136 lines
4.6 KiB
R
136 lines
4.6 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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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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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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geneL_normal = c("pnca")
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geneL_na = c("gid", "rpob")
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geneL_ppi2 = c("alr", "embb", "katg", "rpob")
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# LigDist_colname # from globals used
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# ppi2Dist_colname #from globals used
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# naDist_colname #from globals used
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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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, "avg_stability")
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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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, "foldx_scaled_signC" # needed to get avg stability
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, "avg_stability_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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, "avg_stability_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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, "avg_lig_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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, "avg_lig_affinity_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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, "avg_lig_affinity_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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########################################
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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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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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