278 lines
8.6 KiB
R
278 lines
8.6 KiB
R
#!/usr/bin/env Rscript
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#source("~/git/LSHTM_analysis/config/alr.R")
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source("~/git/LSHTM_analysis/config/embb.R")
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#source("~/git/LSHTM_analysis/config/katg.R")
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#source("~/git/LSHTM_analysis/config/gid.R")
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#source("~/git/LSHTM_analysis/config/pnca.R")
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#source("~/git/LSHTM_analysis/config/rpob.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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#=======
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# output
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#=======
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outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
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###################################################################
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# FIXME: ADD distance to NA when SP replies
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# DONE: plotting_globals.R
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dist_columns = c("ligand_distance", "interface_dist")
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DistCutOff = 10
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common_cols = c("mutationinformation"
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, "X5uhc_position"
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, "X5uhc_offset"
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, "position"
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, "dst_mode"
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, "mutation_info_labels"
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, "sensitivity", dist_columns )
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#===================
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# stability cols
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#===================
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raw_cols_stability = c("duet_stability_change"
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, "deepddg"
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, "ddg_dynamut2"
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, "ddg_foldx")
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scaled_cols_stability = c("duet_scaled"
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, "deepddg_scaled"
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, "ddg_dynamut2_scaled"
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, "foldx_scaled")
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outcome_cols_stability = c("duet_outcome"
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, "deepddg_outcome"
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, "ddg_dynamut2_outcome"
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, "foldx_outcome")
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all_stability_cols = c(raw_cols_stability
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, scaled_cols_stability
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, outcome_cols_stability)
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#===================
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# affinity cols
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#===================
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raw_cols_affinity = c("ligand_affinity_change"
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, "mmcsm_lig"
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, "mcsm_ppi2_affinity"
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, "mcsm_na_affinity")
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scaled_cols_affinity = c("affinity_scaled"
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, "mmcsm_lig_scaled"
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, "mcsm_ppi2_scaled"
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, "mcsm_na_scaled" )
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outcome_cols_affinity = c( "ligand_outcome"
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, "mmcsm_lig_outcome"
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, "mcsm_ppi2_outcome"
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, "mcsm_na_outcome")
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all_affinity_cols = c(raw_cols_affinity
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, scaled_cols_affinity
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, outcome_cols_affinity)
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#===================
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# conservation cols
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#===================
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raw_cols_conservation = c("consurf_score"
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, "snap2_score"
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, "provean_score")
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scaled_cols_conservation = c("consurf_scaled"
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, "snap2_scaled"
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, "provean_scaled")
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outcome_cols_conservation = c("provean_outcome"
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, "snap2_outcome"
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, "consurf_colour_rev"
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, "consurf_outcome")
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all_conserv_cols = c(raw_cols_conservation
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, scaled_cols_conservation
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, outcome_cols_conservation)
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all_cols = c(common_cols
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, all_stability_cols
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, all_affinity_cols
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, all_conserv_cols)
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########################################
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categ_cols_to_factor = grep( "_outcome|_info", colnames(merged_df3) )
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fact_cols = colnames(merged_df3)[categ_cols_to_factor]
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if (any(lapply(merged_df3[, fact_cols], class) == "character")){
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cat("\nChanging", length(categ_cols_to_factor), "cols to factor")
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merged_df3[, fact_cols] <- lapply(merged_df3[, fact_cols], as.factor)
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if (all(lapply(merged_df3[, fact_cols], class) == "factor")){
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cat("\nSuccessful: cols changed to factor")
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}
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}else{
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cat("\nRequested cols aready factors")
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}
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cat("\ncols changed to factor are:\n", colnames(merged_df3)[categ_cols_to_factor] )
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####################################
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# merged_df3: NECESSARY pre-processing
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###################################
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#df3 = merged_df3
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plot_cols = c("mutationinformation", "mutation_info_labels", "position", "dst_mode"
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, all_cols)
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df3 = merged_df3[, colnames(merged_df3)%in%plot_cols]
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#=================
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# PREFORMATTING: for consistency
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#=================
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# DONE: combining_dfs.R
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# df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
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# table(df3$sensitivity)
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# ConSurf labels
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#consurf_colOld = "consurf_colour_rev"
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#consurf_colNew = "consurf_outcome"
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#df3[[consurf_colNew]] = df3[[consurf_colOld]]
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#df3[[consurf_colNew]] = as.factor(df3[[consurf_colNew]])
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#df3[[consurf_colNew]]
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# not this bit
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#!!!!!!!!!!!!!1
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#levels(df3$consurf_outcome) = c( "nsd", 1, 2, 3, 4, 5, 6, 7, 8, 9)
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#levels(df3$consurf_outcome)
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# SNAP2 labels
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#snap2_colname = "snap2_outcome"
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#df3[[snap2_colname]] <- str_replace(df3[[snap2_colname]], "effect", "Effect")
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#df3[[snap2_colname]] <- str_replace(df3[[snap2_colname]], "neutral", "Neutral")
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# for ref: not needed perse as function already does this and assigns labels for barplots
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# labels_duet = levels(as.factor(df3$duet_outcome))
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# labels_foldx = levels(as.factor(df3$foldx_outcome))
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# labels_deepddg = levels(as.factor(df3$deepddg_outcome))
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# labels_ddg_dynamut2_outcome = levels(as.factor(df3$ddg_dynamut2_outcome))
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#
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# labels_lig = levels(as.factor(df3_lig$ligand_outcome))
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# labels_mmlig = levels(as.factor(df3_lig$mmcsm_lig_outcome))
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# labels_ppi2 = levels(as.factor(df3_ppi2$mcsm_ppi2_outcome))
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#
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# labels_provean = levels(as.factor(df3$provean_outcome))
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# labels_snap2 = levels(as.factor(df3$snap2_outcome))
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# labels_consurf = levels(as.factor(df3$consurf_colour_rev))
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# df3$consurf_colour_rev = as.factor(df3$consurf_colour_rev )
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##############################################################################
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#######################################
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# merged_df2: NECESSARY pre-processing
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######################################
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df2 = merged_df2
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#=================
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# PREFORMATTING: for consistency
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#=================
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# DONE: combining_dfs.R
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# df2$sensitivity = ifelse(df2$dst_mode == 1, "R", "S")
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# table(df2$sensitivity)
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#----------------------------------------------------
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# Create dst2: fill na in dst with value of dst_mode
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# for epistasis
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#----------------------------------------------------
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# DONE: combining_dfs.R
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# df2$dst2 = ifelse(is.na(df2$dst), df2$dst_mode, df2f$dst)
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#----------------------------------------------------
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# reverse signs for foldx scaled values for
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# to allow average with other tools
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#----------------------------------------------------
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head(df2['ddg_foldx'])
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df2['ddg_foldxC'] = abs(df2$ddg_foldx)
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head(df2['ddg_foldxC'])
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head(df2['foldx_scaled'])
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df2['foldx_scaled_signC'] = abs(df2$foldx_scaled)
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head(df2['foldx_scaled_signC'])
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rm_foldx_cols = c("ddg_foldx","foldx_scaled")
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raw_cols_stab_revised = raw_cols_stability[!raw_cols_stability%in%rm_foldx_cols]
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raw_cols_stab_revised = c(raw_cols_stab_revised,"ddg_foldxC")
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scaled_cols_stab_revised = scaled_cols_stability[!scaled_cols_stability%in%rm_foldx_cols]
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scaled_cols_stab_revised = c(scaled_cols_stab_revised, "foldx_scaled_signC")
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######################################################
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# Affinity related variables
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# DONE:in plotting_globals.R
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# DistCutOff = 10
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# LigDist_colname # = "ligand_distance" # from globals
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# ppi2Dist_colname = "interface_dist"
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# naDist_colname = "TBC"
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######################################################
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# corr colnames
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# drug
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# "dst_mode"
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# "ligand_distance"
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# "DUET"
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# "mCSM-lig"
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# "FoldX"
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# "DeepDDG"
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# "ASA"
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# "RSA"
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# "KD"
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# "RD"
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# "Consurf"
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# "SNAP2"
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# "MAF"
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# "Log (OR)"
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# "-Log (P)"
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# "Dynamut2"
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# "mCSM-PPI2"
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# "interface_dist"
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corr_ps_colnames = c("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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# , "ASA"
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# , "RSA"
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# , "KD"
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# , "RD"
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# , "Consurf"
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# , "SNAP2"
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#, "mCSM-lig"
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#, "ligand_distance"
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#, "mCSM-PPI2"
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#, "interface_dist"
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, "dst_mode"
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, drug
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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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, "ligand_distance"
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, "dst_mode"
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, drug)
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corr_ppi2_colnames = c("mCSM-PPI2"
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, "SNAP2"
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, "Log (OR)"
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, "-Log (P)"
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, "interface_dist"
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, "dst_mode"
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, drug)
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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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