rearranged corr plot cols and also added example for ggpairs
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fdb3f00503
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7 changed files with 227 additions and 681 deletions
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@ -15,6 +15,10 @@ corr_data_extract <- function(df
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, extract_scaled_cols = F){
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if ( missing(colnames_to_extract) || missing(colnames_display_key) ){
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df$maf2 = log10(df$maf) # can't see otherwise
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sum(is.na(df$maf2))
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cat("\n=========================================="
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, "\nCORR PLOTS data: ALL params"
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, "\n=========================================")
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@ -30,20 +34,22 @@ corr_data_extract <- function(df
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common_colnames = c(drug, "dst_mode"
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, "duet_stability_change" , "ddg_foldx" , "deepddg" , "ddg_dynamut2"
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, "asa" , "rsa" , "kd_values" , "rd_values"
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, "maf" , "log10_or_mychisq" , "neglog_pval_fisher"
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# previously maf
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, "maf2" , "log10_or_mychisq" , "neglog_pval_fisher"
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, LigDist_colname
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, "consurf_score" , "snap2_score" , "provean_score"
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, "ligand_affinity_change"
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, "ligand_affinity_change", "mmcsm_lig"
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#, "ddg_dynamut", "ddg_encom", "dds_encom", "ddg_mcsm", "ddg_sdm", "ddg_duet"
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)
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display_common_colnames = c( drug, "dst_mode"
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, "DUET" , "FoldX" , "DeepDDG", "Dynamut2"
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, "ASA" , "RSA" , "KD" , "RD"
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, "MAF" , "Log(OR)" , "-Log(P)"
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# previously MAF
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, "Log(MAF)" , "Log(OR)" , "-Log(P)"
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, "Lig-Dist"
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, "ConSurf" , "SNAP2" , "PROVEAN"
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, "mCSM-lig"
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, "mCSM-lig", "mmCSM-lig"
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# , "Dynamut" , "ENCoM-DDG" , "mCSM" , "SDM" , "DUET-d" , "ENCoM-DDS"
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)
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@ -8,6 +8,7 @@ source("~/git/LSHTM_analysis/config/embb.R")
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# get plottting dfs
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source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
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####################################################
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#=======
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# output
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@ -23,55 +24,126 @@ corr_plotdf = corr_data_extract(merged_df3
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, drug = drug
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, extract_scaled_cols = F)
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colnames(corr_plotdf)
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colnames(corr_df_m3_f)
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corr_plotdf = corr_df_m3_f #for downstream code
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if (all(colnames(corr_df_m3_f) == colnames(corr_plotdf))){
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cat("PASS: corr plot colnames match for dashboard")
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}else{
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stop("Abort: corr plot colnames DO NOT match for dashboard")
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}
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#corr_plotdf = corr_df_m3_f #for downstream code
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aff_dist_cols = colnames(corr_plotdf)[grep("Dist", colnames(corr_plotdf))]
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aff_dist_cols
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static_cols = c("Log(MAF)"
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, "Log(OR)"
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#, "-Log(P)"
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)
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#================
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# stability
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#================
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corr_ps_colnames = c("DUET"
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#affinity_dist_colnames# lIg DIst and ppi Di
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corr_ps_colnames = c(static_cols
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, "DUET"
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, "FoldX"
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, "DeepDDG"
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, "Dynamut2"
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, "MAF"
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, "Log(OR)"
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, "-Log(P)"
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#, "ligand_distance"
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, "dst_mode"
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, drug)
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, aff_dist_cols
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, "dst_mode")
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corr_ps_colnames%in%colnames(corr_plotdf)
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if (all(corr_ps_colnames%in%colnames(corr_plotdf))){
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cat("PASS: all colnames exist for correlation")
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}else{
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stop("Abort: all colnames DO NOT exist for correlation")
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}
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corr_df_ps = corr_plotdf[, corr_ps_colnames]
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complete_obs_ps = nrow(corr_df_ps) - sum(is.na(corr_df_ps$`Log(OR)`))
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cat("\nComplete muts for Conservation for", gene, ":", complete_obs_ps)
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color_coln = which(colnames(corr_df_ps) == "dst_mode")
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end = which(colnames(corr_df_ps) == drug)
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ncol_omit = 2
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corr_end = end-ncol_omit
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#end = which(colnames(corr_df_ps) == drug)
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#ncol_omit = 2
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#corr_end = end-ncol_omit
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corr_end = color_coln-1
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#------------------------
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# Output: stability corrP
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#------------------------
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corr_psP = paste0(outdir_images
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,tolower(gene)
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,"_corr_stability.svg" )
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,tolower(gene)
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,"_corr_stability.svg" )
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cat("Corr plot stability with coloured dots:", corr_psP)
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svg(corr_psP, width = 15, height = 15)
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my_corr_pairs(corr_data_all = corr_df_ps
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, corr_cols = colnames(corr_df_ps[1:corr_end])
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, corr_method = "spearman"
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, colour_categ_col = colnames(corr_df_ps[color_coln]) #"dst_mode"
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, categ_colour = c("red", "blue")
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, density_show = F
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, hist_col = "coral4"
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, dot_size = 1.6
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, ats = 1.5
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, corr_lab_size = 3
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, corr_value_size = 1)
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, corr_cols = colnames(corr_df_ps[1:corr_end])
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, corr_method = "spearman"
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, colour_categ_col = colnames(corr_df_ps[color_coln]) #"dst_mode"
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, categ_colour = c("red", "blue")
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, density_show = F
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, hist_col = "coral4"
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, dot_size = 1.6
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, ats = 1.5
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, corr_lab_size = 3
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, corr_value_size = 1)
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dev.off()
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#===============
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# CONSERVATION
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#==============
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corr_conservation_cols = c( static_cols
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, "ConSurf"
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, "SNAP2"
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, "PROVEAN"
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, aff_dist_cols
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, "dst_mode"
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, drug)
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if (all(corr_conservation_cols%in%colnames(corr_plotdf))){
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cat("PASS: all colnames exist for ConSurf-correlation")
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}else{
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stop("Abort: all colnames DO NOT exist for ConSurf-correlation")
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}
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corr_df_cons = corr_plotdf[, corr_conservation_cols]
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complete_obs_cons = nrow(corr_df_cons) - sum(is.na(corr_df_cons$`Log(OR)`))
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cat("\nComplete muts for Conservation for", gene, ":", complete_obs_cons)
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color_coln = which(colnames(corr_df_cons) == "dst_mode")
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# end = which(colnames(corr_df_cons) == drug)
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# ncol_omit = 2
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# corr_end = end-ncol_omit
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corr_end = color_coln-1
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#---------------------------
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# Output: Conservation corrP
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#----------------------------
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corr_consP = paste0(outdir_images
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,tolower(gene)
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,"_corr_conservation.svg" )
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cat("Corr plot conservation coloured dots:", corr_consP)
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svg(corr_consP, width = 10, height = 10)
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my_corr_pairs(corr_data_all = corr_df_cons
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, corr_cols = colnames(corr_df_cons[1:corr_end])
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, corr_method = "spearman"
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, colour_categ_col = colnames(corr_df_cons[color_coln]) #"dst_mode"
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, categ_colour = c("red", "blue")
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, density_show = F
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, hist_col = "coral4"
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, dot_size =1.1
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, ats = 1.5
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, corr_lab_size = 2.15
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, corr_value_size = 1)
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dev.off()
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#####################################################
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#DistCutOff = 10
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#LigDist_colname # = "ligand_distance" # from globals
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@ -82,31 +154,36 @@ dev.off()
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#================
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# ligand affinity
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#================
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corr_lig_colnames = c("mCSM-lig"
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, "MAF"
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, "Log(OR)"
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, "-Log(P)"
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, "Lig-Dist"
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, "dst_mode"
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, drug)
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corr_df_lig = corr_plotdf[corr_plotdf["Lig-Dist"]<DistCutOff,]
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corr_lig_colnames = c(static_cols
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, "mCSM-lig"
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, "mmCSM-lig"
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, "dst_mode")
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#, drug)
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if (all(corr_lig_colnames%in%colnames(corr_plotdf))){
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cat("PASS: all colnames exist for Lig-correlation")
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}else{
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stop("Abort: all colnames DO NOT exist for Lig-correlation")
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}
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corr_lig_colnames%in%colnames(corr_plotdf)
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corr_df_lig = corr_plotdf[, corr_lig_colnames]
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corr_df_lig = corr_df_lig[corr_df_lig["Lig-Dist"]<DistCutOff,]
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complete_obs_lig = nrow(corr_df_lig) - sum(is.na(corr_df_lig$`Log(OR)`))
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cat("\nComplete muts for lig affinity for", gene, ":", complete_obs_lig)
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color_coln = which(colnames(corr_df_lig) == "dst_mode")
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end = which(colnames(corr_df_lig) == drug)
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ncol_omit = 3 #omit dist col
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corr_end = end-ncol_omit
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# end = which(colnames(corr_df_lig) == drug)
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# ncol_omit = 2
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# corr_end = end-ncol_omit
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corr_end = color_coln-1
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#------------------------
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# Output: ligand corrP
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#------------------------
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corr_ligP = paste0(outdir_images
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,tolower(gene)
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,"_corr_lig.svg" )
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,tolower(gene)
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,"_corr_lig.svg" )
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cat("Corr plot affinity with coloured dots:", corr_ligP)
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svg(corr_ligP, width = 10, height = 10)
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@ -127,32 +204,38 @@ dev.off()
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#================
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# ppi2 affinity
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#================
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if (tolower(gene)%in%geneL_ppi2){
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corr_ppi2_colnames = c("mCSM-PPI2"
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, "MAF"
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, "Log(OR)"
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, "-Log(P)"
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, "PPI-Dist" # "interface_dist"
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corr_df_ppi2 = corr_plotdf[corr_plotdf["PPI-Dist"]<DistCutOff,]
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corr_ppi2_colnames = c(static_cols
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, "mCSM-PPI2"
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, "dst_mode"
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, drug)
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corr_ppi2_colnames%in%colnames(corr_plotdf)
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if (all(corr_ppi2_colnames%in%colnames(corr_plotdf))){
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cat("PASS: all colnames exist for mcsm-ppi2 correlation")
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}else{
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stop("Abort: all colnames DO NOT exist for mcsm-ppi2 correlation")
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}
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corr_df_ppi2 = corr_plotdf[, corr_ppi2_colnames]
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corr_df_ppi2 = corr_df_ppi2[corr_df_ppi2["PPI-Dist"]<DistCutOff,]
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complete_obs_ppi2 = nrow(corr_df_ppi2) - sum(is.na(corr_df_ppi2$`Log(OR)`))
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cat("\nComplete muts for ppi2 affinity for", gene, ":", complete_obs_ppi2)
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color_coln = which(colnames(corr_df_ppi2) == "dst_mode")
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end = which(colnames(corr_df_ppi2) == drug)
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ncol_omit = 3 #omit dist col
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corr_end = end-ncol_omit
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# end = which(colnames(corr_df_ppi2) == drug)
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# ncol_omit = 2
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# corr_end = end-ncol_omit
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corr_end = color_coln-1
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#------------------------
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# Output: ppi2 corrP
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#------------------------
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corr_ppi2P = paste0(outdir_images
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,tolower(gene)
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,"_corr_ppi2.svg" )
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,tolower(gene)
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,"_corr_ppi2.svg" )
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cat("Corr plot ppi2 with coloured dots:", corr_ppi2P)
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svg(corr_ppi2P, width = 10, height = 10)
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@ -180,25 +263,29 @@ if (tolower(gene)%in%geneL_ppi2){
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# NA affinity
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#================
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if (tolower(gene)%in%geneL_na){
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corr_na_colnames = c("mCSM-NA"
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, "MAF"
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, "Log(OR)"
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, "-Log(P)"
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, "NA-Dist" # "NA_dist"
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corr_df_na = corr_df_na[corr_df_na["NA-Dist"]<DistCutOff,]
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corr_na_colnames = c(static_cols
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, "mCSM-NA"
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, "dst_mode"
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, drug)
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if (all(corr_na_colnames%in%colnames(corr_plotdf))){
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cat("PASS: all colnames exist for mcsm-NA-correlation")
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}else{
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stop("Abort: all colnames DO NOT exist for mcsm-NA-correlation")
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}
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corr_na_colnames%in%colnames(corr_plotdf)
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corr_df_na = corr_plotdf[, corr_na_colnames]
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corr_df_na = corr_df_na[corr_df_na["NA-Dist"]<DistCutOff,]
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complete_obs_na = nrow(corr_df_na) - sum(is.na(corr_df_na$`Log(OR)`))
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cat("\nComplete muts for NA affinity for", gene, ":", complete_obs_na)
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color_coln = which(colnames(corr_df_na) == "dst_mode")
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end = which(colnames(corr_df_na) == drug)
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ncol_omit = 3 #omit dist col
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corr_end = end-ncol_omit
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# end = which(colnames(corr_df_na) == drug)
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# ncol_omit = 2
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# corr_end = end-ncol_omit
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corr_end = color_coln-1
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#------------------------
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# Output: mCSM-NA corrP
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@ -224,51 +311,21 @@ if (tolower(gene)%in%geneL_na){
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dev.off()
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}
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####################################################
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# CONSERVATION
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corr_conservation_cols = c("ConSurf"
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, "SNAP2"
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, "PROVEAN"
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, "MAF"
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, "Log(OR)"
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, "-Log(P)"
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, "dst_mode"
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, drug)
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#===============
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#ggpairs:
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#================
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#corr_df_ps$dst_mode = ifelse(corr_df_cons$dst_mode=="1", "R", "S")
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colnames(corr_plotdf)
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corr_conservation_cols%in%colnames(corr_plotdf)
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corr_df_cons = corr_plotdf[, corr_conservation_cols]
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complete_obs_cons = nrow(corr_df_cons) - sum(is.na(corr_df_cons$`Log(OR)`))
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cat("\nComplete muts for Conservation for", gene, ":", complete_obs_cons)
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svg('/tmp/foo.svg', width=10, height=10, )
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color_coln = which(colnames(corr_df_cons) == "dst_mode")
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end = which(colnames(corr_df_cons) == drug)
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ncol_omit = 2
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corr_end = end-ncol_omit
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ggpairs(corr_df_ps, columns = 1:(ncol(corr_df_ps)-corr_end)
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, upper = list(continuous = wrap('cor', method = "spearman"))
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, aes(colour = factor(dst_mode), alpha = 0.5)
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, title="correlogram with ggpairs()") +
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scale_colour_manual(values = c("red", "blue")) +
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scale_fill_manual(values = c("red", "blue"))
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#---------------------------
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# Output: Conservation corrP
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#----------------------------
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corr_consP = paste0(outdir_images
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,tolower(gene)
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,"_corr_conservation.svg" )
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cat("Corr plot conservation coloured dots:", corr_consP)
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svg(corr_consP, width = 10, height = 10)
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my_corr_pairs(corr_data_all = corr_df_cons
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, corr_cols = colnames(corr_df_cons[1:corr_end])
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, corr_method = "spearman"
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, colour_categ_col = colnames(corr_df_cons[color_coln]) #"dst_mode"
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, categ_colour = c("red", "blue")
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, density_show = F
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, hist_col = "coral4"
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, dot_size =1.1
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, ats = 1.5
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, corr_lab_size = 2.5
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, corr_value_size = 1)
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dev.off()
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@ -9,6 +9,7 @@ source("~/git/LSHTM_analysis/config/embb.R")
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# get plottting dfs
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source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
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#=======
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# output
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#=======
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@ -1,138 +0,0 @@
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foo = df3_affinity_filtered[df3_affinity_filtered$ligand_distance<10,]
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bar = df3_affinity_filtered[df3_affinity_filtered$interface_dist<10,]
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wilcox.test(foo$mmcsm_lig_scaled~foo$sensitivity)
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wilcox.test(foo$mmcsm_lig~foo$sensitivity)
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|
||||
wilcox.test(foo$affinity_scaled~foo$sensitivity)
|
||||
wilcox.test(foo$ligand_affinity_change~foo$sensitivity)
|
||||
|
||||
wilcox.test(bar$mcsm_na_scaled~bar$sensitivity)
|
||||
wilcox.test(bar$mcsm_na_affinity~bar$sensitivity)
|
||||
|
||||
wilcox.test(bar$mcsm_ppi2_scaled~bar$sensitivity)
|
||||
wilcox.test(bar$mcsm_ppi2_affinity~bar$sensitivity)
|
||||
|
||||
|
||||
# find the most "impactful" effect value
|
||||
biggest=max(abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')])))
|
||||
|
||||
abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')]))==biggest
|
||||
|
||||
abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')]))==c(,biggest)
|
||||
|
||||
max(abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')])))
|
||||
|
||||
|
||||
a2 = (a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')])
|
||||
a2
|
||||
#
|
||||
# biggest = max(abs(a2[1,]))
|
||||
#
|
||||
# #hmm
|
||||
# #which(abs(a2) == biggest)
|
||||
# #names(a2)[apply(a2, 1:4, function(i) which(i == max()))]
|
||||
#
|
||||
# # get row max
|
||||
# a2$row_maximum = apply(abs(a2[,-1]), 1, max)
|
||||
#
|
||||
# # get colname for abs(max_value)
|
||||
# #https://stackoverflow.com/questions/36960010/get-column-name-that-matches-specific-row-value-in-dataframe
|
||||
# #names(df)[which(df == 1, arr.ind=T)[, "col"]]
|
||||
# # yayy
|
||||
# names(a2)[which(abs(a2) == biggest, arr.ind=T)[, "col"]]
|
||||
#
|
||||
# #another:https://statisticsglobe.com/return-column-name-of-largest-value-for-each-row-in-r
|
||||
# colnames(a2)[max.col(abs(a2), ties.method = "first")] # Apply colnames & max.col functions
|
||||
# #################################################
|
||||
# # use whole df
|
||||
# #gene_aff_cols = c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')
|
||||
#
|
||||
# biggest = max(abs(a[gene_aff_cols]))
|
||||
# a$max_es = biggest
|
||||
# a$effect = names(a[gene_aff_cols])[which(abs(a[gene_aff_cols]) == biggest, arr.ind=T)[, "col"]]
|
||||
#
|
||||
# effect_name = unique(a$effect)
|
||||
# #get index of value of max effect
|
||||
# ind = (which(abs(a[effect_name]) == biggest, arr.ind=T))
|
||||
# a[effect_name][ind]
|
||||
# # extract sign
|
||||
# a$effect_sign = sign(a[effect_name][ind])
|
||||
########################################################
|
||||
# maxn <- function(n) function(x) order(x, decreasing = TRUE)[n]
|
||||
# second_big = abs(a[gene_aff_cols])[maxn(2)(abs(a[gene_aff_cols])]
|
||||
# apply(df, 1, function(x)x[maxn(1)(x)])
|
||||
# apply(a[gene_aff_cols], 1, function(x) abs(a[gene_aff_cols])[maxn(2)(abs(a[gene_aff_cols]))])
|
||||
#########################################################
|
||||
# loop
|
||||
a2 = df2[df2$position%in%c(167, 423, 427),]
|
||||
test <- a2 %>%
|
||||
dplyr::group_by(position) %>%
|
||||
biggest = max(abs(a2[gene_aff_cols]))
|
||||
a2$max_es = max(abs(a2[gene_aff_cols]))
|
||||
a2$effect = names(a2[gene_aff_cols])[which(abs(a2[gene_aff_cols]) == biggest, arr.ind=T)[, "col"]]
|
||||
effect_name = unique(a2$effect)
|
||||
|
||||
#get index of value of max effect
|
||||
ind = (which(abs(a2[effect_name]) == biggest, arr.ind=T))
|
||||
a2[effect_name][ind]
|
||||
# extract sign
|
||||
a2$effect_dir = sign(a2[effect_name][ind])
|
||||
#################################
|
||||
df2_short = df2[df2$position%in%c(167, 423, 427),]
|
||||
|
||||
for (i in unique(df2_short$position) ){
|
||||
#print(i)
|
||||
#print(paste0("\nNo. of unique positions:", length(unique(df2$position))) )
|
||||
#cat(length(unique(df2$position)))
|
||||
a2 = df2_short[df2_short$position==i,]
|
||||
biggest = max(abs(a2[gene_aff_cols]))
|
||||
a2$max_es = max(abs(a2[gene_aff_cols]))
|
||||
a2$effect = names(a2[gene_aff_cols])[which(abs(a2[gene_aff_cols]) == biggest, arr.ind=T)[, "col"]]
|
||||
effect_name = unique(a2$effect)
|
||||
|
||||
#get index of value of max effect
|
||||
ind = (which(abs(a2[effect_name]) == biggest, arr.ind=T))
|
||||
a2[effect_name][ind]
|
||||
# extract sign
|
||||
a2$effect_sign = sign(a2[effect_name][ind])
|
||||
}
|
||||
|
||||
#========================
|
||||
df2_short = df3[df3$position%in%c(167, 423, 427),]
|
||||
df2_short = df3[df3$position%in%c(170, 167, 493, 453, 435, 433, 480, 456, 445),]
|
||||
df2_short = df3[df3$position%in%c(435, 480),]
|
||||
df2_short = df3[df3$position%in%c(435, 480),]
|
||||
|
||||
give_col=function(x,y,df=df2_short){
|
||||
df[df$position==x,y]
|
||||
}
|
||||
|
||||
for (i in unique(df2_short$position) ){
|
||||
#print(i)
|
||||
#print(paste0("\nNo. of unique positions:", length(unique(df2$position))) )
|
||||
#cat(length(unique(df2$position)))
|
||||
#df2_short[df2_short$position==i,gene_aff_cols]
|
||||
|
||||
biggest = max(abs(give_col(i,gene_aff_cols)))
|
||||
|
||||
df2_short[df2_short$position==i,'abs_max_effect'] = biggest
|
||||
df2_short[df2_short$position==i,'effect_type']= names(
|
||||
give_col(i,gene_aff_cols)[which(
|
||||
abs(
|
||||
give_col(i,gene_aff_cols)
|
||||
) == biggest, arr.ind=T
|
||||
)[, "col"]])
|
||||
|
||||
effect_name = df2_short[df2_short$position==i,'effect_type'][1] # pick first one in case we have multiple exact values
|
||||
|
||||
# get index/rowname for value of max effect, and then use it to get the original sign
|
||||
# here
|
||||
#df2_short[df2_short$position==i,c(effect_name)]
|
||||
#which(abs(df2_short[df2_short$position==i,c('position',effect_name)][effect_name])==biggest, arr.ind=T)
|
||||
ind = rownames(which(abs(df2_short[df2_short$position==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
|
||||
df2_short[df2_short$position==i,'effect_sign'] = sign(df2_short[effect_name][ind,])
|
||||
}
|
||||
|
||||
df2_short$effect_type = sub("\\.[0-9]+", "", df2_short$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
|
||||
|
|
@ -1,241 +0,0 @@
|
|||
#source("~/git/LSHTM_analysis/config/pnca.R")
|
||||
#source("~/git/LSHTM_analysis/config/alr.R")
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
#source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||
#source("~/git/LSHTM_analysis/config/rpob.R")
|
||||
|
||||
source("/home/tanu/git/LSHTM_analysis/my_header.R")
|
||||
#########################################################
|
||||
# TASK: Generate averaged affinity values
|
||||
# across all affinity tools for a given structure
|
||||
# as applicable...
|
||||
#########################################################
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
|
||||
|
||||
#OutFile1
|
||||
outfile_mean_aff = paste0(outdir_images, "/", tolower(gene)
|
||||
, "_mean_affinity_all.csv")
|
||||
print(paste0("Output file:", outfile_mean_aff))
|
||||
|
||||
#OutFile2
|
||||
outfile_mean_aff_priorty = paste0(outdir_images, "/", tolower(gene)
|
||||
, "_mean_affinity_priority.csv")
|
||||
print(paste0("Output file:", outfile_mean_aff_priorty))
|
||||
|
||||
#%%===============================================================
|
||||
|
||||
#=============
|
||||
# Input
|
||||
#=============
|
||||
df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
|
||||
df3 = read.csv(df3_filename)
|
||||
length(df3$mutationinformation)
|
||||
|
||||
# mut_info checks
|
||||
table(df3$mutation_info)
|
||||
table(df3$mutation_info_orig)
|
||||
table(df3$mutation_info_labels_orig)
|
||||
|
||||
# used in plots and analyses
|
||||
table(df3$mutation_info_labels) # different, and matches dst_mode
|
||||
table(df3$dst_mode)
|
||||
|
||||
# create column based on dst mode with different colname
|
||||
table(is.na(df3$dst))
|
||||
table(is.na(df3$dst_mode))
|
||||
|
||||
#===============
|
||||
# Create column: sensitivity mapped to dst_mode
|
||||
#===============
|
||||
df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
|
||||
table(df3$sensitivity)
|
||||
|
||||
length(unique((df3$mutationinformation)))
|
||||
all_colnames = as.data.frame(colnames(df3))
|
||||
|
||||
# FIXME: ADD distance to NA when SP replies
|
||||
dist_columns = c("ligand_distance", "interface_dist")
|
||||
DistCutOff = 10
|
||||
common_cols = c("mutationinformation"
|
||||
, "X5uhc_position"
|
||||
, "X5uhc_offset"
|
||||
, "position"
|
||||
, "dst_mode"
|
||||
, "mutation_info_labels"
|
||||
, "sensitivity", dist_columns )
|
||||
|
||||
all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)]
|
||||
all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)]
|
||||
|
||||
#===================
|
||||
# stability cols
|
||||
#===================
|
||||
raw_cols_stability = c("duet_stability_change"
|
||||
, "deepddg"
|
||||
, "ddg_dynamut2"
|
||||
, "ddg_foldx")
|
||||
|
||||
scaled_cols_stability = c("duet_scaled"
|
||||
, "deepddg_scaled"
|
||||
, "ddg_dynamut2_scaled"
|
||||
, "foldx_scaled")
|
||||
|
||||
outcome_cols_stability = c("duet_outcome"
|
||||
, "deepddg_outcome"
|
||||
, "ddg_dynamut2_outcome"
|
||||
, "foldx_outcome")
|
||||
|
||||
#===================
|
||||
# affinity cols
|
||||
#===================
|
||||
raw_cols_affinity = c("ligand_affinity_change"
|
||||
, "mmcsm_lig"
|
||||
, "mcsm_ppi2_affinity"
|
||||
, "mcsm_na_affinity")
|
||||
|
||||
scaled_cols_affinity = c("affinity_scaled"
|
||||
, "mmcsm_lig_scaled"
|
||||
, "mcsm_ppi2_scaled"
|
||||
, "mcsm_na_scaled" )
|
||||
|
||||
outcome_cols_affinity = c( "ligand_outcome"
|
||||
, "mmcsm_lig_outcome"
|
||||
, "mcsm_ppi2_outcome"
|
||||
, "mcsm_na_outcome")
|
||||
|
||||
#===================
|
||||
# conservation cols
|
||||
#===================
|
||||
# raw_cols_conservation = c("consurf_score"
|
||||
# , "snap2_score"
|
||||
# , "provean_score")
|
||||
#
|
||||
# scaled_cols_conservation = c("consurf_scaled"
|
||||
# , "snap2_scaled"
|
||||
# , "provean_scaled")
|
||||
#
|
||||
# # CANNOT strictly be used, as categories are not identical with conssurf missing altogether
|
||||
# outcome_cols_conservation = c("provean_outcome"
|
||||
# , "snap2_outcome"
|
||||
# #consurf outcome doesn't exist
|
||||
# )
|
||||
|
||||
gene_aff_cols = colnames(df3)[colnames(df3)%in%scaled_cols_affinity]
|
||||
gene_stab_cols = colnames(df3)[colnames(df3)%in%scaled_cols_stability]
|
||||
gene_common_cols = colnames(df3)[colnames(df3)%in%common_cols]
|
||||
|
||||
sel_cols = c(gene_common_cols
|
||||
, gene_stab_cols
|
||||
, gene_aff_cols)
|
||||
|
||||
#########################################
|
||||
#df3_plot = df3[, cols_to_extract]
|
||||
df3_plot = df3[, sel_cols]
|
||||
|
||||
######################
|
||||
#FILTERING HMMMM!
|
||||
#all dist <10
|
||||
#for embb this results in 2 muts
|
||||
######################
|
||||
df3_affinity_filtered = df3_plot[ (df3_plot$ligand_distance<10 | df3_plot$interface_dist <10),]
|
||||
df3_affinity_filtered = df3_plot[ (df3_plot$ligand_distance<10 & df3_plot$interface_dist <10),]
|
||||
|
||||
c0u = unique(df3_affinity_filtered$position)
|
||||
length(c0u)
|
||||
|
||||
#df = df3_affinity_filtered
|
||||
##########################################
|
||||
#NO FILTERING: annotate the whole df
|
||||
df = df3_plot
|
||||
sum(is.na(df))
|
||||
df2 = na.omit(df)
|
||||
c0u = unique(df2$position)
|
||||
length(c0u)
|
||||
|
||||
# reassign orig
|
||||
my_df_raw = df3
|
||||
|
||||
# now subset
|
||||
df3 = df2
|
||||
#######################################################
|
||||
#=================
|
||||
# affinity effect
|
||||
#=================
|
||||
give_col=function(x,y,df=df3){
|
||||
df[df$position==x,y]
|
||||
}
|
||||
|
||||
for (i in unique(df3$position) ){
|
||||
#print(i)
|
||||
biggest = max(abs(give_col(i,gene_aff_cols)))
|
||||
|
||||
df3[df3$position==i,'abs_max_effect'] = biggest
|
||||
df3[df3$position==i,'effect_type']= names(
|
||||
give_col(i,gene_aff_cols)[which(
|
||||
abs(
|
||||
give_col(i,gene_aff_cols)
|
||||
) == biggest, arr.ind=T
|
||||
)[, "col"]])
|
||||
|
||||
# effect_name = unique(df3[df3$position==i,'effect_type'])
|
||||
effect_name = df3[df3$position==i,'effect_type'][1] # pick first one in case we have multiple exact values
|
||||
|
||||
ind = rownames(which(abs(df3[df3$position==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
|
||||
df3[df3$position==i,'effect_sign'] = sign(df3[effect_name][ind,])
|
||||
}
|
||||
|
||||
df3$effect_type = sub("\\.[0-9]+", "", df3$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
|
||||
df3U = df3[!duplicated(df3[c('position')]), ]
|
||||
table(df3U$effect_type)
|
||||
#########################################################
|
||||
#%% consider stability as well
|
||||
df4 = df2
|
||||
|
||||
#=================
|
||||
# stability + affinity effect
|
||||
#=================
|
||||
effect_cols = c(gene_aff_cols, gene_stab_cols)
|
||||
|
||||
give_col=function(x,y,df=df4){
|
||||
df[df$position==x,y]
|
||||
}
|
||||
|
||||
for (i in unique(df4$position) ){
|
||||
#print(i)
|
||||
biggest = max(abs(give_col(i,effect_cols)))
|
||||
|
||||
df4[df4$position==i,'abs_max_effect'] = biggest
|
||||
df4[df4$position==i,'effect_type']= names(
|
||||
give_col(i,effect_cols)[which(
|
||||
abs(
|
||||
give_col(i,effect_cols)
|
||||
) == biggest, arr.ind=T
|
||||
)[, "col"]])
|
||||
|
||||
# effect_name = unique(df4[df4$position==i,'effect_type'])
|
||||
effect_name = df4[df4$position==i,'effect_type'][1] # pick first one in case we have multiple exact values
|
||||
|
||||
ind = rownames(which(abs(df4[df4$position==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
|
||||
df4[df4$position==i,'effect_sign'] = sign(df4[effect_name][ind,])
|
||||
}
|
||||
|
||||
df4$effect_type = sub("\\.[0-9]+", "", df4$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
|
||||
df4U = df4[!duplicated(df4[c('position')]), ]
|
||||
table(df4U$effect_type)
|
||||
|
||||
#%%============================================================
|
||||
# output
|
||||
write.csv(combined_df, outfile_mean_ens_st_aff
|
||||
, row.names = F)
|
||||
cat("Finished writing file:\n"
|
||||
, outfile_mean_ens_st_aff
|
||||
, "\nNo. of rows:", nrow(combined_df)
|
||||
, "\nNo. of cols:", ncol(combined_df))
|
||||
|
||||
# end of script
|
||||
#===============================================================
|
||||
|
|
@ -1,14 +1,13 @@
|
|||
#source("~/git/LSHTM_analysis/config/pnca.R")
|
||||
#source("~/git/LSHTM_analysis/config/alr.R")
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
#source("~/git/LSHTM_analysis/config/embb.R")
|
||||
source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||
#source("~/git/LSHTM_analysis/config/rpob.R")
|
||||
|
||||
source("/home/tanu/git/LSHTM_analysis/my_header.R")
|
||||
#########################################################
|
||||
# TASK: Generate averaged stability values
|
||||
# across all stability tools
|
||||
# TASK: Generate averaged stability values by position
|
||||
# calculated across all stability tools
|
||||
# for a given structure
|
||||
#########################################################
|
||||
|
||||
|
|
@ -23,190 +22,53 @@ print(paste0("Output file:", outfile_mean_ens_st_aff))
|
|||
#%%===============================================================
|
||||
|
||||
#=============
|
||||
# Input
|
||||
# Input: merged_df3
|
||||
#=============
|
||||
df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
|
||||
df3 = read.csv(df3_filename)
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
#merged_df3= paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
|
||||
|
||||
cols_to_extract_ms = c("mutationinformation", "position", "avg_stability_scaled")
|
||||
|
||||
df3 = merged_df3[, cols_to_extract_ms]
|
||||
length(df3$mutationinformation)
|
||||
|
||||
# mut_info checks
|
||||
table(df3$mutation_info)
|
||||
table(df3$mutation_info_orig)
|
||||
table(df3$mutation_info_labels_orig)
|
||||
|
||||
# used in plots and analyses
|
||||
table(df3$mutation_info_labels) # different, and matches dst_mode
|
||||
table(df3$dst_mode)
|
||||
|
||||
# create column based on dst mode with different colname
|
||||
table(is.na(df3$dst))
|
||||
table(is.na(df3$dst_mode))
|
||||
|
||||
#===============
|
||||
# Create column: sensitivity mapped to dst_mode
|
||||
#===============
|
||||
df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
|
||||
table(df3$sensitivity)
|
||||
|
||||
length(unique((df3$mutationinformation)))
|
||||
all_colnames = as.data.frame(colnames(df3))
|
||||
common_cols = c("mutationinformation"
|
||||
, "position"
|
||||
, "dst_mode"
|
||||
, "mutation_info_labels"
|
||||
, "sensitivity"
|
||||
, "ligand_distance"
|
||||
, "interface_dist")
|
||||
|
||||
all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)]
|
||||
all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)]
|
||||
|
||||
#===================
|
||||
# stability cols
|
||||
#===================
|
||||
raw_cols_stability = c("duet_stability_change"
|
||||
, "deepddg"
|
||||
, "ddg_dynamut2"
|
||||
, "ddg_foldx")
|
||||
|
||||
scaled_cols_stability = c("duet_scaled"
|
||||
, "deepddg_scaled"
|
||||
, "ddg_dynamut2_scaled"
|
||||
, "foldx_scaled")
|
||||
|
||||
outcome_cols_stability = c("duet_outcome"
|
||||
, "deepddg_outcome"
|
||||
, "ddg_dynamut2_outcome"
|
||||
, "foldx_outcome")
|
||||
|
||||
#===================
|
||||
# affinity cols
|
||||
#===================
|
||||
raw_cols_affinity = c("ligand_affinity_change"
|
||||
, "mmcsm_lig"
|
||||
, "mcsm_ppi2_affinity"
|
||||
, "mcsm_na_affinity")
|
||||
|
||||
scaled_cols_affinity = c("affinity_scaled"
|
||||
, "mmcsm_lig_scaled"
|
||||
, "mcsm_ppi2_scaled"
|
||||
, "mcsm_na_scaled" )
|
||||
|
||||
outcome_cols_affinity = c( "ligand_outcome"
|
||||
, "mmcsm_lig_outcome"
|
||||
, "mcsm_ppi2_outcome"
|
||||
, "mcsm_na_outcome")
|
||||
|
||||
#===================
|
||||
# conservation cols
|
||||
#===================
|
||||
# raw_cols_conservation = c("consurf_score"
|
||||
# , "snap2_score"
|
||||
# , "provean_score")
|
||||
#
|
||||
# scaled_cols_conservation = c("consurf_scaled"
|
||||
# , "snap2_scaled"
|
||||
# , "provean_scaled")
|
||||
#
|
||||
# # CANNOT strictly be used, as categories are not identical with conssurf missing altogether
|
||||
# outcome_cols_conservation = c("provean_outcome"
|
||||
# , "snap2_outcome"
|
||||
# #consurf outcome doesn't exist
|
||||
# )
|
||||
|
||||
###########################################################
|
||||
cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols
|
||||
, raw_cols_stability
|
||||
, scaled_cols_stability
|
||||
, outcome_cols_stability
|
||||
, raw_cols_affinity
|
||||
, scaled_cols_affinity
|
||||
, outcome_cols_affinity)]
|
||||
|
||||
cols_to_extract = cols_to_consider[cols_to_consider%in%c(common_cols
|
||||
, outcome_cols_stability)]
|
||||
##############################################################
|
||||
#####################
|
||||
# Ensemble stability: outcome_cols_stability
|
||||
#####################
|
||||
# extract outcome cols and map numeric values to the categories
|
||||
# Destabilising == 0, and stabilising == 1, so rescaling can let -1 be destabilising
|
||||
df3_plot = df3[, cols_to_extract]
|
||||
|
||||
# assign numeric values to outcome
|
||||
df3_plot[, outcome_cols_stability] <- sapply(df3_plot[, outcome_cols_stability]
|
||||
, function(x){ifelse(x == "Destabilising", 0, 1)})
|
||||
table(df3$duet_outcome)
|
||||
table(df3_plot$duet_outcome)
|
||||
#=====================================
|
||||
# Stability (4 cols): average the scores
|
||||
# across predictors ==> average by
|
||||
# position ==> scale b/w -1 and 1
|
||||
|
||||
# column to average: ens_stability
|
||||
#=====================================
|
||||
cols_to_average = which(colnames(df3_plot)%in%outcome_cols_stability)
|
||||
|
||||
# ensemble average across predictors
|
||||
df3_plot$ens_stability = rowMeans(df3_plot[,cols_to_average])
|
||||
|
||||
head(df3_plot$position); head(df3_plot$mutationinformation)
|
||||
head(df3_plot$ens_stability)
|
||||
table(df3_plot$ens_stability)
|
||||
|
||||
# ensemble average of predictors by position
|
||||
mean_ens_stability_by_position <- df3_plot %>%
|
||||
avg_stability_by_position <- df3 %>%
|
||||
dplyr::group_by(position) %>%
|
||||
dplyr::summarize(avg_ens_stability = mean(ens_stability))
|
||||
dplyr::summarize(avg_stability_scaled_pos = mean(avg_stability_scaled))
|
||||
|
||||
# REscale b/w -1 and 1
|
||||
#en_stab_min = min(mean_ens_stability_by_position['avg_ens_stability'])
|
||||
#en_stab_max = max(mean_ens_stability_by_position['avg_ens_stability'])
|
||||
min(avg_stability_by_position$avg_stability_scaled_pos)
|
||||
max(avg_stability_by_position$avg_stability_scaled_pos)
|
||||
|
||||
# scale the average stability value between -1 and 1
|
||||
# mean_ens_by_position['averaged_stability3_scaled'] = lapply(mean_ens_by_position['averaged_stability3']
|
||||
# , function(x) ifelse(x < 0, x/abs(en3_min), x/en3_max))
|
||||
|
||||
mean_ens_stability_by_position['avg_ens_stability_scaled'] = lapply(mean_ens_stability_by_position['avg_ens_stability']
|
||||
avg_stability_by_position['avg_stability_scaled_pos_scaled'] = lapply(avg_stability_by_position['avg_stability_scaled_pos']
|
||||
, function(x) {
|
||||
scales::rescale(x, to = c(-1,1)
|
||||
scales::rescale_mid(x, to = c(-1,1)
|
||||
#, from = c(en_stab_min,en_stab_max))
|
||||
, mid = 0
|
||||
, from = c(0,1))
|
||||
})
|
||||
cat(paste0('Average stability scores:\n'
|
||||
, head(mean_ens_stability_by_position['avg_ens_stability'])
|
||||
, head(avg_stability_by_position['avg_stability_scaled_pos'])
|
||||
, '\n---------------------------------------------------------------'
|
||||
, '\nAverage stability scaled scores:\n'
|
||||
, head(mean_ens_stability_by_position['avg_ens_stability_scaled'])))
|
||||
, head(avg_stability_by_position['avg_stability_scaled_pos_scaled'])
|
||||
))
|
||||
|
||||
all(avg_stability_by_position['avg_stability_scaled_pos'] == avg_stability_by_position['avg_stability_scaled_pos_scaled'])
|
||||
|
||||
# convert to a data frame
|
||||
mean_ens_stability_by_position = as.data.frame(mean_ens_stability_by_position)
|
||||
avg_stability_by_position = as.data.frame(avg_stability_by_position)
|
||||
|
||||
#FIXME: sanity checks
|
||||
# TODO: predetermine the bounds
|
||||
# l_bound_ens = min(mean_ens_stability_by_position['avg_ens_stability_scaled'])
|
||||
# u_bound_ens = max(mean_ens_stability_by_position['avg_ens_stability_scaled'])
|
||||
#
|
||||
# if ( (l_bound_ens == -1) && (u_bound_ens == 1) ){
|
||||
# cat(paste0("PASS: ensemble stability scores averaged by position and then scaled"
|
||||
# , "\nmin ensemble averaged stability: ", l_bound_ens
|
||||
# , "\nmax ensemble averaged stability: ", u_bound_ens))
|
||||
# }else{
|
||||
# cat(paste0("FAIL: avergaed duet scores could not be scaled b/w -1 and 1"
|
||||
# , "\nmin ensemble averaged stability: ", l_bound_ens
|
||||
# , "\nmax ensemble averaged stability: ", u_bound_ens))
|
||||
# quit()
|
||||
# }
|
||||
##################################################################
|
||||
# output
|
||||
#write.csv(combined_df, outfile_mean_ens_st_aff
|
||||
write.csv(mean_ens_stability_by_position
|
||||
write.csv(avg_stability_by_position
|
||||
, outfile_mean_ens_st_aff
|
||||
, row.names = F)
|
||||
cat("Finished writing file:\n"
|
||||
, outfile_mean_ens_st_aff
|
||||
, "\nNo. of rows:", nrow(mean_ens_stability_by_position)
|
||||
, "\nNo. of cols:", ncol(mean_ens_stability_by_position))
|
||||
, "\nNo. of rows:", nrow(avg_stability_by_position)
|
||||
, "\nNo. of cols:", ncol(avg_stability_by_position))
|
||||
|
||||
# end of script
|
||||
#===============================================================
|
||||
|
|
|
|||
|
|
@ -1,5 +1,11 @@
|
|||
#!/usr/bin/env Rscript
|
||||
|
||||
|
||||
#source("~/git/LSHTM_analysis/config/alr.R")
|
||||
source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
#source("~/git/LSHTM_analysis/config/pnca.R")
|
||||
#source("~/git/LSHTM_analysis/config/rpob.R")
|
||||
#########################################################
|
||||
# TASK: Replace B-factors in the pdb file with the mean
|
||||
# normalised stability values.
|
||||
|
|
@ -22,25 +28,26 @@ cat(c(getwd(),"\n"))
|
|||
library(bio3d)
|
||||
require("getopt", quietly = TRUE) # cmd parse arguments
|
||||
#========================================================
|
||||
#drug = "pyrazinamide"
|
||||
#gene = "pncA"
|
||||
#drug = ""
|
||||
#gene = ""
|
||||
|
||||
# command line args
|
||||
spec = matrix(c(
|
||||
"drug" , "d", 1, "character",
|
||||
"gene" , "g", 1, "character"
|
||||
), byrow = TRUE, ncol = 4)
|
||||
|
||||
opt = getopt(spec)
|
||||
|
||||
drug = opt$drug
|
||||
gene = opt$gene
|
||||
|
||||
if(is.null(drug)|is.null(gene)) {
|
||||
stop("Missing arguments: --drug and --gene must both be specified (case-sensitive)")
|
||||
}
|
||||
# # command line args
|
||||
# spec = matrix(c(
|
||||
# "drug" , "d", 1, "character",
|
||||
# "gene" , "g", 1, "character"
|
||||
# ), byrow = TRUE, ncol = 4)
|
||||
#
|
||||
# opt = getopt(spec)
|
||||
#
|
||||
# drug = opt$drug
|
||||
# gene = opt$gene
|
||||
#
|
||||
# if(is.null(drug)|is.null(gene)) {
|
||||
# stop("Missing arguments: --drug and --gene must both be specified (case-sensitive)")
|
||||
# }
|
||||
#========================================================
|
||||
gene_match = paste0(gene,"_p.")
|
||||
cat(gene)
|
||||
gene_match = paste0(gene,"_p."); cat(gene_match)
|
||||
cat(gene_match)
|
||||
|
||||
#=============
|
||||
|
|
@ -64,7 +71,6 @@ cat(paste0("Input file:", infile_pdb) )
|
|||
|
||||
in_filename_mean_stability = paste0(tolower(gene), "_mean_ens_stability.csv")
|
||||
infile_mean_stability = paste0(outdir_plots, "/", in_filename_mean_stability)
|
||||
|
||||
cat(paste0("Input file:", infile_mean_stability) )
|
||||
|
||||
#=======
|
||||
|
|
@ -150,12 +156,12 @@ plot(density(df_duet$b)
|
|||
#=============
|
||||
|
||||
#hist(my_df$averaged_duet
|
||||
hist(my_df$avg_ens_stability_scaled
|
||||
hist(my_df$avg_stability_scaled_pos_scaled
|
||||
, xlab = ""
|
||||
, main = "mean stability values")
|
||||
|
||||
#plot(density(my_df$averaged_duet)
|
||||
plot(density(my_df$avg_ens_stability_scaled)
|
||||
plot(density(my_df$avg_stability_scaled_pos_scaled)
|
||||
, xlab = ""
|
||||
, main = "mean stability values")
|
||||
|
||||
|
|
@ -178,7 +184,7 @@ plot(density(my_df$avg_ens_stability_scaled)
|
|||
# this makes all the B-factor values in the non-matched positions as NA
|
||||
|
||||
#df_duet$b = my_df$averaged_duet_scaled[match(df_duet$resno, my_df$position)]
|
||||
df_duet$b = my_df$avg_ens_stability_scaled[match(df_duet$resno, my_df$position)]
|
||||
df_duet$b = my_df$avg_stability_scaled_pos_scaled[match(df_duet$resno, my_df$position)]
|
||||
|
||||
#=========
|
||||
# step 2_P1
|
||||
|
|
@ -194,8 +200,8 @@ na_rep = 2
|
|||
df_duet$b[is.na(df_duet$b)] = na_rep
|
||||
|
||||
# # sanity check: should be 0 and True
|
||||
# # duet and lig
|
||||
# if ( (sum(df_duet$b == na_rep) == b_na_duet) && (sum(df_lig$b == na_rep) == b_na_lig) ) {
|
||||
# # duet
|
||||
# if ( (sum(df_duet$b == na_rep) == b_na_duet) {
|
||||
# print ("PASS: NA's replaced with 0s successfully in df_duet and df_lig")
|
||||
# } else {
|
||||
# print("FAIL: NA replacement in df_duet NOT successful")
|
||||
|
|
@ -205,20 +211,13 @@ df_duet$b[is.na(df_duet$b)] = na_rep
|
|||
# max(df_duet$b); min(df_duet$b)
|
||||
#
|
||||
# # sanity checks: should be True
|
||||
# if( (max(df_duet$b) == max(my_df$avg_ens_stability_scaled)) & (min(df_duet$b) == min(my_df$avg_ens_stability_scaled)) ){
|
||||
# if( (max(df_duet$b) == max(my_df$avg_stability_scaled_pos_scaled)) & (min(df_duet$b) == min(my_df$avg_stability_scaled_pos_scaled)) ){
|
||||
# print("PASS: B-factors replaced correctly in df_duet")
|
||||
# } else {
|
||||
# print ("FAIL: To replace B-factors in df_duet")
|
||||
# quit()
|
||||
# }
|
||||
|
||||
# if( (max(df_lig$b) == max(my_df$avg_ens_affinity_scaled)) & (min(df_lig$b) == min(my_df$avg_ens_affinity_scaled)) ){
|
||||
# print("PASS: B-factors replaced correctly in df_lig")
|
||||
# } else {
|
||||
# print ("FAIL: To replace B-factors in df_lig")
|
||||
# quit()
|
||||
# }
|
||||
|
||||
#=========
|
||||
# step 3_P1
|
||||
#=========
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue