#!/usr/bin/env Rscript  
#########################################################
# TASK: Script to format data for dm om plots: 
# generating WF and LF data for each of the parameters:
# duet, mcsm-lig, foldx, deepddg, dynamut2, mcsm-na, mcsm-ppi2, encom, dynamut..etc
# Called by get_plotting_dfs.R

##################################################################
# from plotting_globals.R
# DistCutOff, LigDist_colname, ppi2Dist_colname, naDist_colname 
#gene

dm_om_wf_lf_data <- function(df
                             , gene              # from globals
                             , colnames_to_extract
                             #, LigDist_colname # from globals used
                             #, ppi2Dist_colname #from globals used 
                             #, naDist_colname #from globals used
                             , snp_colname             = "mutationinformation"
                             , aa_pos_colname          = "position"
                             , mut_colname             = "mutation"
                             , mut_info_colname        = "dst_mode"
                             , mut_info_label_colname  = "mutation_info_labels"
                             , categ_cols_to_factor){
  
  df = as.data.frame(df)
  df$maf2 = log10(df$maf) # can't see otherwise
  sum(is.na(df$maf2))
  
  # Initialise the required dfs based on gene name
  #geneL_normal  = c("pnca")
  #geneL_na      = c("gid", "rpob")
  #geneL_ppi2    = c("alr", "embb", "katg", "rpob")
  
  #ADDED: IMPORTANT for rpob to be in both to make sure all data is returned
  geneL_normal  = c("pnca")
  geneL_both    = c("rpob")
  geneL_ppi2    = c("alr", "embb", "katg")
  geneL_na      = c("gid")
  
  # common_dfs
  common_dfsL     = list(
    wf_duet     = data.frame()
    , lf_duet     = data.frame()
    , wf_mcsm_lig = data.frame()
    , lf_mcsm_lig = data.frame()
    , wf_mmcsm_lig2 =  data.frame() # NEW
    , lf_mmcsm_lig2 =  data.frame() # NEW
    , wf_foldx    = data.frame()
    , lf_foldx    = data.frame()
    , wf_deepddg  = data.frame()
    , lf_deepddg  = data.frame()
    , wf_dynamut2 = data.frame()
    , lf_dynamut2 = data.frame()
    , wf_consurf  = data.frame()
    , lf_consurf  = data.frame()
    , wf_snap2    = data.frame()
    , lf_snap2    = data.frame()
    , wf_dist_gen = data.frame() # NEW
    , lf_dist_gen = data.frame() # NEW
  )
  
  # additional dfs
  if (tolower(gene)%in%geneL_normal){
    wf_lf_dataL   = common_dfsL
  }
  
  if (tolower(gene)%in%geneL_ppi2){
    additional_dfL   = list(
      wf_mcsm_ppi2   = data.frame()
      , lf_mcsm_ppi2 = data.frame()
    )
    wf_lf_dataL      = c(common_dfsL, additional_dfL)
  }
  
  if (tolower(gene)%in%geneL_na){
    additional_dfL = list(
      wf_mcsm_na   = data.frame()
      , lf_mcsm_na = data.frame()
    )
    wf_lf_dataL    = c(common_dfsL, additional_dfL)
  }
  
  if (tolower(gene)%in%geneL_both){
    additional_dfL = list(
      wf_mcsm_ppi2 = data.frame(),
      lf_mcsm_ppi2 = data.frame(),
      wf_mcsm_na   = data.frame(),
      lf_mcsm_na   = data.frame()
    )
    wf_lf_dataL      = c(common_dfsL, additional_dfL)
  }
  
  cat("\nInitializing an empty list of length:"
      , length(wf_lf_dataL))
  
  #=======================================================================
  # display names
  stability_suffix <- paste0(delta_symbol, delta_symbol, "G")
  
  duet_dn      = paste0("mCSM-DUET ", stability_suffix); duet_dn
  foldx_dn     = paste0("FoldX ", stability_suffix); foldx_dn
  deepddg_dn   = paste0("Deepddg " , stability_suffix); deepddg_dn
  dynamut2_dn  = paste0("Dynamut2 " , stability_suffix); dynamut2_dn
  
  consurf_dn   = "ConSurf"
  snap2_dn     = "SNAP2"
  provean_dn   = "PROVEAN"
  
  or_dn        = "Log10(OR)"
  pval_dn      = "-Log10(P)"
  maf2_dn       = "Log10(MAF)"
  
  asa_dn       = "ASA"
  rsa_dn       = "RSA"
  rd_dn        = "RD"
  kd_dn        = "KD"
  
  lig_dist_dn   = paste0("Lig Dist(", angstroms_symbol, ")"); lig_dist_dn
  mcsm_lig_dn   = paste0("mCSM-lig"); mcsm_lig_dn
  mmcsm_lig_dn2 = paste0("mmCSM-lig"); mmcsm_lig_dn2

  na_dist_dn   = paste0("Dist to NA (", angstroms_symbol, ")"); na_dist_dn
  mcsm_na_dn   = paste0("mCSM-NA ", stability_suffix); mcsm_na_dn
  
  ppi2_dist_dn = paste0("PPI Dist(", angstroms_symbol, ")"); ppi2_dist_dn
  mcsm_ppi2_dn = paste0("mCSM-PPI2 ", stability_suffix); mcsm_ppi2_dn
  
  #=======================================================================
  if(missing(categ_cols_to_factor)){
    categ_cols_to_factor = grep( "_outcome|_info", colnames(df) )
  }else{
    categ_cols_to_factor = categ_cols_to_factor 
  }
  #fact_cols = colnames(comb_df_s)[grepl( "_outcome|_info", colnames(comb_df_s) )]
  fact_cols = colnames(df)[categ_cols_to_factor]
  
  if (any(lapply(df[, fact_cols], class) == "character")){
    cat("\nChanging", length(categ_cols_to_factor), "cols to factor")
    df[, fact_cols] <- lapply(df[, fact_cols], as.factor)
    if (all(lapply(df[, fact_cols], class) == "factor")){
      cat("\nSuccessful: cols changed to factor")
    }
  }else{
    cat("\nRequested cols aready factors")
  }
  
  cat("\ncols changed to factor are:\n", colnames(df)[categ_cols_to_factor] )
  
  #=======================================================================
  if (missing(colnames_to_extract)){
    # NOTE: these vars are from globals
    #LigDist_colname, ppi2Dist_colname, naDist_colname
    
    common_colnames = c(snp_colname
                        , mut_colname            ,  "dst_mode"              , mut_info_label_colname
                        , aa_pos_colname
                        
                        , "duet_stability_change" , "duet_scaled"           , "duet_outcome"
                        , "ddg_foldx"             , "foldx_scaled"          , "foldx_outcome"
                        , "deepddg"               , "deepddg_scaled"        , "deepddg_outcome"
                        , "ddg_dynamut2"          , "ddg_dynamut2_scaled"   , "ddg_dynamut2_outcome"
                        
                        , "consurf_score"         , "consurf_scaled"        , "consurf_outcome"   , "consurf_colour_rev" 
                        , "snap2_score"           , "snap2_scaled"          , "snap2_outcome"
                        , "provean_score"         , "provean_scaled"        , "provean_outcome"
                        
                        , "log10_or_mychisq"      , "neglog_pval_fisher"    , "maf2"
                        , "asa"                   , "rsa"                   , "rd_values"          , "kd_values"
                        
                        , "mmcsm_lig"             , "mmcsm_lig_scaled"      , "mmcsm_lig_outcome"
                        , "ligand_affinity_change", "affinity_scaled"       , "ligand_outcome"     , LigDist_colname
    )
    
    display_common_colnames = c(snp_colname
                                , mut_colname
                                , "dst_mode"          , mut_info_label_colname
                                , aa_pos_colname
                                
                                , "duet_stability_change" , duet_dn            , "duet_outcome"
                                , "ddg_foldx"             , foldx_dn           , "foldx_outcome"
                                , "deepddg"               , deepddg_dn         , "deepddg_outcome"
                                , "ddg_dynamut2"          , dynamut2_dn        , "ddg_dynamut2_outcome"
                                , consurf_dn              , "consurf_scaled"   , "consurf_outcome" , "consurf_colour_rev" 
                                , snap2_dn                , "snap2_scaled"     , "snap2_outcome"
                                , provean_dn              , "provean_scaled"   , "provean_outcome"
                                
                                , or_dn                   , pval_dn            , maf2_dn 
                                , asa_dn                  , rsa_dn             , rd_dn              , kd_dn
                                
                                , "mmcsm_lig"             , mmcsm_lig_dn2      , "mmcsm_lig_outcome"
                                , "ligand_affinity_change", mcsm_lig_dn        , "ligand_outcome"   , lig_dist_dn
    )
    
    if (length(common_colnames) == length(display_common_colnames)){
      cat("\nLength match: Proceeding to extracting end cols")
    }else{
      stop("Abort: Length mismatch: b/w ncols to extract and disply name")
    }
    
    # ordering is important!
    # static_cols_end = c(lig_dist_dn
    #                     , "ASA"
    #                     , "RSA"
    #                     , "RD"
    #                     , "KD"
    #                     , "Log10(MAF)"
    #                     #, "Log10(OR)"
    #                     #, "-Log(P)"
    #                     )
    static_cols_end_common = c(lig_dist_dn, "Log10(MAF)"); static_cols_end_common
    
    if (tolower(gene)%in%geneL_normal){
      colnames_to_extract = c(common_colnames)
      display_colnames    = c(display_common_colnames)
      comb_df_sl          = df[, colnames_to_extract]
      
      # Rename cols: display names
      colnames(comb_df_sl) = display_colnames
      #colnames(comb_df)[colnames(comb_df)%in%colnames_to_extract] <- display_colnames
      
      static_cols_end =  static_cols_end_common
      cat("\nend colnames for gene:", static_cols_end)
    }
    
    if (tolower(gene)%in%geneL_ppi2){
      colnames_to_extract = c(common_colnames, "mcsm_ppi2_affinity"       ,"mcsm_ppi2_scaled" , "mcsm_ppi2_outcome"  , ppi2Dist_colname)
      display_colnames    = c(display_common_colnames,"mcsm_ppi2_affinity", mcsm_ppi2_dn      , "mcsm_ppi2_outcome" , ppi2_dist_dn )
      comb_df_sl          = df[, colnames_to_extract]
      
      # Rename cols: display names
      colnames(comb_df_sl)   = display_colnames
      # Affinity filtered data: mcsm-ppi2 --> ppi2Dist_colname
      comb_df_sl_ppi2 = comb_df_sl[comb_df_sl[[ppi2_dist_dn]]<DistCutOff,]
      
      # ordering is important!
      static_cols_end = c(ppi2_dist_dn, static_cols_end_common)
      cat("\nend colnames for gene:", static_cols_end)
    }
    
    if (tolower(gene)%in%geneL_na){
      colnames_to_extract = c(common_colnames         ,"mcsm_na_affinity"   , "mcsm_na_scaled"  , "mcsm_na_outcome"   , naDist_colname)
      display_colnames    = c(display_common_colnames , "mcsm_na_affinity" , mcsm_na_dn        , "mcsm_na_outcome"   , na_dist_dn)
      comb_df_sl          = df[, colnames_to_extract]
      
      # Rename cols: display names
      colnames(comb_df_sl)   = display_colnames
      # Affinity filtered data: mcsm-na --> naDist_colname
      comb_df_sl_na = comb_df_sl[comb_df_sl[[na_dist_dn]]<DistCutOff,]
      
      # ordering is important!
      static_cols_end = c(na_dist_dn, static_cols_end_common)
      cat("\nend colnames for gene:", static_cols_end)
      
    }
    
    if (tolower(gene)%in%geneL_both){
      colnames_to_extract = c(
        common_colnames, 
        "mcsm_ppi2_affinity" ,
        "mcsm_ppi2_scaled" , 
        "mcsm_ppi2_outcome"  , 
        ppi2Dist_colname,
        "mcsm_na_affinity"   , 
        "mcsm_na_scaled"  , 
        "mcsm_na_outcome"   , 
        naDist_colname
      )
      display_colnames    = c(
        display_common_colnames,
        "mcsm_ppi2_affinity", 
        mcsm_ppi2_dn, 
        "mcsm_ppi2_outcome",
        ppi2_dist_dn,
        "mcsm_na_affinity",
        mcsm_na_dn,
        "mcsm_na_outcome",
        na_dist_dn
      )
      comb_df_sl      = df[, colnames_to_extract]
      colnames(comb_df_sl)   = display_colnames
      comb_df_sl_ppi2 = comb_df_sl[comb_df_sl[[ppi2_dist_dn]]<DistCutOff,]
      comb_df_sl_na   = comb_df_sl[comb_df_sl[[na_dist_dn]]<DistCutOff,]
      static_cols_end = c(ppi2_dist_dn, na_dist_dn, static_cols_end_common)
      
    }
    
    
    # Affinity filtered data: mcsm-lig: COMMON for all genes, mcsm-lig --> LigDist_colname
    comb_df_sl_lig = comb_df_sl[comb_df_sl[[lig_dist_dn]]<DistCutOff,]
    
  }
  
  #======================
  # Selecting dfs
  # with appropriate cols
  #=======================
  static_cols_start =  c(snp_colname
                         , aa_pos_colname
                         , mut_colname
                         , mut_info_label_colname)
  
  # static_cols_end
  cat("\nEnd colnames for gene:", static_cols_end)
  
  #########################################################################
  #==============
  # Distance and genomics
  #==============
  # WF data: dist + genomics
  cols_to_select_dist_gen = c(static_cols_start, c("duet_outcome", duet_dn), static_cols_end)
  wf_dist_gen = comb_df_sl[, cols_to_select_dist_gen]; head(wf_dist_gen)
  
  #pivot_cols_ps = cols_to_select_ps[1:5]; pivot_cols_ps
  pivot_cols_dist_gen = cols_to_select_dist_gen[1: (length(static_cols_start) + 1)]; pivot_cols_dist_gen
  expected_rows_lf = nrow(wf_dist_gen) * (length(wf_dist_gen) - length(pivot_cols_dist_gen))
  expected_rows_lf
  
  # LF dist and genomics
  lf_dist_gen = tidyr::gather(wf_dist_gen
                              , key = param_type
                              , value = param_value
                              , all_of(duet_dn):tail(static_cols_end,1)
                              , factor_key = TRUE)
  
  if (nrow(lf_dist_gen) == expected_rows_lf){
    cat("\nPASS: long format data created for Distance and Genomics")
  }else{
    cat("\nFAIL: long format data could not be created for Distance and Genomics")
    quit()
  }
  
  # DROP duet cols
  drop_cols = c(duet_dn, "duet_outcome"); drop_cols
  table(lf_dist_gen$param_type)
  lf_dist_gen = lf_dist_gen[!lf_dist_gen$param_type%in%drop_cols,]
  lf_dist_gen$param_type = factor(lf_dist_gen$param_type)
  table(lf_dist_gen$param_type)
  
  # NEW columns [outcome and outcome colname]
  lf_dist_gen$outcome_colname = mut_info_colname
  lf_dist_gen$outcome         = lf_dist_gen[[mut_info_label_colname]]
  head(lf_dist_gen)
  
  wf_dist_gen = subset(wf_dist_gen, select = !(names(wf_dist_gen) %in% drop_cols))
  
  colnames(wf_dist_gen)
  colnames(lf_dist_gen)
  
  # Assign them to the output list
  wf_lf_dataL[['wf_dist_gen']] = wf_dist_gen
  wf_lf_dataL[['lf_dist_gen']] = lf_dist_gen
  ##########################################################
  
  #==============
  # DUET
  #==============
  # WF data: duet
  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 = tidyr::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()
  }
  
  table(lf_duet$param_type)
  
  # NEW columns [outcome and outcome colname]
  lf_duet$outcome_colname = "duet_outcome"
  lf_duet$outcome         = lf_duet$duet_outcome
  
  # DROP static cols
  lf_duet  = lf_duet[!lf_duet$param_type%in%c(static_cols_end),]
  lf_duet$param_type = factor(lf_duet$param_type)
  table(lf_duet$param_type); colnames(lf_duet)
  
  # Assign them to the output list
  wf_lf_dataL[['wf_duet']] = wf_duet
  wf_lf_dataL[['lf_duet']] = lf_duet
  
  ############################################################################
  #==============
  # FoldX
  #==============
  # WF data: Foldx
  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: Foldx
  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()
  }
  
  # NEW column
  lf_foldx$outcome_colname = "foldx_outcome"
  lf_foldx$outcome         = lf_foldx$foldx_outcome
  
  # DROP static cols
  lf_foldx  = lf_foldx[!lf_foldx$param_type%in%c(static_cols_end),]
  lf_foldx$param_type = factor(lf_foldx$param_type)
  table(lf_foldx$param_type); colnames(lf_foldx)
  
  # Assign them to the output list
  wf_lf_dataL[['wf_foldx']] = wf_foldx
  wf_lf_dataL[['lf_foldx']] = lf_foldx
  
  ############################################################################
  #==============
  # Deepddg
  #==============
  # WF data: deepddg
  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: Deepddg
  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()
  }
  
  # NEW columns [outcome and outcome colname]
  lf_deepddg$outcome_colname = "deepddg_outcome"
  lf_deepddg$outcome         = lf_deepddg$deepddg_outcome
  
  # DROP static cols
  lf_deepddg  = lf_deepddg[!lf_deepddg$param_type%in%c(static_cols_end),]
  lf_deepddg$param_type = factor(lf_deepddg$param_type)
  table(lf_deepddg$param_type); colnames(lf_deepddg)
  
  # Assign them to the output list
  wf_lf_dataL[['wf_deepddg']] = wf_deepddg
  wf_lf_dataL[['lf_deepddg']] = lf_deepddg
  ############################################################################
  #==============
  # Dynamut2: LF
  #==============
  # WF data: dynamut2
  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: dynamut2
  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()
  }
  
  # NEW columns [outcome and outcome colname]
  lf_dynamut2$outcome_colname = "ddg_dynamut2_outcome"
  lf_dynamut2$outcome         = lf_dynamut2$ddg_dynamut2_outcome
  
  # DROP static cols
  lf_dynamut2  = lf_dynamut2[!lf_dynamut2$param_type%in%c(static_cols_end),]
  lf_dynamut2$param_type = factor(lf_dynamut2$param_type)
  table(lf_dynamut2$param_type); colnames(lf_dynamut2)
  
  # Assign them to the output list
  wf_lf_dataL[['wf_dynamut2']] = wf_dynamut2
  wf_lf_dataL[['lf_dynamut2']] = lf_dynamut2
  
  ######################################################################################
  #==================
  # Consurf: LF
  #https://consurf.tau.ac.il/overview.php
  # consurf_score:
  # <0 (below average): slowly evolving i.e CONSERVED
  # >0 (above average): rapidly evolving, i.e VARIABLE 
  #table(df$consurf_colour_rev)
  # TODO
  #1--> "most_variable", 2--> "", 3-->"",  4-->""
  #5-->"", 6-->"", 7-->"", 8-->"", 9-->"most_conserved"
  #====================
  # WF data: consurf
  cols_to_select_consurf = c(static_cols_start, c("consurf_outcome", consurf_dn), static_cols_end)
  wf_consurf = comb_df_sl[, cols_to_select_consurf]
  
  pivot_cols_consurf = cols_to_select_consurf[1: (length(static_cols_start) + 1)]; pivot_cols_consurf
  expected_rows_lf = nrow(wf_consurf) * (length(wf_consurf) - length(pivot_cols_consurf))
  expected_rows_lf
  
  # when outcome didn't exist
  #cols_to_select_consurf = c(static_cols_start, c(consurf_dn), static_cols_end)
  #wf_consurf = comb_df_sl[, cols_to_select_consurf]
  # 
  # pivot_cols_consurf = cols_to_select_consurf[1: (length(static_cols_start))]; pivot_cols_consurf
  # expected_rows_lf = nrow(wf_consurf) * (length(wf_consurf) - length(pivot_cols_consurf))
  # expected_rows_lf
  
  # LF data: consurf
  lf_consurf = gather(wf_consurf
                      , key = param_type
                      , value = param_value
                      , all_of(consurf_dn):tail(static_cols_end,1)
                      , factor_key = TRUE)
  
  if (nrow(lf_consurf) == expected_rows_lf){
    cat("\nPASS: long format data created for ", consurf_dn)
  }else{
    cat("\nFAIL: long format data could not be created for duet")
    quit()
  }
  
  # NEW columns [outcome and outcome colname]
  lf_consurf$outcome_colname = "consurf_outcome"
  lf_consurf$outcome         = lf_consurf$consurf_outcome
  
  # DROP static cols
  lf_consurf  = lf_consurf[!lf_consurf$param_type%in%c(static_cols_end),]
  lf_consurf$param_type = factor(lf_consurf$param_type)
  table(lf_consurf$param_type); colnames(lf_consurf)
  
  # Assign them to the output list
  wf_lf_dataL[['wf_consurf']] = wf_consurf
  wf_lf_dataL[['lf_consurf']] = lf_consurf
  ###########################################################################
  #==============
  # SNAP2: LF
  #==============
  # WF data: snap2
  cols_to_select_snap2 = c(static_cols_start, c("snap2_outcome", snap2_dn), static_cols_end)
  wf_snap2 = comb_df_sl[, cols_to_select_snap2]
  
  pivot_cols_snap2 = cols_to_select_snap2[1: (length(static_cols_start) + 1)]; pivot_cols_snap2
  expected_rows_lf = nrow(wf_snap2) * (length(wf_snap2) - length(pivot_cols_snap2))
  expected_rows_lf
  
  # LF data: snap2
  lf_snap2 = gather(wf_snap2
                    , key = param_type
                    , value = param_value
                    , all_of(snap2_dn):tail(static_cols_end,1)
                    , factor_key = TRUE)
  
  if (nrow(lf_snap2) == expected_rows_lf){
    cat("\nPASS: long format data created for ", snap2_dn)
  }else{
    cat("\nFAIL: long format data could not be created for duet")
    quit()
  }
  
  # NEW columns [outcome and outcome colname]
  lf_snap2$outcome_colname = "snap2_outcome"
  lf_snap2$outcome         = lf_snap2$snap2_outcome
  
  # DROP static cols
  lf_snap2  = lf_snap2[!lf_snap2$param_type%in%c(static_cols_end),]
  lf_snap2$param_type = factor(lf_snap2$param_type)
  table(lf_snap2$param_type); colnames(lf_snap2)
  
  # Assign them to the output list
  wf_lf_dataL[['wf_snap2']] = wf_snap2
  wf_lf_dataL[['lf_snap2']] = lf_snap2
  
  #==============
  # Provean2: LF
  #==============
  # WF data: provean
  cols_to_select_provean = c(static_cols_start, c("provean_outcome", provean_dn), static_cols_end)
  wf_provean = comb_df_sl[, cols_to_select_provean]
  
  pivot_cols_provean = cols_to_select_provean[1: (length(static_cols_start) + 1)]; pivot_cols_provean
  expected_rows_lf = nrow(wf_provean) * (length(wf_provean) - length(pivot_cols_provean))
  expected_rows_lf
  
  # LF data: provean
  lf_provean = gather(wf_provean
                      , key = param_type
                      , value = param_value
                      , all_of(provean_dn):tail(static_cols_end,1)
                      , factor_key = TRUE)
  
  if (nrow(lf_provean) == expected_rows_lf){
    cat("\nPASS: long format data created for ", provean_dn)
  }else{
    cat("\nFAIL: long format data could not be created for duet")
    quit()
  }
  
  # NEW columns [outcome and outcome colname]
  lf_provean$outcome_colname = "provean_outcome"
  lf_provean$outcome         = lf_provean$provean_outcome
  
  # DROP static cols
  lf_provean  = lf_provean[!lf_provean$param_type%in%c(static_cols_end),]
  lf_provean$param_type = factor(lf_provean$param_type)
  table(lf_provean$param_type); colnames(lf_provean)
  
  # Assign them to the output list
  wf_lf_dataL[['wf_provean']] = wf_provean
  wf_lf_dataL[['lf_provean']] = lf_provean
  
  
  ###########################################################################
  # AFFINITY cols
  ###########################################################################
  #=========================
  # mCSM-lig:
  # data filtered by cut off
  #=========================
  #---------------------
  # mCSM-lig: WF and lF
  #----------------------
  # WF data: mcsm_lig
  cols_to_select_mcsm_lig = c(static_cols_start,  c("ligand_outcome", mcsm_lig_dn), static_cols_end)
  wf_mcsm_lig = comb_df_sl_lig[, cols_to_select_mcsm_lig] # filtered df
  
  pivot_cols_mcsm_lig = cols_to_select_mcsm_lig[1: (length(static_cols_start) + 1)]; pivot_cols_mcsm_lig
  expected_rows_lf = nrow(wf_mcsm_lig) * (length(wf_mcsm_lig) - length(pivot_cols_mcsm_lig))
  expected_rows_lf
  
  # LF data: mcsm_lig
  lf_mcsm_lig = gather(wf_mcsm_lig
                       , key = param_type
                       , value = param_value
                       , all_of(mcsm_lig_dn):tail(static_cols_end,1)
                       , factor_key = TRUE)
  
  if (nrow(lf_mcsm_lig) == expected_rows_lf){
    cat("\nPASS: long format data created for ", mcsm_lig_dn)
  }else{
    cat("\nFAIL: long format data could not be created for mcsm_lig")
    quit()
  }
  
  # NEW columns [outcome and outcome colname]
  lf_mcsm_lig$outcome_colname = "ligand_outcome"
  lf_mcsm_lig$outcome         = lf_mcsm_lig$ligand_outcome
  
  # DROP static cols
  lf_mcsm_lig  = lf_mcsm_lig[!lf_mcsm_lig$param_type%in%c(static_cols_end),]
  lf_mcsm_lig$param_type = factor(lf_mcsm_lig$param_type)
  table(lf_mcsm_lig$param_type); colnames(lf_mcsm_lig)
  
  # Assign them to the output list
  wf_lf_dataL[['wf_mcsm_lig']] = wf_mcsm_lig
  wf_lf_dataL[['lf_mcsm_lig']] = lf_mcsm_lig
  
  #=========================
  # mmCSM-lig2:
  # data filtered by cut off
  #=========================
  #---------------------
  # mmCSM-lig2: WF and lF
  #----------------------
  # WF data: mmcsm_lig2
  cols_to_select_mmcsm_lig2 = c(static_cols_start,  c("mmcsm_lig_outcome", mmcsm_lig_dn2), static_cols_end)
  wf_mmcsm_lig2 = comb_df_sl_lig[, cols_to_select_mmcsm_lig2] # filtered df
  
  pivot_cols_mmcsm_lig2 = cols_to_select_mmcsm_lig2[1: (length(static_cols_start) + 1)]; pivot_cols_mmcsm_lig2
  expected_rows_lf = nrow(wf_mmcsm_lig2) * (length(wf_mmcsm_lig2) - length(pivot_cols_mmcsm_lig2))
  expected_rows_lf
  
  # LF data: mmcsm_lig2
  lf_mmcsm_lig2 = gather(wf_mmcsm_lig2
                         , key = param_type
                         , value = param_value
                         , all_of(mmcsm_lig_dn2):tail(static_cols_end,1)
                         , factor_key = TRUE)
  
  if (nrow(lf_mmcsm_lig2) == expected_rows_lf){
    cat("\nPASS: long format data created for ", mmcsm_lig_dn2)
  }else{
    cat("\nFAIL: long format data could not be created for mmcsm_lig2")
    quit()
  }
  
  # NEW columns [outcome and outcome colname]
  lf_mmcsm_lig2$outcome_colname = "mmcsm_lig_outcome"
  lf_mmcsm_lig2$outcome         = lf_mmcsm_lig2$mmcsm_lig_outcome
  
  # DROP static cols
  lf_mmcsm_lig2  = lf_mmcsm_lig2[!lf_mmcsm_lig2$param_type%in%c(static_cols_end),]
  lf_mmcsm_lig2$param_type = factor(lf_mmcsm_lig2$param_type)
  table(lf_mmcsm_lig2$param_type); colnames(lf_mmcsm_lig2)
  
  # Assign them to the output list
  wf_lf_dataL[['wf_mmcsm_lig2']] = wf_mmcsm_lig2
  wf_lf_dataL[['lf_mmcsm_lig2']] = lf_mmcsm_lig2
  
  #=========================
  # mcsm-ppi2 affinity
  # data filtered by cut off
  #========================
  if (tolower(gene)%in%geneL_ppi2 || tolower(gene)%in%geneL_both){
    #-----------------
    # mCSM-PPI2: WF and lF
    #-----------------
    # WF data: mcsm-ppi2
    cols_to_select_mcsm_ppi2 = c(static_cols_start, c("mcsm_ppi2_outcome", mcsm_ppi2_dn), static_cols_end)
    #wf_mcsm_ppi2 = comb_df_sl[, cols_to_select_mcsm_ppi2]
    wf_mcsm_ppi2 = comb_df_sl_ppi2[, cols_to_select_mcsm_ppi2]
    
    pivot_cols_mcsm_ppi2 = cols_to_select_mcsm_ppi2[1: (length(static_cols_start) + 1)]; pivot_cols_mcsm_ppi2
    expected_rows_lf = nrow(wf_mcsm_ppi2) * (length(wf_mcsm_ppi2) - length(pivot_cols_mcsm_ppi2))
    expected_rows_lf
    
    # LF data: mcsm-ppi2
    lf_mcsm_ppi2 = gather(wf_mcsm_ppi2
                          , key = param_type
                          , value = param_value
                          , all_of(mcsm_ppi2_dn):tail(static_cols_end,1)
                          , factor_key = TRUE)
    
    if (nrow(lf_mcsm_ppi2) == expected_rows_lf){
      cat("\nPASS: long format data created for ", mcsm_ppi2_dn)
    }else{
      cat("\nFAIL: long format data could not be created for duet")
      quit()
    }
    
    # NEW columns [outcome and outcome colname]
    lf_mcsm_ppi2$outcome_colname = "mcsm_ppi2_outcome"
    lf_mcsm_ppi2$outcome         = lf_mcsm_ppi2$mcsm_ppi2_outcome
    
    # DROP static cols
    lf_mcsm_ppi2  = lf_mcsm_ppi2[!lf_mcsm_ppi2$param_type%in%c(static_cols_end),]
    lf_mcsm_ppi2$param_type = factor(lf_mcsm_ppi2$param_type)
    table(lf_mcsm_ppi2$param_type); colnames(lf_mcsm_ppi2)
    
    # Assign them to the output list
    wf_lf_dataL[['wf_mcsm_ppi2']] = wf_mcsm_ppi2
    wf_lf_dataL[['lf_mcsm_ppi2']] = lf_mcsm_ppi2
    
  }
  
  #====================
  # mcsm-NA affinity
  # data filtered by cut off
  #====================
  if (tolower(gene)%in%geneL_na|| tolower(gene)%in%geneL_both){
    #---------------
    # mCSM-NA: WF and lF
    #-----------------
    # WF data: mcsm-na
    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]
    wf_mcsm_na = comb_df_sl_na[, 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: mcsm-na
    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()
    }
    
    # NEW columns [outcome and outcome colname]
    lf_mcsm_na$outcome_colname = "mcsm_na_outcome"
    lf_mcsm_na$outcome         = lf_mcsm_na$mcsm_na_outcome
    
    # DROP static cols
    lf_mcsm_na  = lf_mcsm_na[!lf_mcsm_na$param_type%in%c(static_cols_end),]
    lf_mcsm_na$param_type = factor(lf_mcsm_na$param_type)
    table(lf_mcsm_na$param_type); colnames(lf_mcsm_na)
    
    # Assign them to the output list
    wf_lf_dataL[['wf_mcsm_na']] = wf_mcsm_na
    wf_lf_dataL[['lf_mcsm_na']] = lf_mcsm_na
    
  }
  
  return(wf_lf_dataL)
}
############################################################################