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, '\n===================================')
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mcsm_df = pd.read_csv(infile_mcsm, sep = ',')
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mcsm_df.shape
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# add 3 lowercase aa code for wt and mutant
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get_aa_3lower(df = mcsm_df
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@ -189,7 +269,7 @@ get_aa_3lower(df = mcsm_df
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, col_mut = 'mut_aa_3lower')
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#mcsm_df.columns = mcsm_df.columns.str.lower()
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foldx_df = pd.read_csv(infile_foldx , sep = ',')
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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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merging_cols_m1 = detect_common_cols(mcsm_df, foldx_df)
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@ -205,8 +285,8 @@ 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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#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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@ -218,9 +298,9 @@ print('==================================='
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, '\Third merge: dssp + kd'
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, '\n===================================')
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dssp_df = pd.read_csv(infile_dssp, sep = ',')
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kd_df = pd.read_csv(infile_kd, sep = ',')
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rd_df = pd.read_csv(infile_rd, sep = ',')
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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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merging_cols_m2 = detect_common_cols(dssp_df, kd_df)
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@ -308,8 +388,8 @@ print('\n======================================='
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, '\ncombined_df_clean + afor_df '
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, '\n=======================================')
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afor_df = pd.read_csv(infile_afor, sep = ',')
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afor_cols = afor_df.columns
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afor_df.shape
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# create a mapping from the gwas mutation column i.e <gene_match>_abcXXXrst
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#----------------------
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@ -360,16 +440,60 @@ else:
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sys.exit('\nFAIL: merge unsuccessful for af and or')
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#%%============================================================================
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# Output columns
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# Output columns: when dynamut, dynamut2 and others weren't being combined
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out_filename_comb_afor = gene.lower() + '_comb_afor.csv'
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outfile_comb_afor = outdir + '/' + out_filename_comb_afor
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print('Output filename:', outfile_comb_afor
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, '\n===================================================================')
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# write csv
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# # write csv
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print('Writing file: combined stability and afor')
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combined_stab_afor.to_csv(outfile_comb_afor, index = False)
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print('\nFinished writing file:'
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, '\nNo. of rows:', combined_stab_afor.shape[0]
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, '\nNo. of cols:', combined_stab_afor.shape[1])
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#%% end of script
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#%%============================================================================
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# combine dynamut, dynamut2, and mcsm_na
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dfs_list = [dynamut_df, dynamut2_df, mcsm_na_df]
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dfs_merged = reduce(lambda left,right: pd.merge(left
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, right
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, on = ['mutationinformation']
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, how = 'inner')
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, dfs_list)
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# drop excess columns
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drop_cols = detect_common_cols(dfs_merged, combined_stab_afor)
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drop_cols.remove('mutationinformation')
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dfs_merged_clean = dfs_merged.drop(drop_cols, axis = 1)
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merging_cols_m6 = detect_common_cols(dfs_merged_clean, combined_stab_afor)
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len(dfs_merged_clean.columns)
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len(combined_stab_afor.columns)
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combined_all_params = pd.merge(combined_stab_afor
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, dfs_merged_clean
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, on = merging_cols_m6
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, how = i_join)
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expected_ncols = len(dfs_merged_clean.columns) + len(combined_stab_afor.columns) - len(merging_cols_m6)
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expected_nrows = len(combined_stab_afor)
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if len(combined_all_params.columns) == expected_ncols and len(combined_all_params) == expected_nrows:
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print('\nPASS: All dfs combined')
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else:
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print('\nFAIL:lengths mismatch'
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, '\nExpected ncols:', expected_ncols
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, '\nGot:', len(dfs_merged_clean.columns)
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, '\nExpected nrows:', expected_nrows
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, '\nGot:', len(dfs_merged_clean) )
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#%% Done for gid on 10/09/2021
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# write csv
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print('Writing file: all params')
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combined_all_params.to_csv(outfile_comb, index = False)
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print('\nFinished writing file:'
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, '\nNo. of rows:', combined_all_params.shape[0]
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, '\nNo. of cols:', combined_all_params.shape[1])
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#%% end of script
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@ -8,11 +8,11 @@ setwd("~/git/LSHTM_analysis/scripts/plotting")
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getwd()
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source("Header_TT.R")
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source("../functions/my_pairs_panel.R") # with lower panel turned off
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source("../functions/plotting_globals.R")
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source("../functions/plotting_data.R")
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source("../functions/combining_dfs_plotting.R")
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source("../functions/bp_subcolours.R")
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# source("../functions/my_pairs_panel.R") # with lower panel turned off
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# source("../functions/plotting_globals.R")
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# source("../functions/plotting_data.R")
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# source("../functions/combining_dfs_plotting.R")
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# source("../functions/bp_subcolours.R")
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#********************
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# cmd args passed
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@ -41,8 +41,8 @@ import_dirs(drug, gene)
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#---------------------------
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if (!exists("infile_params") && exists("gene")){
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#if (!is.character(infile_params) && exists("gene")){ # when running as cmd
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#in_filename_params = paste0(tolower(gene), "_all_params.csv") #for pncA
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in_filename_params = paste0(tolower(gene), "_comb_afor.csv") # part combined for gid
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in_filename_params = paste0(tolower(gene), "_all_params.csv") #for pncA (and for gid finally) 10/09/21
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#in_filename_params = paste0(tolower(gene), "_comb_afor.csv") # part combined for gid
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infile_params = paste0(outdir, "/", in_filename_params)
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cat("\nInput file for mcsm comb data not specified, assuming filename: ", infile_params, "\n")
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}
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@ -91,369 +91,139 @@ merged_df3 = all_plot_dfs[[2]]
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merged_df2_comp = all_plot_dfs[[3]]
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merged_df3_comp = all_plot_dfs[[4]]
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#======================================================================
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# read other files
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infilename_dynamut = paste0("~/git/Data/", drug, "/output/dynamut_results/", gene
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, "_complex_dynamut_norm.csv")
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#TODO: Think! MOVE TO COMBINE or singular file for deepddg
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infilename_dynamut2 = paste0("~/git/Data/", drug, "/output/dynamut_results/dynamut2/", gene
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, "_complex_dynamut2_norm.csv")
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#============================
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# adding deepddg scaled values
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# scale data b/w -1 and 1
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#============================
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n = which(colnames(merged_df3) == "deepddg"); n
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infilename_mcsm_na = paste0("~/git/Data/", drug, "/output/mcsm_na_results/", gene
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, "_complex_mcsm_na_norm.csv")
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infilename_mcsm_f_snps <- paste0("~/git/Data/", drug, "/output/", gene
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, "_mcsm_formatted_snps.csv")
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||||
dynamut_df = read.csv(infilename_dynamut)
|
||||
dynamut2_df = read.csv(infilename_dynamut2)
|
||||
mcsm_na_df = read.csv(infilename_mcsm_na)
|
||||
mcsm_f_snps = read.csv(infilename_mcsm_f_snps, header = F)
|
||||
names(mcsm_f_snps) = "mutationinformation"
|
||||
my_min = min(merged_df3[,n]); my_min
|
||||
my_max = max(merged_df3[,n]); my_max
|
||||
|
||||
####################################################################
|
||||
# Data for subcols barplot (~heatmpa)
|
||||
####################################################################
|
||||
# can include: mutation, or_kin, pwald, af_kin
|
||||
cols_to_select = c("mutationinformation", "drtype"
|
||||
, "wild_type"
|
||||
, "position"
|
||||
, "mutant_type"
|
||||
, "chain", "ligand_id", "ligand_distance"
|
||||
, "duet_stability_change", "duet_outcome", "duet_scaled"
|
||||
, "ligand_affinity_change", "ligand_outcome", "affinity_scaled"
|
||||
, "ddg_foldx", "foldx_scaled", "foldx_outcome"
|
||||
, "deepddg", "deepddg_outcome" # comment out as not available for pnca
|
||||
, "asa", "rsa", "rd_values", "kd_values"
|
||||
, "af", "or_mychisq", "pval_fisher"
|
||||
, "or_fisher", "or_logistic", "pval_logistic"
|
||||
, "wt_prop_water", "mut_prop_water", "wt_prop_polarity", "mut_prop_polarity"
|
||||
, "wt_calcprop", "mut_calcprop")
|
||||
merged_df3$deepddg_scaled = ifelse(merged_df3[,n] < 0
|
||||
, merged_df3[,n]/abs(my_min)
|
||||
, merged_df3[,n]/my_max)
|
||||
# sanity check
|
||||
my_min = min(merged_df3$deepddg_scaled); my_min
|
||||
my_max = max(merged_df3$deepddg_scaled); my_max
|
||||
|
||||
#=======================
|
||||
# Data for sub colours
|
||||
# barplot: PS
|
||||
#=======================
|
||||
|
||||
cat("\nNo. of cols to select:", length(cols_to_select))
|
||||
|
||||
subcols_df_ps = merged_df3[, cols_to_select]
|
||||
|
||||
cat("\nNo of unique positions for ps:"
|
||||
, length(unique(subcols_df_ps$position)))
|
||||
|
||||
# add count_pos col that counts the no. of nsSNPS at a position
|
||||
setDT(subcols_df_ps)[, pos_count := .N, by = .(position)]
|
||||
|
||||
# should be a factor
|
||||
if (is.factor(subcols_df_ps$duet_outcome)){
|
||||
cat("\nDuet_outcome is factor")
|
||||
table(subcols_df_ps$duet_outcome)
|
||||
if (my_min == -1 && my_max == 1){
|
||||
cat("\nPASS: DeepDDG successfully scaled b/w -1 and 1"
|
||||
#, "\nProceeding with assigning deep outcome category")
|
||||
, "\n")
|
||||
}else{
|
||||
cat("\nConverting duet_outcome to factor")
|
||||
subcols_df_ps$duet_outcome = as.factor(subcols_df_ps$duet_outcome)
|
||||
table(subcols_df_ps$duet_outcome)
|
||||
cat("\nFAIL: could not scale DeepDDG ddg values"
|
||||
, "Aborting!")
|
||||
}
|
||||
|
||||
# should be -1 and 1
|
||||
min(subcols_df_ps$duet_scaled)
|
||||
max(subcols_df_ps$duet_scaled)
|
||||
|
||||
tapply(subcols_df_ps$duet_scaled, subcols_df_ps$duet_outcome, min)
|
||||
tapply(subcols_df_ps$duet_scaled, subcols_df_ps$duet_outcome, max)
|
||||
####################################################################
|
||||
# Data for combining other dfs
|
||||
####################################################################
|
||||
|
||||
# check unique values in normalised data
|
||||
cat("\nNo. of unique values in duet scaled, no rounding:"
|
||||
, length(unique(subcols_df_ps$duet_scaled)))
|
||||
source("other_dfs_data.R")
|
||||
|
||||
# No rounding
|
||||
my_grp = subcols_df_ps$duet_scaled; length(my_grp)
|
||||
####################################################################
|
||||
# Data for subcols barplot (~heatmap)
|
||||
####################################################################
|
||||
|
||||
# Add rounding is to be used
|
||||
n = 3
|
||||
subcols_df_ps$duet_scaledR = round(subcols_df_ps$duet_scaled, n)
|
||||
|
||||
cat("\nNo. of unique values in duet scaled", n, "places rounding:"
|
||||
, length(unique(subcols_df_ps$duet_scaledR)))
|
||||
|
||||
my_grp_r = subcols_df_ps$duet_scaledR # rounding
|
||||
|
||||
# Add grp cols
|
||||
subcols_df_ps$group <- paste0(subcols_df_ps$duet_outcome, "_", my_grp, sep = "")
|
||||
subcols_df_ps$groupR <- paste0(subcols_df_ps$duet_outcome, "_", my_grp_r, sep = "")
|
||||
|
||||
# Call the function to create the palette based on the group defined above
|
||||
subcols_ps <- ColourPalleteMulti(subcols_df_ps, "duet_outcome", "my_grp")
|
||||
subcolsR_ps <- ColourPalleteMulti(subcols_df_ps, "duet_outcome", "my_grp_r")
|
||||
|
||||
print(paste0("Colour palette generated for my_grp: ", length(subcols_ps), " colours"))
|
||||
print(paste0("Colour palette generated for my_grp_r: ", length(subcolsR_ps), " colours"))
|
||||
source("coloured_bp_data.R")
|
||||
|
||||
####################################################################
|
||||
# Data for logoplots
|
||||
####################################################################
|
||||
#-------------------------
|
||||
# choose df for logoplot
|
||||
#-------------------------
|
||||
logo_data = merged_df3
|
||||
#logo_data = merged_df3_comp
|
||||
|
||||
# quick checks
|
||||
colnames(logo_data)
|
||||
str(logo_data)
|
||||
source("logo_data.R")
|
||||
|
||||
c1 = unique(logo_data$position)
|
||||
nrow(logo_data)
|
||||
cat("No. of rows in my_data:", nrow(logo_data)
|
||||
, "\nDistinct positions corresponding to snps:", length(c1)
|
||||
, "\n===========================================================")
|
||||
#=======================================================================
|
||||
#==================
|
||||
# logo data: OR
|
||||
#==================
|
||||
foo = logo_data[, c("position"
|
||||
, "mutant_type","duet_scaled", "or_mychisq"
|
||||
, "mut_prop_polarity", "mut_prop_water")]
|
||||
s1 = c("\nSuccessfully sourced logo_data.R")
|
||||
cat(s1)
|
||||
|
||||
logo_data$log10or = log10(logo_data$or_mychisq)
|
||||
logo_data_plot = logo_data[, c("position"
|
||||
, "mutant_type", "or_mychisq", "log10or")]
|
||||
|
||||
logo_data_plot_or = logo_data[, c("position", "mutant_type", "or_mychisq")]
|
||||
wide_df_or <- logo_data_plot_or %>% spread(position, or_mychisq, fill = 0.0)
|
||||
|
||||
wide_df_or = as.matrix(wide_df_or)
|
||||
rownames(wide_df_or) = wide_df_or[,1]
|
||||
dim(wide_df_or)
|
||||
wide_df_or = wide_df_or[,-1]
|
||||
str(wide_df_or)
|
||||
|
||||
position_or = as.numeric(colnames(wide_df_or))
|
||||
|
||||
#==================
|
||||
# logo data: logOR
|
||||
#==================
|
||||
logo_data_plot_logor = logo_data[, c("position", "mutant_type", "log10or")]
|
||||
wide_df_logor <- logo_data_plot_logor %>% spread(position, log10or, fill = 0.0)
|
||||
|
||||
wide_df_logor = as.matrix(wide_df_logor)
|
||||
|
||||
rownames(wide_df_logor) = wide_df_logor[,1]
|
||||
wide_df_logor = subset(wide_df_logor, select = -c(1) )
|
||||
colnames(wide_df_logor)
|
||||
wide_df_logor_m = data.matrix(wide_df_logor)
|
||||
|
||||
rownames(wide_df_logor_m)
|
||||
colnames(wide_df_logor_m)
|
||||
|
||||
position_logor = as.numeric(colnames(wide_df_logor_m))
|
||||
|
||||
#===============================
|
||||
# logo data: multiple nsSNPs (>1)
|
||||
#=================================
|
||||
#require(data.table)
|
||||
|
||||
# get freq count of positions so you can subset freq<1
|
||||
setDT(logo_data)[, mut_pos_occurrence := .N, by = .(position)]
|
||||
|
||||
table(logo_data$position)
|
||||
table(logo_data$mut_pos_occurrence)
|
||||
|
||||
max_mut = max(table(logo_data$position))
|
||||
|
||||
# extract freq_pos > 1
|
||||
my_data_snp = logo_data[logo_data$mut_pos_occurrence!=1,]
|
||||
u = unique(my_data_snp$position)
|
||||
max_mult_mut = max(table(my_data_snp$position))
|
||||
|
||||
if (nrow(my_data_snp) == nrow(logo_data) - table(logo_data$mut_pos_occurrence)[[1]] ){
|
||||
|
||||
cat("PASS: positions with multiple muts extracted"
|
||||
, "\nNo. of mutations:", nrow(my_data_snp)
|
||||
, "\nNo. of positions:", length(u)
|
||||
, "\nMax no. of muts at any position", max_mult_mut)
|
||||
}else{
|
||||
cat("FAIL: positions with multiple muts could NOT be extracted"
|
||||
, "\nExpected:",nrow(logo_data) - table(logo_data$mut_pos_occurrence)[[1]]
|
||||
, "\nGot:", nrow(my_data_snp) )
|
||||
}
|
||||
|
||||
cat("\nNo. of sites with only 1 mutations:", table(logo_data$mut_pos_occurrence)[[1]])
|
||||
|
||||
#--------------------------------------
|
||||
# matrix for_mychisq mutant type
|
||||
# frequency of mutant type by position
|
||||
#---------------------------------------
|
||||
table(my_data_snp$mutant_type, my_data_snp$position)
|
||||
tab_mt = table(my_data_snp$mutant_type, my_data_snp$position)
|
||||
class(tab_mt)
|
||||
|
||||
# unclass to convert to matrix
|
||||
tab_mt = unclass(tab_mt)
|
||||
tab_mt = as.matrix(tab_mt, rownames = T)
|
||||
|
||||
# should be TRUE
|
||||
is.matrix(tab_mt)
|
||||
|
||||
rownames(tab_mt) #aa
|
||||
colnames(tab_mt) #pos
|
||||
|
||||
#-------------------------------------
|
||||
# matrix for wild type
|
||||
# frequency of wild type by position
|
||||
#-------------------------------------
|
||||
tab_wt = table(my_data_snp$wild_type, my_data_snp$position); tab_wt
|
||||
tab_wt = unclass(tab_wt)
|
||||
|
||||
# remove wt duplicates
|
||||
wt = my_data_snp[, c("position", "wild_type")]
|
||||
wt = wt[!duplicated(wt),]
|
||||
|
||||
tab_wt = table(wt$wild_type, wt$position); tab_wt # should all be 1
|
||||
|
||||
rownames(tab_wt)
|
||||
rownames(tab_wt)
|
||||
|
||||
identical(colnames(tab_mt), colnames(tab_wt))
|
||||
identical(ncol(tab_mt), ncol(tab_wt))
|
||||
|
||||
#----------------------------------
|
||||
# logo data OR: multiple nsSNPs (>1)
|
||||
#----------------------------------
|
||||
logo_data_or_mult = my_data_snp[, c("position", "mutant_type", "or_mychisq")]
|
||||
#wide_df_or <- logo_data_or %>% spread(position, or_mychisq, fill = 0.0)
|
||||
wide_df_or_mult <- logo_data_or_mult %>% spread(position, or_mychisq, fill = NA)
|
||||
|
||||
wide_df_or_mult = as.matrix(wide_df_or_mult)
|
||||
rownames(wide_df_or_mult) = wide_df_or_mult[,1]
|
||||
wide_df_or_mult = wide_df_or_mult[,-1]
|
||||
str(wide_df_or_mult)
|
||||
|
||||
position_or_mult = as.numeric(colnames(wide_df_or_mult))
|
||||
|
||||
####################################################################
|
||||
# Data for Corrplots
|
||||
####################################################################
|
||||
cat("\n=========================================="
|
||||
, "\nCORR PLOTS data: PS"
|
||||
, "\n===========================================")
|
||||
|
||||
df_ps = merged_df2
|
||||
|
||||
#--------------------
|
||||
# adding log cols : NEW UNCOMMENT
|
||||
#--------------------
|
||||
#df_ps$log10_or_mychisq = log10(df_ps$or_mychisq)
|
||||
#df_ps$neglog_pval_fisher = -log10(df_ps$pval_fisher)
|
||||
|
||||
##df_ps$log10_or_kin = log10(df_ps$or_kin)
|
||||
##df_ps$neglog_pwald_kin = -log10(df_ps$pwald_kin)
|
||||
|
||||
#df_ps$mutation_info_labels = ifelse(df_ps$mutation_info == dr_muts_col, 1, 0)
|
||||
|
||||
#----------------------------
|
||||
# columns for corr plots:PS
|
||||
#----------------------------
|
||||
# subset data to generate pairwise correlations
|
||||
cols_to_select = c("mutationinformation"
|
||||
, "duet_scaled"
|
||||
, "foldx_scaled"
|
||||
#, "mutation_info_labels"
|
||||
, "asa"
|
||||
, "rsa"
|
||||
, "rd_values"
|
||||
, "kd_values"
|
||||
, "log10_or_mychisq"
|
||||
, "neglog_pval_fisher"
|
||||
##, "or_kin"
|
||||
##, "neglog_pwald_kin"
|
||||
, "af"
|
||||
##, "af_kin"
|
||||
, "duet_outcome"
|
||||
, drug)
|
||||
|
||||
corr_data_ps = df_ps[cols_to_select]
|
||||
|
||||
dim(corr_data_ps)
|
||||
|
||||
#--------------------------------------
|
||||
# assign nice colnames (for display)
|
||||
#--------------------------------------
|
||||
my_corr_colnames = c("Mutation"
|
||||
, "DUET"
|
||||
, "FoldX"
|
||||
#, "Mutation class"
|
||||
, "ASA"
|
||||
, "RSA"
|
||||
, "RD"
|
||||
, "KD"
|
||||
, "Log (OR)"
|
||||
, "-Log (P)"
|
||||
##, "Adjusted (OR)"
|
||||
##, "-Log (P wald)"
|
||||
, "MAF"
|
||||
##, "AF_kin"
|
||||
, "duet_outcome"
|
||||
, drug)
|
||||
|
||||
length(my_corr_colnames)
|
||||
|
||||
colnames(corr_data_ps)
|
||||
colnames(corr_data_ps) <- my_corr_colnames
|
||||
colnames(corr_data_ps)
|
||||
|
||||
start = 1
|
||||
end = which(colnames(corr_data_ps) == drug); end # should be the last column
|
||||
offset = 1
|
||||
|
||||
#===========================
|
||||
# Corr data for plots: PS
|
||||
# big_df ps: ~ merged_df2
|
||||
#===========================
|
||||
|
||||
#corr_ps_df2 = corr_data_ps[start:(end-offset)] # without drug
|
||||
corr_ps_df2 = corr_data_ps[start:end]
|
||||
head(corr_ps_df2)
|
||||
|
||||
#===========================
|
||||
# Corr data for plots: PS
|
||||
# short_df ps: ~merged_df3
|
||||
#===========================
|
||||
corr_ps_df3 = corr_ps_df2[!duplicated(corr_ps_df2$Mutation),]
|
||||
|
||||
na_or = sum(is.na(corr_ps_df3$`Log (OR)`))
|
||||
check1 = nrow(corr_ps_df3) - na_or
|
||||
|
||||
##na_adj_or = sum(is.na(corr_ps_df3$`adjusted (OR)`))
|
||||
##check2 = nrow(corr_ps_df3) - na_adj_or
|
||||
|
||||
if (nrow(corr_ps_df3) == nrow(merged_df3) && nrow(merged_df3_comp) == check1) {
|
||||
cat( "\nPASS: No. of rows for corr_ps_df3 match"
|
||||
, "\nPASS: No. of OR values checked: " , check1)
|
||||
} else {
|
||||
cat("\nFAIL: Numbers mismatch:"
|
||||
, "\nExpected nrows: ", nrow(merged_df3)
|
||||
, "\nGot: ", nrow(corr_ps_df3)
|
||||
, "\nExpected OR values: ", nrow(merged_df3_comp)
|
||||
, "\nGot: ", check1)
|
||||
}
|
||||
|
||||
rm(foo)
|
||||
####################################################################
|
||||
# Data for DM OM Plots: Long format dfs
|
||||
####################################################################
|
||||
|
||||
source("other_plots_data.R")
|
||||
#source("other_plots_data.R")
|
||||
|
||||
source("dm_om_data.R")
|
||||
|
||||
s2 = c("\nSuccessfully sourced other_plots_data.R")
|
||||
cat(s2)
|
||||
|
||||
####################################################################
|
||||
# Data for Lineage barplots: WF and LF dfs
|
||||
####################################################################
|
||||
|
||||
source("lineage_bp_data.R")
|
||||
source("lineage_data.R")
|
||||
|
||||
s3 = c("\nSuccessfully sourced lineage_data.R")
|
||||
cat(s3)
|
||||
|
||||
####################################################################
|
||||
# Data for corr plots:
|
||||
####################################################################
|
||||
# make sure the above script works because merged_df2_combined is needed
|
||||
source("corr_data.R")
|
||||
|
||||
s4 = c("\nSuccessfully sourced corr_data.R")
|
||||
cat(s4)
|
||||
|
||||
########################################################################
|
||||
# End of script
|
||||
########################################################################
|
||||
if ( all( length(s1), length(s2), length(s3), length(s4) ) >0 ){
|
||||
cat(
|
||||
"\n##################################################"
|
||||
, "\nSuccessful: get_plotting_dfs.R worked!"
|
||||
, "\n###################################################\n")
|
||||
} else {
|
||||
cat(
|
||||
"\n#################################################"
|
||||
, "\nFAIL: get_plotting_dfs.R didn't complete fully!Please check"
|
||||
, "\n###################################################\n" )
|
||||
}
|
||||
|
||||
########################################################################
|
||||
# clear excess variables
|
||||
rm(c1, c2, c3, c4, check1
|
||||
, curr_count, curr_total
|
||||
, cols_check
|
||||
, cols_to_select
|
||||
, cols_to_select_deepddg
|
||||
, cols_to_select_duet
|
||||
, cols_to_select_dynamut
|
||||
, cols_to_select_dynamut2
|
||||
, cols_to_select_encomddg
|
||||
, cols_to_select_encomdds
|
||||
, cols_to_select_mcsm
|
||||
, cols_to_select_mcsm_na
|
||||
, cols_to_select_sdm
|
||||
, infile_metadata
|
||||
, infile_params
|
||||
#, infilename_dynamut
|
||||
#, infilename_dynamut2
|
||||
#, infilename_mcsm_f_snps
|
||||
#, infilename_mcsm_na
|
||||
)
|
||||
|
||||
cat("\n######################################################\n"
|
||||
, "\nSuccessful: get_plotting_dfs.R worked!"
|
||||
, "\n###################################################\n")
|
||||
rm(pivot_cols
|
||||
, pivot_cols_deepddg
|
||||
, pivot_cols_duet
|
||||
, pivot_cols_dynamut
|
||||
, pivot_cols_dynamut2
|
||||
, pivot_cols_encomddg
|
||||
, pivot_cols_encomdds
|
||||
, pivot_cols_foldx
|
||||
, pivot_cols_mcsm
|
||||
, pivot_cols_mcsm_na
|
||||
, pivot_cols_n
|
||||
, pivot_cols_sdm)
|
||||
|
||||
rm(expected_cols
|
||||
, expected_ncols
|
||||
, expected_rows
|
||||
, expected_rows_lf
|
||||
, fact_cols)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -4,21 +4,10 @@
|
|||
# WF and LF data with lineage sample, and snp counts
|
||||
# sourced by get_plotting_dfs.R
|
||||
#########################################################
|
||||
# working dir and loading libraries
|
||||
# getwd()
|
||||
# setwd("~/git/LSHTM_analysis/scripts/plotting")
|
||||
# getwd()
|
||||
|
||||
# make cmd
|
||||
# globals
|
||||
# drug = "streptomycin"
|
||||
# gene = "gid"
|
||||
|
||||
# source("get_plotting_dfs.R")
|
||||
#=======================================================================
|
||||
#################################################
|
||||
#=================================================
|
||||
# Get data with lineage count, and snp diversity
|
||||
#################################################
|
||||
#=================================================
|
||||
table(merged_df2$lineage)
|
||||
|
||||
if (table(merged_df2$lineage == "")[[2]]) {
|
||||
|
|
@ -30,12 +19,12 @@ cat("\nMissing samples with lineage classification:", table(merged_df2$lineage =
|
|||
table(merged_df2$lineage_labels)
|
||||
class(merged_df2$lineage_labels); nlevels(merged_df2$lineage_labels)
|
||||
|
||||
##################################
|
||||
#==========================================
|
||||
# WF data: lineages with
|
||||
# snp count
|
||||
# total_samples
|
||||
# snp diversity (perc)
|
||||
##################################
|
||||
#==========================================
|
||||
sel_lineages = levels(merged_df2$lineage_labels)
|
||||
|
||||
lin_wf = data.frame(sel_lineages) #4, 1
|
||||
|
|
@ -67,9 +56,9 @@ lin_wf
|
|||
lin_wf$snp_diversity = lin_wf$num_snps_u/lin_wf$total_samples
|
||||
lin_wf
|
||||
|
||||
#=====================
|
||||
#----------------------
|
||||
# Add some formatting
|
||||
#=====================
|
||||
#----------------------
|
||||
# SNP diversity
|
||||
lin_wf$snp_diversity_f = round( (lin_wf$snp_diversity * 100), digits = 0)
|
||||
lin_wf$snp_diversity_f = paste0(lin_wf$snp_diversity_f, "%")
|
||||
|
|
@ -100,12 +89,12 @@ lin_wf$sel_lineages = factor(lin_wf$sel_lineages, c("L1"
|
|||
|
||||
levels(lin_wf$sel_lineages)
|
||||
|
||||
##################################
|
||||
#=================================
|
||||
# LF data: lineages with
|
||||
# snp count
|
||||
# total_samples
|
||||
# snp diversity (perc)
|
||||
##################################
|
||||
#=================================
|
||||
names(lin_wf)
|
||||
tot_cols = ncol(lin_wf)
|
||||
pivot_cols = c("sel_lineages", "snp_diversity", "snp_diversity_f")
|
||||
|
|
@ -153,3 +142,6 @@ lin_lf$sel_lineages = factor(lin_lf$sel_lineages, c("L1"
|
|||
, ""))
|
||||
|
||||
levels(lin_lf$sel_lineages)
|
||||
|
||||
################################################################
|
||||
|
||||
|
|
|
|||
|
|
@ -16,9 +16,9 @@ source("Header_TT.R") # also loads all my functions
|
|||
#===========
|
||||
# input
|
||||
#===========
|
||||
#drug = "streptomycin"
|
||||
#gene = "gid"
|
||||
source("get_plotting_dfs.R")
|
||||
drug = "streptomycin"
|
||||
gene = "gid"
|
||||
#source("get_plotting_dfs.R")
|
||||
|
||||
spec = matrix(c(
|
||||
"drug" , "d", 1, "character",
|
||||
|
|
@ -47,7 +47,7 @@ plot_lineage_dist_dm_om_ps = paste0(plotdir,"/", lineage_dist_dm_om_ps)
|
|||
|
||||
###########################
|
||||
# Data for plots
|
||||
# you need merged_df2 or merged_df2_comp
|
||||
# you need merged_df2_combined or merged_df2_combined_comp
|
||||
# since this is one-many relationship
|
||||
# i.e the same SNP can belong to multiple lineages
|
||||
# using the _comp dataset means
|
||||
|
|
@ -59,10 +59,12 @@ plot_lineage_dist_dm_om_ps = paste0(plotdir,"/", lineage_dist_dm_om_ps)
|
|||
# Data for plots
|
||||
#===================
|
||||
# quick checks
|
||||
table(merged_df2$mutation_info_labels); levels(merged_df2$lineage_labels)
|
||||
table(merged_df2$lineage_labels); levels(merged_df2$mutation_info_labels)
|
||||
table(merged_df2_combined$mutation_info_labels); levels(merged_df2_combined$lineage_labels)
|
||||
table(merged_df2_combined$lineage_labels); levels(merged_df2_combined$mutation_info_labels)
|
||||
|
||||
lin_dist_plot = merged_df2[merged_df2$lineage_labels%in%c("L1", "L2", "L3", "L4"),]
|
||||
sel_lineages = c("L1", "L2", "L3", "L4")
|
||||
|
||||
lin_dist_plot = merged_df2_combined[merged_df2_combined$lineage_labels%in%sel_lineages,]
|
||||
table(lin_dist_plot$lineage_labels); nlevels(lin_dist_plot$lineage_labels)
|
||||
|
||||
# refactor
|
||||
|
|
@ -79,29 +81,55 @@ table(lin_dist_plot$lineage_labels)#{RESULT: No of samples within lineage}
|
|||
length(unique(lin_dist_plot$mutationinformation))#{Result: No. of unique mutations selected lineages contribute to}
|
||||
length(lin_dist_plot$mutationinformation)
|
||||
|
||||
u2 = unique(merged_df2$mutationinformation)
|
||||
u2 = unique(merged_df2_combined$mutationinformation)
|
||||
u = unique(lin_dist_plot$mutationinformation)
|
||||
check = u2[!u2%in%u]; print(check) #{Muts not present within selected lineages}
|
||||
#-----------------------------------------------------------------------
|
||||
# without facet
|
||||
|
||||
my_x_and_t = c("duet_scaled", "mCSM-DUET")
|
||||
my_x_and_t = c("foldx_scaled", "FoldX")
|
||||
#my_x_and_t = c("deepddg_scaled", "DeepDDG")
|
||||
|
||||
my_x_and_t = c("ddg_dynamut2_scaled", "Dynamut2")
|
||||
my_x_and_t = c("ddg_dynamut_scaled", "Dynamut")
|
||||
|
||||
my_x_and_t = c("ddg_mcsm_scaled", "mCSM")
|
||||
my_x_and_t = c("ddg_sdm_scaled", "SDM")
|
||||
my_x_and_t = c("ddg_duet_scaled", "DUET-d")
|
||||
|
||||
my_x_and_t = c("ddg_encom_scaled", "EnCOM-Stability")
|
||||
my_x_and_t = c("dds_encom_scaled", "EnCOM-Flexibility")
|
||||
|
||||
my_x_and_t = c("mcsm_na_scaled", "mCSM-NA")
|
||||
|
||||
# TO DO
|
||||
my_x_and_t = c("affinity_scaled", "mCSM-Lig") #ligdist< 10
|
||||
|
||||
#=====================
|
||||
# Plot: without facet
|
||||
#=====================
|
||||
|
||||
linP_dm_om = lineage_distP(lin_dist_plot
|
||||
, with_facet = F
|
||||
, x_axis = "deepddg"
|
||||
, x_axis = my_x_and_t[1]
|
||||
, x_lab = my_x_and_t[2]
|
||||
, y_axis = "lineage_labels"
|
||||
, x_lab = "DeepDDG"
|
||||
, leg_label = "Mutation Class"
|
||||
)
|
||||
, with_facet = F)
|
||||
linP_dm_om
|
||||
|
||||
# with facet
|
||||
#=====================
|
||||
# Plot: with facet
|
||||
#=====================
|
||||
|
||||
linP_dm_om_facet = lineage_distP(lin_dist_plot
|
||||
, with_facet = T
|
||||
, facet_wrap_var = "mutation_info_labels"
|
||||
, leg_label = "Mutation Class"
|
||||
, leg_pos_wf = "none"
|
||||
, leg_dir_wf = "horizontal"
|
||||
|
||||
)
|
||||
, x_axis = my_x_and_t[1]
|
||||
, x_lab = my_x_and_t[2]
|
||||
, y_axis = "lineage_labels"
|
||||
, with_facet = T
|
||||
, facet_wrap_var = "mutation_info_labels"
|
||||
, leg_label = "Mutation Class"
|
||||
, leg_pos_wf = "none"
|
||||
, leg_dir_wf = "horizontal")
|
||||
linP_dm_om_facet
|
||||
|
||||
#=================
|
||||
|
|
@ -109,6 +137,7 @@ linP_dm_om_facet
|
|||
# without facet
|
||||
#=================
|
||||
svg(plot_lineage_dist_dm_om_ps)
|
||||
|
||||
linP_dm_om
|
||||
|
||||
dev.off()
|
||||
|
|
|
|||
|
|
@ -1,538 +0,0 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#########################################################
|
||||
# TASK: Script to format data for dm om plots:
|
||||
# generating LF data
|
||||
# sourced by get_plotting_dfs.R
|
||||
#########################################################
|
||||
# working dir and loading libraries
|
||||
# getwd()
|
||||
# setwd("~/git/LSHTM_analysis/scripts/plotting")
|
||||
# getwd()
|
||||
|
||||
# make cmd
|
||||
# globals
|
||||
# drug = "streptomycin"
|
||||
# gene = "gid"
|
||||
|
||||
# source("get_plotting_dfs.R")
|
||||
#=======================================================================
|
||||
# MOVE TO COMBINE or singular file for deepddg
|
||||
#
|
||||
# cols_to_select = c("mutation", "mutationinformation"
|
||||
# , "wild_type", "position", "mutant_type"
|
||||
# , "mutation_info")
|
||||
#
|
||||
# merged_df3_short = merged_df3[, cols_to_select]
|
||||
|
||||
# infilename_mcsm_f_snps <- paste0("~/git/Data/", drug, "/output/", gene
|
||||
# , "_mcsm_formatted_snps.csv")
|
||||
#
|
||||
# mcsm_f_snps<- read.csv(infilename_mcsm_f_snps, header = F)
|
||||
# names(mcsm_f_snps) <- "mutationinformation"
|
||||
|
||||
# write merged_df3 to generate structural figure on chimera
|
||||
#write.csv(merged_df3_short, "merged_df3_short.csv")
|
||||
#========================================================================
|
||||
# MOVE TO COMBINE or singular file for deepddg
|
||||
|
||||
#============================
|
||||
# adding deepddg scaled values
|
||||
# scale data b/w -1 and 1
|
||||
#============================
|
||||
n = which(colnames(merged_df3) == "deepddg"); n
|
||||
|
||||
my_min = min(merged_df3[,n]); my_min
|
||||
my_max = max(merged_df3[,n]); my_max
|
||||
|
||||
merged_df3$deepddg_scaled = ifelse(merged_df3[,n] < 0
|
||||
, merged_df3[,n]/abs(my_min)
|
||||
, merged_df3[,n]/my_max)
|
||||
# sanity check
|
||||
my_min = min(merged_df3$deepddg_scaled); my_min
|
||||
my_max = max(merged_df3$deepddg_scaled); my_max
|
||||
|
||||
if (my_min == -1 && my_max == 1){
|
||||
cat("\nPASS: DeepDDG successfully scaled b/w -1 and 1"
|
||||
#, "\nProceeding with assigning deep outcome category")
|
||||
, "\n")
|
||||
}else{
|
||||
cat("\nFAIL: could not scale DeepDDG ddg values"
|
||||
, "Aborting!")
|
||||
}
|
||||
|
||||
#========================================================================
|
||||
# cols to select
|
||||
|
||||
cols_mcsm_df <- merged_df3[, c("mutationinformation", "mutation"
|
||||
, "mutation_info", "position"
|
||||
, LigDist_colname
|
||||
, "duet_stability_change", "duet_scaled", "duet_outcome"
|
||||
, "ligand_affinity_change", "affinity_scaled", "ligand_outcome"
|
||||
, "ddg_foldx", "foldx_scaled", "foldx_outcome"
|
||||
, "deepddg", "deepddg_scaled", "deepddg_outcome"
|
||||
, "asa", "rsa"
|
||||
, "rd_values", "kd_values"
|
||||
, "log10_or_mychisq", "neglog_pval_fisher", "af")]
|
||||
|
||||
cols_mcsm_na_df <- mcsm_na_df[, c("mutationinformation"
|
||||
, "mcsm_na_affinity", "mcsm_na_scaled"
|
||||
, "mcsm_na_outcome")]
|
||||
# entire dynamut_df
|
||||
|
||||
cols_dynamut2_df <- dynamut2_df[, c("mutationinformation"
|
||||
, "ddg_dynamut2", "ddg_dynamut2_scaled"
|
||||
, "ddg_dynamut2_outcome")]
|
||||
|
||||
n_comb_cols = length(cols_mcsm_df) + length(cols_mcsm_na_df) +
|
||||
length(dynamut_df) + length(cols_dynamut2_df); n_comb_cols
|
||||
|
||||
i1<- intersect(names(cols_mcsm_df), names(cols_mcsm_na_df))
|
||||
i2<- intersect(names(dynamut_df), names(cols_dynamut2_df))
|
||||
merging_cols <- intersect(i1, i2)
|
||||
cat("\nmerging_cols:", merging_cols)
|
||||
|
||||
if (merging_cols == "mutationinformation") {
|
||||
cat("\nStage 1: Found common col between dfs, checking values in it...")
|
||||
c1 <- all(mcsm_f_snps[[merging_cols]]%in%cols_mcsm_df[[merging_cols]])
|
||||
c2 <- all(mcsm_f_snps[[merging_cols]]%in%cols_mcsm_na_df[[merging_cols]])
|
||||
c3 <- all(mcsm_f_snps[[merging_cols]]%in%dynamut_df[[merging_cols]])
|
||||
c4 <- all(mcsm_f_snps[[merging_cols]]%in%cols_dynamut2_df[[merging_cols]])
|
||||
cols_check <- c(c1, c2, c3, c4)
|
||||
expected_cols = n_comb_cols - ( length(cols_check) - 1)
|
||||
if (all(cols_check)){
|
||||
cat("\nStage 2: Proceeding with merging dfs:\n")
|
||||
comb_df <- Reduce(inner_join, list(cols_mcsm_df
|
||||
, cols_mcsm_na_df
|
||||
, dynamut_df
|
||||
, cols_dynamut2_df))
|
||||
comb_df_s = arrange(comb_df, position)
|
||||
|
||||
# if ( nrow(comb_df_s) == nrow(mcsm_f_snps) && ncol(comb_df_s) == expected_cols) {
|
||||
# cat("\Stage3, PASS: dfs merged sucessfully"
|
||||
# , "\nnrow of merged_df: ", nrow(comb_df_s)
|
||||
# , "\nncol of merged_df:", ncol(comb_df_s))
|
||||
# }
|
||||
|
||||
}
|
||||
}
|
||||
#names(comb_df_s)
|
||||
cat("\n!!!IT GOT TO HERE!!!!")
|
||||
#=======================================================================
|
||||
fact_cols = colnames(comb_df_s)[grepl( "_outcome|_info", colnames(comb_df_s) )]
|
||||
fact_cols
|
||||
lapply(comb_df_s[, fact_cols], class)
|
||||
comb_df_s[, fact_cols] <- lapply(comb_df_s[, fact_cols], as.factor)
|
||||
|
||||
if (any(lapply(comb_df_s[, fact_cols], class) == "character")){
|
||||
cat("\nChanging cols to factor")
|
||||
comb_df_s[, fact_cols] <- lapply(comb_df_s[, fact_cols],as.factor)
|
||||
if (all(lapply(comb_df_s[, fact_cols], class) == "factor")){
|
||||
cat("\nSuccessful: cols changed to factor")
|
||||
}
|
||||
}
|
||||
lapply(comb_df_s[, fact_cols], class)
|
||||
|
||||
#=======================================================================
|
||||
table(comb_df_s$mutation_info)
|
||||
|
||||
# further checks to make sure dr and other muts are indeed unique
|
||||
dr_muts = comb_df_s[comb_df_s$mutation_info == dr_muts_col,]
|
||||
dr_muts_names = unique(dr_muts$mutation)
|
||||
|
||||
other_muts = comb_df_s[comb_df_s$mutation_info == other_muts_col,]
|
||||
other_muts_names = unique(other_muts$mutation)
|
||||
|
||||
if ( table(dr_muts_names%in%other_muts_names)[[1]] == length(dr_muts_names) &&
|
||||
table(other_muts_names%in%dr_muts_names)[[1]] == length(other_muts_names) ){
|
||||
cat("PASS: dr and other muts are indeed unique")
|
||||
}else{
|
||||
cat("FAIL: dr and others muts are NOT unique!")
|
||||
quit()
|
||||
}
|
||||
|
||||
# pretty display names i.e. labels to reduce major code duplication later
|
||||
foo_cnames = data.frame(colnames(comb_df_s))
|
||||
names(foo_cnames) <- "old_name"
|
||||
|
||||
stability_suffix <- paste0(delta_symbol, delta_symbol, "G")
|
||||
flexibility_suffix <- paste0(delta_symbol, delta_symbol, "S")
|
||||
|
||||
lig_dn = paste0("Ligand distance (", angstroms_symbol, ")"); lig_dn
|
||||
duet_dn = paste0("DUET ", stability_suffix); duet_dn
|
||||
foldx_dn = paste0("FoldX ", stability_suffix); foldx_dn
|
||||
deepddg_dn = paste0("Deepddg " , stability_suffix); deepddg_dn
|
||||
mcsm_na_dn = paste0("mCSM-NA affinity ", stability_suffix); mcsm_na_dn
|
||||
dynamut_dn = paste0("Dynamut ", stability_suffix); dynamut_dn
|
||||
dynamut2_dn = paste0("Dynamut2 " , stability_suffix); dynamut2_dn
|
||||
encom_ddg_dn = paste0("EnCOM " , stability_suffix); encom_ddg_dn
|
||||
encom_dds_dn = paste0("EnCOM " , flexibility_suffix ); encom_dds_dn
|
||||
sdm_dn = paste0("SDM " , stability_suffix); sdm_dn
|
||||
mcsm_dn = paste0("mCSM " , stability_suffix ); mcsm_dn
|
||||
|
||||
# Change colnames of some columns using datatable
|
||||
comb_df_sl = comb_df_s
|
||||
names(comb_df_sl)
|
||||
|
||||
setnames(comb_df_sl
|
||||
, old = c("asa", "rsa", "rd_values", "kd_values"
|
||||
, "log10_or_mychisq", "neglog_pval_fisher", "af"
|
||||
, LigDist_colname
|
||||
, "duet_scaled"
|
||||
, "foldx_scaled"
|
||||
, "deepddg_scaled"
|
||||
, "mcsm_na_scaled"
|
||||
, "ddg_dynamut_scaled"
|
||||
, "ddg_dynamut2_scaled"
|
||||
, "ddg_encom_scaled"
|
||||
, "dds_encom_scaled"
|
||||
, "ddg_sdm"
|
||||
, "ddg_mcsm")
|
||||
|
||||
, new = c("ASA", "RSA", "RD", "KD"
|
||||
, "Log10 (OR)", "-Log (P)", "MAF"
|
||||
, lig_dn
|
||||
, duet_dn
|
||||
, foldx_dn
|
||||
, deepddg_dn
|
||||
, mcsm_na_dn
|
||||
, dynamut_dn
|
||||
, dynamut2_dn
|
||||
, encom_ddg_dn
|
||||
, encom_dds_dn
|
||||
, sdm_dn
|
||||
, mcsm_dn)
|
||||
)
|
||||
|
||||
foo_cnames <- cbind(foo_cnames, colnames(comb_df_sl))
|
||||
|
||||
# some more pretty labels
|
||||
table(comb_df_sl$mutation_info)
|
||||
|
||||
levels(comb_df_sl$mutation_info)[levels(comb_df_sl$mutation_info)==dr_muts_col] <- "DM"
|
||||
levels(comb_df_sl$mutation_info)[levels(comb_df_sl$mutation_info)==other_muts_col] <- "OM"
|
||||
|
||||
table(comb_df_sl$mutation_info)
|
||||
|
||||
#######################################################################
|
||||
#======================
|
||||
# Selecting dfs
|
||||
# with appropriate cols
|
||||
#=======================
|
||||
static_cols_start = c("mutationinformation"
|
||||
, "position"
|
||||
, "mutation"
|
||||
, "mutation_info")
|
||||
|
||||
static_cols_end = c(lig_dn
|
||||
, "ASA"
|
||||
, "RSA"
|
||||
, "RD"
|
||||
, "KD")
|
||||
|
||||
# ordering is important!
|
||||
|
||||
#########################################################################
|
||||
#==============
|
||||
# DUET: LF
|
||||
#==============
|
||||
cols_to_select_duet = c(static_cols_start, c("duet_outcome", duet_dn), static_cols_end)
|
||||
wf_duet = comb_df_sl[, cols_to_select_duet]
|
||||
|
||||
#pivot_cols_ps = cols_to_select_ps[1:5]; pivot_cols_ps
|
||||
pivot_cols_duet = cols_to_select_duet[1: (length(static_cols_start) + 1)]; pivot_cols_duet
|
||||
|
||||
expected_rows_lf = nrow(wf_duet) * (length(wf_duet) - length(pivot_cols_duet))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: duet
|
||||
lf_duet = gather(wf_duet
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(duet_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_duet) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for ", duet_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for duet")
|
||||
quit()
|
||||
}
|
||||
|
||||
############################################################################
|
||||
#==============
|
||||
# FoldX: LF
|
||||
#==============
|
||||
cols_to_select_foldx= c(static_cols_start, c("foldx_outcome", foldx_dn), static_cols_end)
|
||||
wf_foldx = comb_df_sl[, cols_to_select_foldx]
|
||||
|
||||
pivot_cols_foldx = cols_to_select_foldx[1: (length(static_cols_start) + 1)]; pivot_cols_foldx
|
||||
|
||||
expected_rows_lf = nrow(wf_foldx) * (length(wf_foldx) - length(pivot_cols_foldx))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: duet
|
||||
print("TESTXXXXXXXXXXXXXXXXXXXXX---------------------->>>>")
|
||||
lf_foldx <<- gather(wf_foldx
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(foldx_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_foldx) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for ", foldx_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for duet")
|
||||
quit()
|
||||
}
|
||||
|
||||
############################################################################
|
||||
#==============
|
||||
# Deepddg: LF
|
||||
#==============
|
||||
cols_to_select_deepddg = c(static_cols_start, c("deepddg_outcome", deepddg_dn), static_cols_end)
|
||||
wf_deepddg = comb_df_sl[, cols_to_select_deepddg]
|
||||
|
||||
pivot_cols_deepddg = cols_to_select_deepddg[1: (length(static_cols_start) + 1)]; pivot_cols_deepddg
|
||||
|
||||
expected_rows_lf = nrow(wf_deepddg) * (length(wf_deepddg) - length(pivot_cols_deepddg))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: duet
|
||||
lf_deepddg = gather(wf_deepddg
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(deepddg_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_deepddg) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for ", deepddg_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for duet")
|
||||
quit()
|
||||
}
|
||||
|
||||
############################################################################
|
||||
#==============
|
||||
# mCSM-NA: LF
|
||||
#==============
|
||||
cols_to_select_mcsm_na = c(static_cols_start, c("mcsm_na_outcome", mcsm_na_dn), static_cols_end)
|
||||
wf_mcsm_na = comb_df_sl[, cols_to_select_mcsm_na]
|
||||
|
||||
pivot_cols_mcsm_na = cols_to_select_mcsm_na[1: (length(static_cols_start) + 1)]; pivot_cols_mcsm_na
|
||||
|
||||
expected_rows_lf = nrow(wf_mcsm_na) * (length(wf_mcsm_na) - length(pivot_cols_mcsm_na))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: duet
|
||||
lf_mcsm_na = gather(wf_mcsm_na
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(mcsm_na_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_mcsm_na) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for ", mcsm_na_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for duet")
|
||||
quit()
|
||||
}
|
||||
|
||||
############################################################################
|
||||
#==============
|
||||
# Dynamut: LF
|
||||
#==============
|
||||
cols_to_select_dynamut = c(static_cols_start, c("ddg_dynamut_outcome", dynamut_dn), static_cols_end)
|
||||
wf_dynamut = comb_df_sl[, cols_to_select_dynamut]
|
||||
|
||||
pivot_cols_dynamut = cols_to_select_dynamut[1: (length(static_cols_start) + 1)]; pivot_cols_dynamut
|
||||
|
||||
expected_rows_lf = nrow(wf_dynamut) * (length(wf_dynamut) - length(pivot_cols_dynamut))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: duet
|
||||
lf_dynamut = gather(wf_dynamut
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(dynamut_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_dynamut) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for ", dynamut_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for duet")
|
||||
quit()
|
||||
}
|
||||
|
||||
############################################################################
|
||||
#==============
|
||||
# Dynamut2: LF
|
||||
#==============
|
||||
cols_to_select_dynamut2 = c(static_cols_start, c("ddg_dynamut2_outcome", dynamut2_dn), static_cols_end)
|
||||
|
||||
wf_dynamut2 = comb_df_sl[, cols_to_select_dynamut2]
|
||||
|
||||
pivot_cols_dynamut2 = cols_to_select_dynamut2[1: (length(static_cols_start) + 1)]; pivot_cols_dynamut2
|
||||
|
||||
expected_rows_lf = nrow(wf_dynamut2) * (length(wf_dynamut2) - length(pivot_cols_dynamut2))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: duet
|
||||
lf_dynamut2 = gather(wf_dynamut2
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(dynamut2_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_dynamut2) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for ", dynamut2_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for duet")
|
||||
quit()
|
||||
}
|
||||
|
||||
############################################################################
|
||||
#==============
|
||||
# EnCOM ddg: LF
|
||||
#==============
|
||||
cols_to_select_encomddg = c(static_cols_start, c("ddg_encom_outcome", encom_ddg_dn), static_cols_end)
|
||||
wf_encomddg = comb_df_sl[, cols_to_select_encomddg]
|
||||
|
||||
pivot_cols_encomddg = cols_to_select_encomddg[1: (length(static_cols_start) + 1)]; pivot_cols_encomddg
|
||||
|
||||
expected_rows_lf = nrow(wf_encomddg ) * (length(wf_encomddg ) - length(pivot_cols_encomddg))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: encomddg
|
||||
lf_encomddg = gather(wf_encomddg
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(encom_ddg_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_encomddg) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for ", encom_ddg_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for duet")
|
||||
quit()
|
||||
}
|
||||
############################################################################
|
||||
#==============
|
||||
# EnCOM dds: LF
|
||||
#==============
|
||||
cols_to_select_encomdds = c(static_cols_start, c("dds_encom_outcome", encom_dds_dn), static_cols_end)
|
||||
wf_encomdds = comb_df_sl[, cols_to_select_encomdds]
|
||||
|
||||
pivot_cols_encomdds = cols_to_select_encomdds[1: (length(static_cols_start) + 1)]; pivot_cols_encomdds
|
||||
|
||||
expected_rows_lf = nrow(wf_encomdds) * (length(wf_encomdds) - length(pivot_cols_encomdds))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: encomddg
|
||||
lf_encomdds = gather(wf_encomdds
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(encom_dds_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_encomdds) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for", encom_dds_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for duet")
|
||||
quit()
|
||||
}
|
||||
|
||||
############################################################################
|
||||
#==============
|
||||
# SDM: LF
|
||||
#==============
|
||||
cols_to_select_sdm = c(static_cols_start, c("ddg_sdm_outcome", sdm_dn), static_cols_end)
|
||||
wf_sdm = comb_df_sl[, cols_to_select_sdm]
|
||||
|
||||
pivot_cols_sdm = cols_to_select_sdm[1: (length(static_cols_start) + 1)]; pivot_cols_sdm
|
||||
|
||||
expected_rows_lf = nrow(wf_sdm) * (length(wf_sdm) - length(pivot_cols_sdm))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: encomddg
|
||||
lf_sdm = gather(wf_sdm
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(sdm_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_sdm) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for", sdm_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for duet")
|
||||
quit()
|
||||
}
|
||||
|
||||
############################################################################
|
||||
#==============
|
||||
# mCSM: LF
|
||||
#==============
|
||||
cols_to_select_mcsm = c(static_cols_start, c("ddg_mcsm_outcome", mcsm_dn), static_cols_end)
|
||||
wf_mcsm = comb_df_sl[, cols_to_select_mcsm]
|
||||
|
||||
pivot_cols_mcsm = cols_to_select_mcsm[1: (length(static_cols_start) + 1)]; pivot_cols_mcsm
|
||||
|
||||
expected_rows_lf = nrow(wf_mcsm) * (length(wf_mcsm) - length(pivot_cols_mcsm))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: encomddg
|
||||
lf_mcsm = gather(wf_mcsm
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(mcsm_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_mcsm) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for", mcsm_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for duet")
|
||||
quit()
|
||||
}
|
||||
############################################################################
|
||||
# clear excess variables
|
||||
rm(all_plot_dfs
|
||||
, cols_dynamut2_df
|
||||
, cols_mcsm_df
|
||||
, cols_mcsm_na_df
|
||||
, comb_df
|
||||
, corr_data_ps
|
||||
, corr_ps_df3
|
||||
, df_lf_ps
|
||||
, foo
|
||||
, foo_cnames
|
||||
, gene_metadata
|
||||
, logo_data
|
||||
, logo_data_or_mult
|
||||
, logo_data_plot
|
||||
, logo_data_plot_logor
|
||||
, logo_data_plot_or
|
||||
, my_data_snp
|
||||
, my_df
|
||||
, my_df_u
|
||||
, other_muts
|
||||
, pd_df
|
||||
, subcols_df_ps
|
||||
, tab_mt
|
||||
, wide_df_logor
|
||||
, wide_df_logor_m
|
||||
, wide_df_or
|
||||
, wide_df_or_mult
|
||||
, wt)
|
||||
|
||||
|
||||
rm(c3, c4, check1
|
||||
, cols_check
|
||||
, cols_to_select
|
||||
, cols_to_select_deepddg
|
||||
, cols_to_select_duet
|
||||
, cols_to_select_dynamut
|
||||
, cols_to_select_dynamut2
|
||||
, cols_to_select_encomddg
|
||||
, cols_to_select_encomdds
|
||||
, cols_to_select_mcsm
|
||||
, cols_to_select_mcsm_na
|
||||
, cols_to_select_sdm)
|
||||
Loading…
Add table
Add a link
Reference in a new issue