From 43399760021355c018e1dd23878d05365cfe4a33 Mon Sep 17 00:00:00 2001 From: Tanushree Tunstall Date: Fri, 10 Sep 2021 16:58:36 +0100 Subject: [PATCH] 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 --- scripts/combining_dfs.py | 240 +++++++++--- scripts/plotting/get_plotting_dfs.R | 452 ++++++---------------- scripts/plotting/lineage_data.R | 30 +- scripts/plotting/lineage_dist_plots.R | 71 +++- scripts/plotting/other_plots_data.R | 538 -------------------------- 5 files changed, 354 insertions(+), 977 deletions(-) delete mode 100755 scripts/plotting/other_plots_data.R diff --git a/scripts/combining_dfs.py b/scripts/combining_dfs.py index 634af18..4e2781e 100755 --- a/scripts/combining_dfs.py +++ b/scripts/combining_dfs.py @@ -41,6 +41,7 @@ import pandas as pd from pandas import DataFrame import numpy as np import argparse +from functools import reduce #======================================================================= #%% specify input and curr dir homedir = os.path.expanduser('~') @@ -92,19 +93,6 @@ outdir = args.output_dir gene_match = gene + '_p.' print('mut pattern for gene', gene, ':', gene_match) -# !"Redundant, now that improvements have been made! -# See section "REGEX" -# nssnp_match = gene_match +'[A-Za-z]{3}[0-9]+[A-Za-z]{3}' -# print('nsSNP for gene', gene, ':', nssnp_match) - -# wt_regex = gene_match.lower()+'([A-Za-z]{3})' -# print('wt regex:', wt_regex) - -# mut_regex = r'[0-9]+(\w{3})$' -# print('mt regex:', mut_regex) - -# pos_regex = r'([0-9]+)' -# print('position regex:', pos_regex) #%%======================================================================= #============== # directories @@ -122,49 +110,52 @@ if not outdir: # input #======= #in_filename_mcsm = gene.lower() + '_complex_mcsm_norm.csv' -in_filename_mcsm = gene.lower() + '_complex_mcsm_norm_SAM.csv' # gidb -in_filename_foldx = gene.lower() + '_foldx.csv' -in_filename_deepddg = gene.lower() + '_ni_deepddg.csv' # change to decent filename and put it in the correct dir - -in_filename_dssp = gene.lower() + '_dssp.csv' -in_filename_kd = gene.lower() + '_kd.csv' -in_filename_rd = gene.lower() + '_rd.csv' - +in_filename_mcsm = gene.lower() + '_complex_mcsm_norm_SAM.csv' # gidb +in_filename_foldx = gene.lower() + '_foldx.csv' +in_filename_deepddg = gene.lower() + '_ni_deepddg.csv' # change to decent filename and put it in the correct dir +in_filename_dssp = gene.lower() + '_dssp.csv' +in_filename_kd = gene.lower() + '_kd.csv' +in_filename_rd = gene.lower() + '_rd.csv' #in_filename_snpinfo = 'ns' + gene.lower() + '_snp_info_f.csv' # gwas f info -in_filename_afor = gene.lower() + '_af_or.csv' +in_filename_afor = gene.lower() + '_af_or.csv' #in_filename_afor_kin = gene.lower() + '_af_or_kinship.csv' +infilename_dynamut = gene.lower() + '_complex_dynamut_norm.csv' +infilename_dynamut2 = gene.lower() + '_complex_dynamut2_norm.csv' +infilename_mcsm_na = gene.lower() + '_complex_mcsm_na_norm.csv' +infilename_mcsm_f_snps = gene.lower() + '_mcsm_formatted_snps.csv' -infile_mcsm = outdir + in_filename_mcsm -infile_foldx = outdir + in_filename_foldx +infile_mcsm = outdir + in_filename_mcsm +infile_foldx = outdir + in_filename_foldx infile_deepddg = outdir + in_filename_deepddg +infile_dssp = outdir + in_filename_dssp +infile_kd = outdir + in_filename_kd +infile_rd = outdir + in_filename_rd +#infile_snpinfo = outdir + in_filename_snpinfo +infile_afor = outdir + in_filename_afor +#infile_afor_kin = outdir + in_filename_afor_kin +infile_dynamut = outdir + 'dynamut_results/' + infilename_dynamut +infile_dynamut2 = outdir + 'dynamut_results/dynamut2/' + infilename_dynamut2 +infile_mcsm_na = outdir + 'mcsm_na_results/' + infilename_mcsm_na +infile_mcsm_f_snps = outdir + infilename_mcsm_f_snps -infile_dssp = outdir + in_filename_dssp -infile_kd = outdir + in_filename_kd -infile_rd = outdir + in_filename_rd - -#infile_snpinfo = outdir + '/' + in_filename_snpinfo -infile_afor = outdir + '/' + in_filename_afor -#infile_afor_kin = outdir + '/' + in_filename_afor_kin - -print('\nInput path:', indir - , '\nOutput path:', outdir, '\n' - , '\nInput filename mcsm:', infile_mcsm - , '\nInput filename foldx:', infile_foldx, '\n' - , '\nInput filename deepddg', infile_deepddg , '\n' - , '\nInput filename dssp:', infile_dssp - , '\nInput filename kd:', infile_kd - , '\nInput filename rd', infile_rd - - #, '\nInput filename snp info:', infile_snpinfo, '\n' - , '\nInput filename af or:', infile_afor - #, '\nInput filename afor kinship:', infile_afor_kin - , '\n============================================================') +# read csv +mcsm_df = pd.read_csv(infile_mcsm, sep = ',') +foldx_df = pd.read_csv(infile_foldx , sep = ',') +deepddg_df = pd.read_csv(infile_deepddg, sep = ',') +dssp_df = pd.read_csv(infile_dssp, sep = ',') +kd_df = pd.read_csv(infile_kd, sep = ',') +rd_df = pd.read_csv(infile_rd, sep = ',') +afor_df = pd.read_csv(infile_afor, sep = ',') +dynamut_df = pd.read_csv(infile_dynamut, sep = ',') +dynamut2_df = pd.read_csv(infile_dynamut2, sep = ',') +mcsm_na_df = pd.read_csv(infile_mcsm_na, sep = ',') +mcsm_f_snps = pd.read_csv(infile_mcsm_f_snps, sep = ',', names = ['mutationinformation'], header = None) #======= # output #======= out_filename_comb = gene.lower() + '_all_params.csv' -outfile_comb = outdir + '/' + out_filename_comb +outfile_comb = outdir + out_filename_comb print('Output filename:', outfile_comb , '\n===================================================================') @@ -174,12 +165,101 @@ r_join = 'right' i_join = 'inner' # end of variable assignment for input and output files -#%%============================================================================ +#%%############################################################################ +#===================== +# some preprocessing +#===================== +#------------- +# FoldX +#------------- +foldx_df.shape +#======================= +# scale foldx values +#======================= + +# Rescale values in Foldx_change col b/w -1 and 1 so negative numbers +# stay neg and pos numbers stay positive +foldx_min = foldx_df['ddg'].min() +foldx_max = foldx_df['ddg'].max() +foldx_min +foldx_max + +foldx_scale = lambda x : x/abs(foldx_min) if x < 0 else (x/foldx_max if x >= 0 else 'failed') + +foldx_df['foldx_scaled'] = foldx_df['ddg'].apply(foldx_scale) +print('Raw foldx scores:\n', foldx_df['ddg'] + , '\n---------------------------------------------------------------' + , '\nScaled foldx scores:\n', foldx_df['foldx_scaled']) + +# additional check added +fsmi = foldx_df['foldx_scaled'].min() +fsma = foldx_df['foldx_scaled'].max() + +c = foldx_df[foldx_df['ddg']>=0].count() +foldx_pos = c.get(key = 'ddg') + +c2 = foldx_df[foldx_df['foldx_scaled']>=0].count() +foldx_pos2 = c2.get(key = 'foldx_scaled') + +if foldx_pos == foldx_pos2 and fsmi == -1 and fsma == 1: + print('\nPASS: Foldx values scaled correctly b/w -1 and 1') +else: + print('\nFAIL: Foldx values scaled numbers MISmatch' + , '\nExpected number:', foldx_pos + , '\nGot:', foldx_pos2 + , '\n======================================================') + +# rename ddg column to ddg_foldx +foldx_df['ddg'] +foldx_df = foldx_df.rename(columns = {'ddg':'ddg_foldx'}) +foldx_df['ddg_foldx'] + +#------------- +# Deepddg +#------------- +deepddg_df.shape + +#======================= +# scale Deepddg values +#======================= + +# Rescale values in deepddg_change col b/w -1 and 1 so negative numbers +# stay neg and pos numbers stay positive +deepddg_min = deepddg_df['deepddg'].min() +deepddg_max = deepddg_df['deepddg'].max() + +deepddg_scale = lambda x : x/abs(deepddg_min) if x < 0 else (x/deepddg_max if x >= 0 else 'failed') + +deepddg_df['deepddg_scaled'] = deepddg_df['deepddg'].apply(deepddg_scale) +print('Raw deepddg scores:\n', deepddg_df['deepddg'] + , '\n---------------------------------------------------------------' + , '\nScaled deepddg scores:\n', deepddg_df['deepddg_scaled']) + +# additional check added +dsmi = deepddg_df['deepddg_scaled'].min() +dsma = deepddg_df['deepddg_scaled'].max() + +c = deepddg_df[deepddg_df['deepddg']>=0].count() +deepddg_pos = c.get(key = 'deepddg') + +c2 = deepddg_df[deepddg_df['deepddg_scaled']>=0].count() +deepddg_pos2 = c2.get(key = 'deepddg_scaled') + +if deepddg_pos == deepddg_pos2 and dsmi == -1 and dsma == 1: + print('\nPASS: deepddg values scaled correctly b/w -1 and 1') +else: + print('\nFAIL: deepddg values scaled numbers MISmatch' + , '\nExpected number:', deepddg_pos + , '\nGot:', deepddg_pos2 + , '\n======================================================') +#%%============================================================================= +# Now merges begin +#%%============================================================================= print('===================================' , '\nFirst merge: mcsm + foldx' , '\n===================================') -mcsm_df = pd.read_csv(infile_mcsm, sep = ',') +mcsm_df.shape # add 3 lowercase aa code for wt and mutant get_aa_3lower(df = mcsm_df @@ -189,7 +269,7 @@ get_aa_3lower(df = mcsm_df , col_mut = 'mut_aa_3lower') #mcsm_df.columns = mcsm_df.columns.str.lower() -foldx_df = pd.read_csv(infile_foldx , sep = ',') +# foldx_df.shape #mcsm_foldx_dfs = combine_dfs_with_checks(mcsm_df, foldx_df, my_join = o_join) merging_cols_m1 = detect_common_cols(mcsm_df, foldx_df) @@ -205,8 +285,8 @@ print('===================================' , '\nSecond merge: mcsm_foldx_dfs + deepddg' , '\n===================================') -deepddg_df = pd.read_csv(infile_deepddg, sep = ',') -deepddg_df.columns +#deepddg_df = pd.read_csv(infile_deepddg, sep = ',') +#deepddg_df.columns # merge with mcsm_foldx_dfs and deepddg_df mcsm_foldx_deepddg_dfs = pd.merge(mcsm_foldx_dfs, deepddg_df, on = 'mutationinformation', how = l_join) @@ -218,9 +298,9 @@ print('===================================' , '\Third merge: dssp + kd' , '\n===================================') -dssp_df = pd.read_csv(infile_dssp, sep = ',') -kd_df = pd.read_csv(infile_kd, sep = ',') -rd_df = pd.read_csv(infile_rd, sep = ',') +dssp_df.shape +kd_df.shape +rd_df.shape #dssp_kd_dfs = combine_dfs_with_checks(dssp_df, kd_df, my_join = o_join) merging_cols_m2 = detect_common_cols(dssp_df, kd_df) @@ -308,8 +388,8 @@ print('\n=======================================' , '\ncombined_df_clean + afor_df ' , '\n=======================================') -afor_df = pd.read_csv(infile_afor, sep = ',') afor_cols = afor_df.columns +afor_df.shape # create a mapping from the gwas mutation column i.e _abcXXXrst #---------------------- @@ -360,16 +440,60 @@ else: sys.exit('\nFAIL: merge unsuccessful for af and or') #%%============================================================================ -# Output columns +# Output columns: when dynamut, dynamut2 and others weren't being combined out_filename_comb_afor = gene.lower() + '_comb_afor.csv' 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:' , '\nNo. of rows:', combined_stab_afor.shape[0] , '\nNo. of cols:', combined_stab_afor.shape[1]) -#%% end of script +#%%============================================================================ +# combine dynamut, dynamut2, and mcsm_na +dfs_list = [dynamut_df, dynamut2_df, mcsm_na_df] + +dfs_merged = reduce(lambda left,right: pd.merge(left + , right + , on = ['mutationinformation'] + , how = 'inner') + , dfs_list) +# drop excess columns +drop_cols = detect_common_cols(dfs_merged, combined_stab_afor) +drop_cols.remove('mutationinformation') + +dfs_merged_clean = dfs_merged.drop(drop_cols, axis = 1) +merging_cols_m6 = detect_common_cols(dfs_merged_clean, combined_stab_afor) + +len(dfs_merged_clean.columns) +len(combined_stab_afor.columns) + +combined_all_params = pd.merge(combined_stab_afor + , dfs_merged_clean + , on = merging_cols_m6 + , how = i_join) + +expected_ncols = len(dfs_merged_clean.columns) + len(combined_stab_afor.columns) - len(merging_cols_m6) +expected_nrows = len(combined_stab_afor) + +if len(combined_all_params.columns) == expected_ncols and len(combined_all_params) == expected_nrows: + print('\nPASS: All dfs combined') +else: + print('\nFAIL:lengths mismatch' + , '\nExpected ncols:', expected_ncols + , '\nGot:', len(dfs_merged_clean.columns) + , '\nExpected nrows:', expected_nrows + , '\nGot:', len(dfs_merged_clean) ) + +#%% Done for gid on 10/09/2021 +# write csv +print('Writing file: all params') +combined_all_params.to_csv(outfile_comb, index = False) + +print('\nFinished writing file:' + , '\nNo. of rows:', combined_all_params.shape[0] + , '\nNo. of cols:', combined_all_params.shape[1]) +#%% end of script \ No newline at end of file diff --git a/scripts/plotting/get_plotting_dfs.R b/scripts/plotting/get_plotting_dfs.R index 89b477c..f1a7620 100755 --- a/scripts/plotting/get_plotting_dfs.R +++ b/scripts/plotting/get_plotting_dfs.R @@ -8,11 +8,11 @@ setwd("~/git/LSHTM_analysis/scripts/plotting") getwd() source("Header_TT.R") -source("../functions/my_pairs_panel.R") # with lower panel turned off -source("../functions/plotting_globals.R") -source("../functions/plotting_data.R") -source("../functions/combining_dfs_plotting.R") -source("../functions/bp_subcolours.R") +# source("../functions/my_pairs_panel.R") # with lower panel turned off +# source("../functions/plotting_globals.R") +# source("../functions/plotting_data.R") +# source("../functions/combining_dfs_plotting.R") +# source("../functions/bp_subcolours.R") #******************** # cmd args passed @@ -41,8 +41,8 @@ import_dirs(drug, gene) #--------------------------- if (!exists("infile_params") && exists("gene")){ #if (!is.character(infile_params) && exists("gene")){ # when running as cmd - #in_filename_params = paste0(tolower(gene), "_all_params.csv") #for pncA - in_filename_params = paste0(tolower(gene), "_comb_afor.csv") # part combined for gid + in_filename_params = paste0(tolower(gene), "_all_params.csv") #for pncA (and for gid finally) 10/09/21 + #in_filename_params = paste0(tolower(gene), "_comb_afor.csv") # part combined for gid infile_params = paste0(outdir, "/", in_filename_params) cat("\nInput file for mcsm comb data not specified, assuming filename: ", infile_params, "\n") } @@ -91,369 +91,139 @@ merged_df3 = all_plot_dfs[[2]] merged_df2_comp = all_plot_dfs[[3]] merged_df3_comp = all_plot_dfs[[4]] #====================================================================== -# read other files -infilename_dynamut = paste0("~/git/Data/", drug, "/output/dynamut_results/", gene - , "_complex_dynamut_norm.csv") +#TODO: Think! MOVE TO COMBINE or singular file for deepddg -infilename_dynamut2 = paste0("~/git/Data/", drug, "/output/dynamut_results/dynamut2/", gene - , "_complex_dynamut2_norm.csv") +#============================ +# adding deepddg scaled values +# scale data b/w -1 and 1 +#============================ +n = which(colnames(merged_df3) == "deepddg"); n -infilename_mcsm_na = paste0("~/git/Data/", drug, "/output/mcsm_na_results/", gene - , "_complex_mcsm_na_norm.csv") - -infilename_mcsm_f_snps <- paste0("~/git/Data/", drug, "/output/", gene - , "_mcsm_formatted_snps.csv") - -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) + + diff --git a/scripts/plotting/lineage_data.R b/scripts/plotting/lineage_data.R index 29a6348..9549863 100755 --- a/scripts/plotting/lineage_data.R +++ b/scripts/plotting/lineage_data.R @@ -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$sel_lineages) + +################################################################ + diff --git a/scripts/plotting/lineage_dist_plots.R b/scripts/plotting/lineage_dist_plots.R index a425f37..cd1563d 100644 --- a/scripts/plotting/lineage_dist_plots.R +++ b/scripts/plotting/lineage_dist_plots.R @@ -16,9 +16,9 @@ source("Header_TT.R") # also loads all my functions #=========== # input #=========== -#drug = "streptomycin" -#gene = "gid" -source("get_plotting_dfs.R") +drug = "streptomycin" +gene = "gid" +#source("get_plotting_dfs.R") spec = matrix(c( "drug" , "d", 1, "character", @@ -47,7 +47,7 @@ plot_lineage_dist_dm_om_ps = paste0(plotdir,"/", lineage_dist_dm_om_ps) ########################### # Data for plots -# you need merged_df2 or merged_df2_comp +# you need merged_df2_combined or merged_df2_combined_comp # since this is one-many relationship # i.e the same SNP can belong to multiple lineages # using the _comp dataset means @@ -59,10 +59,12 @@ plot_lineage_dist_dm_om_ps = paste0(plotdir,"/", lineage_dist_dm_om_ps) # Data for plots #=================== # quick checks -table(merged_df2$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() diff --git a/scripts/plotting/other_plots_data.R b/scripts/plotting/other_plots_data.R deleted file mode 100755 index a55303b..0000000 --- a/scripts/plotting/other_plots_data.R +++ /dev/null @@ -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)