#!/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

# dm_om_wf_lf_data()
# INPUT: 
    # df: merged_df3 (data with all parameters)
      # NOTE*: merged_df2 will not be appropriate as it brings up most params as significant!?,atleast for gid
    # gene: [conditional generation of dfs like mcsm-NA, mcsm-ppi2 as not all genes have all these values]
    # colnames_to_extract     : columns to extract, either user-specified. 
      #By default it is c("mutationinformation" , "duet_affinity_change...")
    # ligand_dist_colname     : column name containing ligand distance. By deafult, it is LigDist_colname (imported from globals)
    # dr_muts                 : dr_muts_col (imported from globals; dr_mutations_<drug>)
    # other_muts              : other_muts_col (imported from globals ...other_mutations_<drug>)
    # snp_colname             : SNP column name. By default it is "mutationinformation"
    # aa_pos_colname          : Column name containing the aa position. This is used to sort the df by.
    # mut_colname             : Column name containing snp info in format "<abc_pXXdef>. By default, it is "mutation"
    # mut_info_colname        : Column name containing mutation info whether it is DM or OM. By default, it is "mutation_info"
    # mut_info_label_colname  : Column containing pre-formatted labels for mutation info. 
      # For my use case, this is called "mutation_info_labels"
      # This column has short labels like DM and OM coresponding to dr_muts and other_muts.
      # NOTE*: if this is left empty, then the arg ('dr_other_muts_labels') will be used
    # dr_other_muts_labels    : User specified labels, must correspond to dr_muts and other_muts. 
      # NOTE*: Only used if the arg (mut_info_label_colname) is empty!
    # categ_cols_to_factor    : Column names to convert to factors. These mainly correspond to the outcome columns associated with the
      # arg ('colnames_to_extract'). These have the suffix "_outcome" in their colnames. Additionally column 'mutation_info' is also 
      # converted to factor. By default, it converts the cols with '_outcome'and 'info' to factor.
      # Users are able to provide a vector of their corresponding column names

# RETURNS: List
    # WF nd LF data grouped by mutation_info i.e DM (drug mutations) and OM (other mutations)
    
# TO DO: SHINY
#1) df to choose (merged_df3 or merged_df2)
#2)
##################################################################
DistCutOff = 10
LigDist_colname  # = "ligand_distance" # from globals 
ppi2Dist_colname  = "interface_dist"
naDist_colname    = "TBC"

dm_om_wf_lf_data <- function(df
                          , gene_name               = gene # from globals
                          , colnames_to_extract
                          , ligand_dist_colname     = LigDist_colname # from globals
                          , dr_muts                 = dr_muts_col # from globals
                          , other_muts              = other_muts_col # from globals
                          , snp_colname             = "mutationinformation"
                          , aa_pos_colname          = "position" # to sort df by
                          , mut_colname             = "mutation"
                          , mut_info_colname        = "mutation_info"
                          , mut_info_label_colname  = "mutation_info_labels" # if empty, below used
                          , dr_other_muts_labels    = c("DM", "OM") # only used if ^^ = ""
                          , categ_cols_to_factor){
  
  df = as.data.frame(df)
 
  df['sensitivity'] = ifelse(df['dst_mode'] == 1, "R", "S")
  table(df['sensitivity'])
  
  df[[mut_info_label_colname]] = ifelse(df[[mut_info_label_colname]] == "DM", "R", "S")
  table(df[[mut_info_label_colname]])
  
  
  # Initialise the required dfs based on gene name
  geneL_normal  = c("pnca")
  #geneL_na_dy   = c("gid")
  geneL_na      = c("gid", "rpob")
  geneL_dy      = c("gid")
  geneL_ppi2    = c("alr", "embb", "katg", "rpob")
  
  # common_dfs
  common_dfsL     = list(
      wf_duet       = data.frame()
    , lf_duet     = data.frame()
    , wf_mcsm_lig = data.frame()
    , lf_mcsm_lig = data.frame()
    , 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()
  )
  
  # additional dfs
  if (tolower(gene_name)%in%geneL_normal){
    wf_lf_dataL   = common_dfsL
  }

 if (tolower(gene_name)%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_name)%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_name)%in%geneL_dy){
    additional_dfL  = list( 
      wf_mcsm_na    = data.frame()
      , lf_mcsm_na  = data.frame()
      , wf_dynamut  = data.frame()
      , lf_dynamut  = data.frame()
      , wf_encomddg = data.frame()
      , lf_encomddg = data.frame()
      , wf_encomdds = data.frame()
      , lf_encomdds = data.frame()
      , wf_sdm      = data.frame()
      , lf_sdm      = data.frame()
      , wf_mcsm     = data.frame()
      , lf_mcsm     = data.frame() 
    )
    wf_lf_dataL     = c(common_dfsL, additional_dfL)
  }
  cat("\nInitializing an empty list of length:"
      , length(wf_lf_dataL))
  
  #=======================================================================
  if (missing(colnames_to_extract)){
  
  colnames_to_extract = c(snp_colname
        , mut_colname, mut_info_colname, mut_info_label_colname
        , aa_pos_colname
        ,  LigDist_colname
        , ppi2Dist_colname, naDist_colname
        , "duet_stability_change" , "duet_scaled"        , "duet_outcome"
        , "ligand_affinity_change", "affinity_scaled"    , "ligand_outcome"
        , "ddg_foldx"             , "foldx_scaled"       , "foldx_outcome"
        , "deepddg"               , "deepddg_scaled"     , "deepddg_outcome"
        , "asa"                   , "rsa"
        , "rd_values"             , "kd_values"
        , "log10_or_mychisq"      , "neglog_pval_fisher" , "af"
        , "ddg_dynamut2"          , "ddg_dynamut2_scaled",  "ddg_dynamut2_outcome"
        , "mcsm_ppi2_affinity"    , "mcsm_ppi2_scaled"   , "mcsm_ppi2_outcome"
        , "consurf_score"         , "consurf_scaled"    #, "consurf_outcome"
        , "snap2_score"           , "snap2_scaled"       , "snap2_outcome"
        , "mcsm_na_affinity"      , "mcsm_na_scaled"     , "mcsm_na_outcome"
        , "ddg_dynamut"           , "ddg_dynamut_scaled" , "ddg_dynamut_outcome"
        , "ddg_encom"             , "ddg_encom_scaled"   ,  "ddg_encom_outcome"
        , "dds_encom"             , "dds_encom_scaled"   ,  "dds_encom_outcome"
        , "ddg_mcsm"              , "ddg_mcsm_scaled"    ,  "ddg_mcsm_outcome"    
        , "ddg_sdm"               , "ddg_sdm_scaled"     ,  "ddg_sdm_outcome"
        , "ddg_duet"              , "ddg_duet_scaled"    ,  "ddg_duet_outcome")
  }else{
    colnames_to_extract = c(mut_colname, mut_info_colname, mut_info_label_colname
                            , aa_pos_colname, LigDist_colname
                            , colnames_to_extract)
  }
  comb_df   = df[, colnames(df)%in%colnames_to_extract]
  comb_df_s = dplyr::arrange(comb_df, aa_pos_colname)
  
#=======================================================================
 if(missing(categ_cols_to_factor)){
  categ_cols_to_factor = grep( "_outcome|_info", colnames(comb_df_s) )
 }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(comb_df_s)[categ_cols_to_factor]

  if (any(lapply(comb_df_s[, fact_cols], class) == "character")){
    cat("\nChanging", length(categ_cols_to_factor), "cols to factor")
    comb_df_s[, fact_cols] <- lapply(comb_df_s[, fact_cols], as.factor)
    if (all(lapply(comb_df_s[, fact_cols], class) == "factor")){
      cat("\nSuccessful: cols changed to factor")
    }
  }else{
    cat("\nRequested cols aready factors")
  }
#=======================================================================
table(comb_df_s[[mut_info_colname]])

# further checks to make sure dr and other muts are indeed unique
dr_muts = comb_df_s[comb_df_s[[mut_info_colname]] == dr_muts,]
dr_muts_names = unique(dr_muts$mutation)

other_muts = comb_df_s[comb_df_s[[mut_info_colname]] == other_muts,]
other_muts_names = unique(other_muts$mutation)

if ( table(dr_muts_names%in%other_muts_names)[[1]] == length(dr_muts_names) &&
  table(other_muts_names%in%dr_muts_names)[[1]] == length(other_muts_names) ){
  cat("PASS: dr and other muts are indeed unique")
}else{
  cat("FAIL: dr and others muts are NOT unique!")
  quit()
}

# pretty display names i.e. labels to reduce major code duplication later
foo_cnames = data.frame(colnames(comb_df_s))
names(foo_cnames) <- "old_name"

stability_suffix <- paste0(delta_symbol, delta_symbol, "G")
flexibility_suffix <- paste0(delta_symbol, delta_symbol, "S")

lig_dn       = paste0("Ligand distance (", angstroms_symbol, ")"); lig_dn
mcsm_lig_dn  = paste0("Ligand affinity (log fold change)"); mcsm_lig_dn

duet_dn      = paste0("DUET ", stability_suffix); duet_dn
foldx_dn     = paste0("FoldX ", stability_suffix); foldx_dn
deepddg_dn   = paste0("Deepddg " , stability_suffix); deepddg_dn
dynamut2_dn  = paste0("Dynamut2 " , stability_suffix); dynamut2_dn

mcsm_na_dn   = paste0("mCSM-NA affinity ", stability_suffix); mcsm_na_dn
mcsm_ppi2_dn = paste0("mCSM-PPI2 affinity ", stability_suffix); mcsm_ppi2_dn
consurf_dn   = paste0("Consurf"); consurf_dn
snap2_dn     = paste0("SNAP2"); snap2_dn

dynamut_dn   = paste0("Dynamut ", stability_suffix); dynamut_dn
encom_ddg_dn = paste0("EnCOM " , stability_suffix); encom_ddg_dn
encom_dds_dn = paste0("EnCOM " , flexibility_suffix ); encom_dds_dn
sdm_dn       = paste0("SDM " , stability_suffix); sdm_dn
mcsm_dn      = paste0("mCSM " , stability_suffix ); mcsm_dn


# change column names: plyr
new_colnames = c(asa  = "ASA"
                , rsa                 = "RSA"
                , rd_values           = "RD"
                , kd_values           = "KD"
                , log10_or_mychisq    = "Log10 (OR)"
                , neglog_pval_fisher  = "-Log (P)"
                , af                  = "MAF"
                #, ligand_dist_colname     = lig_dn # cannot handle variable name 'ligand_dist_colname'
                , affinity_scaled     = mcsm_lig_dn
                , duet_scaled         = duet_dn
                , foldx_scaled        = foldx_dn
                , deepddg_scaled      = deepddg_dn
                , ddg_dynamut2_scaled = dynamut2_dn
                , mcsm_na_scaled      = mcsm_na_dn
                , mcsm_ppi2_affinity  = mcsm_ppi2_dn
                , consurf_score       = consurf_dn
                , snap2_score         = snap2_dn
                , ddg_dynamut_scaled  = dynamut_dn
                , ddg_encom_scaled    = encom_ddg_dn
                , dds_encom_scaled    = encom_dds_dn
                , ddg_sdm             = sdm_dn
                , ddg_mcsm            = mcsm_dn)

comb_df_sl1 = plyr::rename(comb_df_s
                          , replace = new_colnames
                          , warn_missing = T
                          , warn_duplicated = T)

# renaming colname using variable i.e ligand_dist_colname: dplyr
comb_df_sl = comb_df_sl1 %>% dplyr::rename(!!lig_dn := all_of(ligand_dist_colname))
names(comb_df_sl)
#####################################################################
if (mut_info_label_colname == "") {
  cat("\nAssigning labels:", dr_other_muts_labels, "--> to column:", mut_info_colname)
  table(comb_df_sl[[mut_info_colname]])

  # dr_muts
  levels(comb_df_sl[[mut_info_colname]])[levels(comb_df_sl[[mut_info_colname]])==dr_muts] <- dr_other_muts_labels[[1]]
  # other_muts
  levels(comb_df_sl[[mut_info_colname]])[levels(comb_df_sl[[mut_info_colname]])==other_muts] <- dr_other_muts_labels[[2]]
  table(comb_df_sl[[mut_info_colname]])
  
  static_cols1 = mut_info_colname
}else{
  table(comb_df_sl[[mut_info_label_colname]])
  static_cols1 = mut_info_label_colname
  
}
#######################################################################
#======================
# Selecting dfs
# with appropriate cols
#=======================

static_cols_start =  c(snp_colname
                       , aa_pos_colname
                       , mut_colname
                       , static_cols1)

# ordering is important!
static_cols_end = c(lig_dn
                    , "ASA"
                    , "RSA"
                    , "RD"
                    , "KD"
                    , "MAF"
                    , "Log10 (OR)"
                    , "-Log (P)")

#########################################################################
#==============
# 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 = 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()
}

# Assign them to the output list
wf_lf_dataL[['wf_duet']] = wf_duet
wf_lf_dataL[['lf_duet']] = lf_duet

############################################################################
#==============
# mCSM-lig
#==============
# 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[, cols_to_select_mcsm_lig]

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()
}

# Assign them to the output list
wf_lf_dataL[['wf_mcsm_lig']] = wf_mcsm_lig
wf_lf_dataL[['lf_mcsm_lig']] = lf_mcsm_lig
############################################################################
#==============
# 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()
}

# 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()
}

# 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()
}

# 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"
#====================
# FIXME: if you add category column to 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

# WF data: consurf
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()
}

# 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()
}

# Assign them to the output list
wf_lf_dataL[['wf_snap2']] = wf_snap2
wf_lf_dataL[['lf_snap2']] = lf_snap2

############################################################################
if (tolower(gene_name)%in%geneL_na){
  #==============
  # mCSM-NA: 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]
  
  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()
  }
  
  # Assign them to the output list
  wf_lf_dataL[['wf_mcsm_na']] = wf_mcsm_na
  wf_lf_dataL[['lf_mcsm_na']] = lf_mcsm_na

}
#-------------------------------------------------------------------
if (tolower(gene_name)%in%geneL_ppi2){
  #==============
  # mCSM-PPI2: 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]
  
  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()
  }
  
  # Assign them to the output list
  wf_lf_dataL[['wf_mcsm_ppi2']] = wf_mcsm_ppi2
  wf_lf_dataL[['lf_mcsm_ppi2']] = lf_mcsm_ppi2
  
}
#-------------------------------------------------------------------
if (tolower(gene_name)%in%geneL_dy){
  #==============
  # Dynamut: LF
  #==============
  # WF data: dynamut
  cols_to_select_dynamut  = c(static_cols_start, c("ddg_dynamut_outcome", dynamut_dn), static_cols_end)
  wf_dynamut = comb_df_sl[, cols_to_select_dynamut]
  
  pivot_cols_dynamut = cols_to_select_dynamut[1: (length(static_cols_start) + 1)]; pivot_cols_dynamut
  expected_rows_lf = nrow(wf_dynamut) * (length(wf_dynamut) - length(pivot_cols_dynamut))
  expected_rows_lf
  
  # LF data: dynamut
  lf_dynamut = gather(wf_dynamut
                      , key = param_type
                      , value = param_value
                      , all_of(dynamut_dn):tail(static_cols_end,1)
                      , factor_key = TRUE)
  
  if (nrow(lf_dynamut) == expected_rows_lf){
    cat("\nPASS: long format data created for ", dynamut_dn)
  }else{
    cat("\nFAIL: long format data could not be created for duet")
    quit()
  }
  
  # Assign them to the output list
  wf_lf_dataL[['wf_dynamut']] = wf_dynamut
  wf_lf_dataL[['lf_dynamut']] = lf_dynamut

#-------------------------------------------------------------------------
  #==============
  # EnCOM ddg: LF
  #==============
  # WF data: encomddg
  cols_to_select_encomddg = c(static_cols_start, c("ddg_encom_outcome", encom_ddg_dn), static_cols_end)
  wf_encomddg = comb_df_sl[, cols_to_select_encomddg]
  
  pivot_cols_encomddg = cols_to_select_encomddg[1: (length(static_cols_start) + 1)]; pivot_cols_encomddg 
  expected_rows_lf = nrow(wf_encomddg ) * (length(wf_encomddg ) - length(pivot_cols_encomddg))
  expected_rows_lf
  
  # LF data: encomddg 
  lf_encomddg  = gather(wf_encomddg 
                       , key = param_type
                       , value = param_value
                       , all_of(encom_ddg_dn):tail(static_cols_end,1)
                       , factor_key = TRUE)
  
  if (nrow(lf_encomddg) == expected_rows_lf){
    cat("\nPASS: long format data created for ", encom_ddg_dn)
  }else{
    cat("\nFAIL: long format data could not be created for duet")
    quit()
  }
  
  # Assign them to the output list
  wf_lf_dataL[['wf_encomddg']] = wf_encomddg
  wf_lf_dataL[['lf_encomddg']] = lf_encomddg
#-------------------------------------------------------------------------
  #==============
  # EnCOM dds: LF
  #==============
  # WF data: encomdds
  cols_to_select_encomdds = c(static_cols_start, c("dds_encom_outcome", encom_dds_dn), static_cols_end)
  wf_encomdds = comb_df_sl[, cols_to_select_encomdds]
  
  pivot_cols_encomdds = cols_to_select_encomdds[1: (length(static_cols_start) + 1)]; pivot_cols_encomdds 
  expected_rows_lf = nrow(wf_encomdds) * (length(wf_encomdds) - length(pivot_cols_encomdds))
  expected_rows_lf
  
  # LF data: encomdds 
  lf_encomdds  = gather(wf_encomdds
                        , key = param_type
                        , value = param_value
                        , all_of(encom_dds_dn):tail(static_cols_end,1)
                        , factor_key = TRUE)
  
  if (nrow(lf_encomdds) == expected_rows_lf){
    cat("\nPASS: long format data created for", encom_dds_dn)
  }else{
    cat("\nFAIL: long format data could not be created for duet")
    quit()
  }
  
  # Assign them to the output list
  wf_lf_dataL[['wf_encomdds']] = wf_encomdds
  wf_lf_dataL[['lf_encomdds']] = lf_encomdds
#-------------------------------------------------------------------------
  #==============
  # SDM: LF
  #==============
  # WF data: sdm
  cols_to_select_sdm = c(static_cols_start, c("ddg_sdm_outcome", sdm_dn), static_cols_end)
  wf_sdm = comb_df_sl[, cols_to_select_sdm]
  
  pivot_cols_sdm = cols_to_select_sdm[1: (length(static_cols_start) + 1)]; pivot_cols_sdm
  expected_rows_lf = nrow(wf_sdm) * (length(wf_sdm) - length(pivot_cols_sdm))
  expected_rows_lf
  
  # LF data: sdm
  lf_sdm  = gather(wf_sdm
                   , key = param_type
                   , value = param_value
                   , all_of(sdm_dn):tail(static_cols_end,1)
                   , factor_key = TRUE)
  
  if (nrow(lf_sdm) == expected_rows_lf){
    cat("\nPASS: long format data created for", sdm_dn)
  }else{
    cat("\nFAIL: long format data could not be created for duet")
    quit()
  }
  
  # Assign them to the output list
  wf_lf_dataL[['wf_sdm']] = wf_sdm
  wf_lf_dataL[['lf_sdm']] = lf_sdm
#-------------------------------------------------------------------------
  #==============
  # mCSM: LF
  #==============
  # WF data: mcsm
  cols_to_select_mcsm  = c(static_cols_start, c("ddg_mcsm_outcome", mcsm_dn), static_cols_end)
  wf_mcsm = comb_df_sl[, cols_to_select_mcsm]
  
  pivot_cols_mcsm = cols_to_select_mcsm[1: (length(static_cols_start) + 1)]; pivot_cols_mcsm
  expected_rows_lf = nrow(wf_mcsm) * (length(wf_mcsm) - length(pivot_cols_mcsm))
  expected_rows_lf
  
  # LF data: mcsm
  lf_mcsm  = gather(wf_mcsm
                   , key = param_type
                   , value = param_value
                   , all_of(mcsm_dn):tail(static_cols_end,1)
                   , factor_key = TRUE)
  
  if (nrow(lf_mcsm) == expected_rows_lf){
    cat("\nPASS: long format data created for", mcsm_dn)
  }else{
    cat("\nFAIL: long format data could not be created for duet")
    quit()
  }
  
  # Assign them to the output list
  wf_lf_dataL[['wf_mcsm']] = wf_mcsm
  wf_lf_dataL[['lf_mcsm']] = lf_mcsm

  }
#-------------------------------------------------------------------------
return(wf_lf_dataL)
}
############################################################################