562 lines
20 KiB
R
562 lines
20 KiB
R
#!/usr/bin/env Rscript
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#########################################################
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# TASK: Script to format data for dm om plots:
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# generating WF and LF data for each of the parameters:
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# duet, mcsm-lig, foldx, deepddg, dynamut2, mcsm-na, mcsm-ppi2, encom, dynamut..etc
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# Called by get_plotting_dfs.R
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##################################################################
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# from plotting_globals.R
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# DistCutOff, LigDist_colname, ppi2Dist_colname, naDist_colname
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dm_om_wf_lf_data <- function(df
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, gene_name = gene # from globals
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, colnames_to_extract
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, ligand_dist_colname = LigDist_colname # from globals
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#, ppi2Dist_colname #from globals used
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#, naDist_colname #from globals used
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, dr_muts = dr_muts_col # from globals
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, other_muts = other_muts_col # from globals
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, snp_colname = "mutationinformation"
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, aa_pos_colname = "position" # to sort df by
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, mut_colname = "mutation"
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, mut_info_colname = "mutation_info"
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, mut_info_label_colname = "mutation_info_labels" # if empty, below used
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#, dr_other_muts_labels = c("DM", "OM") # only used if ^^ = ""
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, categ_cols_to_factor){
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df = as.data.frame(df)
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df$maf = log10(df$maf) # can't see otherwise
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# Initialise the required dfs based on gene name
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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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# common_dfs
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common_dfsL = list(
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wf_duet = data.frame()
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, lf_duet = data.frame()
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, wf_mcsm_lig = data.frame()
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, lf_mcsm_lig = data.frame()
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, wf_foldx = data.frame()
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, lf_foldx = data.frame()
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, wf_deepddg = data.frame()
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, lf_deepddg = data.frame()
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, wf_dynamut2 = data.frame()
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, lf_dynamut2 = data.frame()
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, wf_consurf = data.frame()
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, lf_consurf = data.frame()
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, wf_snap2 = data.frame()
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, lf_snap2 = data.frame()
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)
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# additional dfs
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if (tolower(gene_name)%in%geneL_normal){
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wf_lf_dataL = common_dfsL
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}
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if (tolower(gene_name)%in%geneL_na){
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additional_dfL = list(
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wf_mcsm_na = data.frame()
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, lf_mcsm_na = data.frame()
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)
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wf_lf_dataL = c(common_dfsL, additional_dfL)
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}
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if (tolower(gene_name)%in%geneL_ppi2){
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additional_dfL = list(
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wf_mcsm_ppi2 = data.frame()
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, lf_mcsm_ppi2 = data.frame()
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)
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wf_lf_dataL = c(common_dfsL, additional_dfL)
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}
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cat("\nInitializing an empty list of length:"
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, length(wf_lf_dataL))
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#=======================================================================
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if (missing(colnames_to_extract)){
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colnames_to_extract = c(snp_colname
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, mut_colname, mut_info_colname, mut_info_label_colname
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, aa_pos_colname
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, LigDist_colname # from globals
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, ppi2Dist_colname # from globals
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, naDist_colname # from globals
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, "duet_stability_change" , "duet_scaled" , "duet_outcome"
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, "ligand_affinity_change", "affinity_scaled" , "ligand_outcome"
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, "ddg_foldx" , "foldx_scaled" , "foldx_outcome"
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, "deepddg" , "deepddg_scaled" , "deepddg_outcome"
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, "asa" , "rsa"
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, "rd_values" , "kd_values"
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, "log10_or_mychisq" , "neglog_pval_fisher" , "maf" #"af"
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, "ddg_dynamut2" , "ddg_dynamut2_scaled", "ddg_dynamut2_outcome"
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, "mcsm_ppi2_affinity" , "mcsm_ppi2_scaled" , "mcsm_ppi2_outcome"
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, "consurf_score" , "consurf_scaled" , "consurf_outcome" # exists now
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, "consurf_colour_rev"
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, "snap2_score" , "snap2_scaled" , "snap2_outcome"
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, "mcsm_na_affinity" , "mcsm_na_scaled" , "mcsm_na_outcome"
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, "provean_score" , "provean_scaled" , "provean_outcome")
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}else{
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colnames_to_extract = c(mut_colname, mut_info_colname, mut_info_label_colname
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, aa_pos_colname, LigDist_colname
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, colnames_to_extract)
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}
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comb_df = df[, colnames(df)%in%colnames_to_extract]
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comb_df_s = dplyr::arrange(comb_df, aa_pos_colname)
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#=======================================================================
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if(missing(categ_cols_to_factor)){
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categ_cols_to_factor = grep( "_outcome|_info", colnames(comb_df_s) )
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}else{
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categ_cols_to_factor = categ_cols_to_factor
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}
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#fact_cols = colnames(comb_df_s)[grepl( "_outcome|_info", colnames(comb_df_s) )]
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fact_cols = colnames(comb_df_s)[categ_cols_to_factor]
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if (any(lapply(comb_df_s[, fact_cols], class) == "character")){
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cat("\nChanging", length(categ_cols_to_factor), "cols to factor")
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comb_df_s[, fact_cols] <- lapply(comb_df_s[, fact_cols], as.factor)
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if (all(lapply(comb_df_s[, 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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#=======================================================================
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table(comb_df_s[[mut_info_colname]])
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# pretty display names i.e. labels to reduce major code duplication later
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foo_cnames = data.frame(colnames(comb_df_s))
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names(foo_cnames) <- "old_name"
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stability_suffix <- paste0(delta_symbol, delta_symbol, "G")
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#flexibility_suffix <- paste0(delta_symbol, delta_symbol, "S")
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#lig_dn = paste0("Ligand distance (", angstroms_symbol, ")"); lig_dn
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#mcsm_lig_dn = paste0("Ligand affinity (log fold change)"); mcsm_lig_dn
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lig_dn = paste0("Lig Dist(", angstroms_symbol, ")"); lig_dn
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mcsm_lig_dn = paste0("mCSM-lig"); mcsm_lig_dn
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duet_dn = paste0("DUET ", stability_suffix); duet_dn
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foldx_dn = paste0("FoldX ", stability_suffix); foldx_dn
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deepddg_dn = paste0("Deepddg " , stability_suffix); deepddg_dn
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dynamut2_dn = paste0("Dynamut2 " , stability_suffix); dynamut2_dn
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mcsm_na_dn = paste0("mCSM-NA ", stability_suffix); mcsm_na_dn
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mcsm_ppi2_dn = paste0("mCSM-PPI2 ", stability_suffix); mcsm_ppi2_dn
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consurf_dn = paste0("ConSurf"); consurf_dn
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snap2_dn = paste0("SNAP2"); snap2_dn
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provean_dn = paste0("PROVEAN"); provean_dn
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# change column names: plyr
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new_colnames = c(asa = "ASA"
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, rsa = "RSA"
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, rd_values = "RD"
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, kd_values = "KD"
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#, log10_or_mychisq = "Log10(OR)"
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#, neglog_pval_fisher = "-Log(P)"
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#, af = "MAF"
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, maf = "Log10(MAF)"
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#, ligand_dist_colname= lig_dn # cannot handle variable name 'ligand_dist_colname'
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, affinity_scaled = mcsm_lig_dn
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, duet_scaled = duet_dn
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, foldx_scaled = foldx_dn
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, deepddg_scaled = deepddg_dn
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, ddg_dynamut2_scaled = dynamut2_dn
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, mcsm_na_scaled = mcsm_na_dn
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, mcsm_ppi2_scaled = mcsm_ppi2_dn
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#, consurf_scaled = consurf_dn
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, consurf_score = consurf_dn
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#, consurf_colour_rev = consurf_dn
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#, snap2_scaled = snap2_dn
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, snap2_score = snap2_dn
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, provean_score = provean_dn)
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comb_df_sl1 = plyr::rename(comb_df_s
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, replace = new_colnames
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, warn_missing = T
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, warn_duplicated = T)
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# renaming colname using variable i.e ligand_dist_colname: dplyr
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#comb_df_sl = comb_df_sl1 %>% dplyr::rename(!!lig_dn := all_of(ligand_dist_colname))
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comb_df_sl = comb_df_sl1 %>% dplyr::rename(!!lig_dn := all_of(LigDist_colname)) # NEW
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names(comb_df_sl)
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#=======================
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# NEW: Affinity filtered data
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#========================
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# mcsm-lig --> LigDist_colname
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comb_df_sl_lig = comb_df_sl[comb_df_sl[[lig_dn]]<DistCutOff,]
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# mcsm-ppi2 --> ppi2Dist_colname
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comb_df_sl_ppi2 = comb_df_sl[comb_df_sl[[ppi2Dist_colname]]<DistCutOff,]
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# mcsm-na --> naDist_colname
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comb_df_sl_na = comb_df_sl[comb_df_sl[[naDist_colname]]<DistCutOff,]
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#####################################################################
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static_cols1 = mut_info_label_colname
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#######################################################################
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#======================
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# Selecting dfs
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# with appropriate cols
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#=======================
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static_cols_start = c(snp_colname
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, aa_pos_colname
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, mut_colname
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, static_cols1)
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# ordering is important!
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static_cols_end = c(lig_dn
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, "ASA"
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, "RSA"
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, "RD"
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, "KD"
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, "Log10(MAF)"
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#, "Log10(OR)"
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#, "-Log(P)"
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)
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#########################################################################
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#==============
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# DUET
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#==============
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# WF data: duet
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cols_to_select_duet = c(static_cols_start, c("duet_outcome", duet_dn), static_cols_end)
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wf_duet = comb_df_sl[, cols_to_select_duet]
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#pivot_cols_ps = cols_to_select_ps[1:5]; pivot_cols_ps
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pivot_cols_duet = cols_to_select_duet[1: (length(static_cols_start) + 1)]; pivot_cols_duet
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expected_rows_lf = nrow(wf_duet) * (length(wf_duet) - length(pivot_cols_duet))
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expected_rows_lf
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# LF data: duet
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lf_duet = tidyr::gather(wf_duet
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, key = param_type
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, value = param_value
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, all_of(duet_dn):tail(static_cols_end,1)
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, factor_key = TRUE)
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if (nrow(lf_duet) == expected_rows_lf){
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cat("\nPASS: long format data created for ", duet_dn)
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}else{
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cat("\nFAIL: long format data could not be created for duet")
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quit()
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}
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# Assign them to the output list
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wf_lf_dataL[['wf_duet']] = wf_duet
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wf_lf_dataL[['lf_duet']] = lf_duet
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############################################################################
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#==============
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# FoldX
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#==============
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# WF data: Foldx
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cols_to_select_foldx= c(static_cols_start, c("foldx_outcome", foldx_dn), static_cols_end)
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wf_foldx = comb_df_sl[, cols_to_select_foldx]
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pivot_cols_foldx = cols_to_select_foldx[1: (length(static_cols_start) + 1)]; pivot_cols_foldx
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expected_rows_lf = nrow(wf_foldx) * (length(wf_foldx) - length(pivot_cols_foldx))
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expected_rows_lf
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# LF data: Foldx
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lf_foldx = gather(wf_foldx
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, key = param_type
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, value = param_value
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, all_of(foldx_dn):tail(static_cols_end,1)
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, factor_key = TRUE)
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if (nrow(lf_foldx) == expected_rows_lf){
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cat("\nPASS: long format data created for ", foldx_dn)
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}else{
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cat("\nFAIL: long format data could not be created for duet")
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quit()
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}
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# Assign them to the output list
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wf_lf_dataL[['wf_foldx']] = wf_foldx
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wf_lf_dataL[['lf_foldx']] = lf_foldx
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############################################################################
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#==============
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# Deepddg
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#==============
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# WF data: deepddg
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cols_to_select_deepddg = c(static_cols_start, c("deepddg_outcome", deepddg_dn), static_cols_end)
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wf_deepddg = comb_df_sl[, cols_to_select_deepddg]
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pivot_cols_deepddg = cols_to_select_deepddg[1: (length(static_cols_start) + 1)]; pivot_cols_deepddg
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expected_rows_lf = nrow(wf_deepddg) * (length(wf_deepddg) - length(pivot_cols_deepddg))
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expected_rows_lf
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# LF data: Deepddg
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lf_deepddg = gather(wf_deepddg
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, key = param_type
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, value = param_value
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, all_of(deepddg_dn):tail(static_cols_end,1)
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, factor_key = TRUE)
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if (nrow(lf_deepddg) == expected_rows_lf){
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cat("\nPASS: long format data created for ", deepddg_dn)
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}else{
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cat("\nFAIL: long format data could not be created for duet")
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quit()
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}
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# Assign them to the output list
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wf_lf_dataL[['wf_deepddg']] = wf_deepddg
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wf_lf_dataL[['lf_deepddg']] = lf_deepddg
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############################################################################
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#==============
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# Dynamut2: LF
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#==============
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# WF data: dynamut2
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cols_to_select_dynamut2 = c(static_cols_start, c("ddg_dynamut2_outcome", dynamut2_dn), static_cols_end)
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wf_dynamut2 = comb_df_sl[, cols_to_select_dynamut2]
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pivot_cols_dynamut2 = cols_to_select_dynamut2[1: (length(static_cols_start) + 1)]; pivot_cols_dynamut2
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expected_rows_lf = nrow(wf_dynamut2) * (length(wf_dynamut2) - length(pivot_cols_dynamut2))
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expected_rows_lf
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# LF data: dynamut2
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lf_dynamut2 = gather(wf_dynamut2
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, key = param_type
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, value = param_value
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, all_of(dynamut2_dn):tail(static_cols_end,1)
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, factor_key = TRUE)
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if (nrow(lf_dynamut2) == expected_rows_lf){
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cat("\nPASS: long format data created for ", dynamut2_dn)
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}else{
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cat("\nFAIL: long format data could not be created for duet")
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quit()
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}
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# Assign them to the output list
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wf_lf_dataL[['wf_dynamut2']] = wf_dynamut2
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wf_lf_dataL[['lf_dynamut2']] = lf_dynamut2
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######################################################################################
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#==================
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# Consurf: LF
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#https://consurf.tau.ac.il/overview.php
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# consurf_score:
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# <0 (below average): slowly evolving i.e CONSERVED
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# >0 (above average): rapidly evolving, i.e VARIABLE
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#table(df$consurf_colour_rev)
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# TODO
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#1--> "most_variable", 2--> "", 3-->"", 4-->""
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#5-->"", 6-->"", 7-->"", 8-->"", 9-->"most_conserved"
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#====================
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# WF data: consurf
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cols_to_select_consurf = c(static_cols_start, c("consurf_outcome", consurf_dn), static_cols_end)
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wf_consurf = comb_df_sl[, cols_to_select_consurf]
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pivot_cols_consurf = cols_to_select_consurf[1: (length(static_cols_start) + 1)]; pivot_cols_consurf
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expected_rows_lf = nrow(wf_consurf) * (length(wf_consurf) - length(pivot_cols_consurf))
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expected_rows_lf
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# when outcome didn't exist
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#cols_to_select_consurf = c(static_cols_start, c(consurf_dn), static_cols_end)
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#wf_consurf = comb_df_sl[, cols_to_select_consurf]
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#
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# pivot_cols_consurf = cols_to_select_consurf[1: (length(static_cols_start))]; pivot_cols_consurf
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# expected_rows_lf = nrow(wf_consurf) * (length(wf_consurf) - length(pivot_cols_consurf))
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# expected_rows_lf
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# LF data: consurf
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lf_consurf = gather(wf_consurf
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, key = param_type
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, value = param_value
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, all_of(consurf_dn):tail(static_cols_end,1)
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, factor_key = TRUE)
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if (nrow(lf_consurf) == expected_rows_lf){
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cat("\nPASS: long format data created for ", consurf_dn)
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}else{
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cat("\nFAIL: long format data could not be created for duet")
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quit()
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}
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# Assign them to the output list
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wf_lf_dataL[['wf_consurf']] = wf_consurf
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wf_lf_dataL[['lf_consurf']] = lf_consurf
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###########################################################################
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#==============
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# SNAP2: LF
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#==============
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# WF data: snap2
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cols_to_select_snap2 = c(static_cols_start, c("snap2_outcome", snap2_dn), static_cols_end)
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wf_snap2 = comb_df_sl[, cols_to_select_snap2]
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pivot_cols_snap2 = cols_to_select_snap2[1: (length(static_cols_start) + 1)]; pivot_cols_snap2
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expected_rows_lf = nrow(wf_snap2) * (length(wf_snap2) - length(pivot_cols_snap2))
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expected_rows_lf
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# LF data: snap2
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lf_snap2 = gather(wf_snap2
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, key = param_type
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, value = param_value
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, all_of(snap2_dn):tail(static_cols_end,1)
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, factor_key = TRUE)
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if (nrow(lf_snap2) == expected_rows_lf){
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cat("\nPASS: long format data created for ", snap2_dn)
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}else{
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cat("\nFAIL: long format data could not be created for duet")
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quit()
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}
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# Assign them to the output list
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wf_lf_dataL[['wf_snap2']] = wf_snap2
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wf_lf_dataL[['lf_snap2']] = lf_snap2
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#==============
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# Provean2: LF
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#==============
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# WF data: provean
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cols_to_select_provean = c(static_cols_start, c("provean_outcome", provean_dn), static_cols_end)
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wf_provean = comb_df_sl[, cols_to_select_provean]
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pivot_cols_provean = cols_to_select_provean[1: (length(static_cols_start) + 1)]; pivot_cols_provean
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expected_rows_lf = nrow(wf_provean) * (length(wf_provean) - length(pivot_cols_provean))
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expected_rows_lf
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# LF data: provean
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lf_provean = gather(wf_provean
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, key = param_type
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, value = param_value
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, all_of(provean_dn):tail(static_cols_end,1)
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, factor_key = TRUE)
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if (nrow(lf_provean) == expected_rows_lf){
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cat("\nPASS: long format data created for ", provean_dn)
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}else{
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cat("\nFAIL: long format data could not be created for duet")
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quit()
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}
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# Assign them to the output list
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wf_lf_dataL[['wf_provean']] = wf_provean
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wf_lf_dataL[['lf_provean']] = lf_provean
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###########################################################################
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# AFFINITY cols
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###########################################################################
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#=========================
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# mCSM-lig:
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# data filtered by cut off
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#=========================
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#---------------------
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# mCSM-lig: WF and lF
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#----------------------
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# WF data: mcsm_lig
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cols_to_select_mcsm_lig = c(static_cols_start, c("ligand_outcome", mcsm_lig_dn), static_cols_end)
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wf_mcsm_lig = comb_df_sl_lig[, cols_to_select_mcsm_lig] # filtered df
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pivot_cols_mcsm_lig = cols_to_select_mcsm_lig[1: (length(static_cols_start) + 1)]; pivot_cols_mcsm_lig
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expected_rows_lf = nrow(wf_mcsm_lig) * (length(wf_mcsm_lig) - length(pivot_cols_mcsm_lig))
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expected_rows_lf
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# LF data: mcsm_lig
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lf_mcsm_lig = gather(wf_mcsm_lig
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, key = param_type
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, value = param_value
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, all_of(mcsm_lig_dn):tail(static_cols_end,1)
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, factor_key = TRUE)
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if (nrow(lf_mcsm_lig) == expected_rows_lf){
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cat("\nPASS: long format data created for ", mcsm_lig_dn)
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}else{
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cat("\nFAIL: long format data could not be created for mcsm_lig")
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quit()
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}
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# Assign them to the output list
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wf_lf_dataL[['wf_mcsm_lig']] = wf_mcsm_lig
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wf_lf_dataL[['lf_mcsm_lig']] = lf_mcsm_lig
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#====================
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# mcsm-NA affinity
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# data filtered by cut off
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#====================
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if (tolower(gene_name)%in%geneL_na){
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#---------------
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# mCSM-NA: WF and lF
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#-----------------
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# WF data: mcsm-na
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cols_to_select_mcsm_na = c(static_cols_start, c("mcsm_na_outcome", mcsm_na_dn), static_cols_end)
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#wf_mcsm_na = comb_df_sl[, cols_to_select_mcsm_na]
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wf_mcsm_na = comb_df_sl_na[, cols_to_select_mcsm_na]
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pivot_cols_mcsm_na = cols_to_select_mcsm_na[1: (length(static_cols_start) + 1)]; pivot_cols_mcsm_na
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expected_rows_lf = nrow(wf_mcsm_na) * (length(wf_mcsm_na) - length(pivot_cols_mcsm_na))
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expected_rows_lf
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# LF data: mcsm-na
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lf_mcsm_na = gather(wf_mcsm_na
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, key = param_type
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, value = param_value
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, all_of(mcsm_na_dn):tail(static_cols_end,1)
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, factor_key = TRUE)
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if (nrow(lf_mcsm_na) == expected_rows_lf){
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cat("\nPASS: long format data created for ", mcsm_na_dn)
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}else{
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cat("\nFAIL: long format data could not be created for duet")
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quit()
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}
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# Assign them to the output list
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wf_lf_dataL[['wf_mcsm_na']] = wf_mcsm_na
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wf_lf_dataL[['lf_mcsm_na']] = lf_mcsm_na
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}
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#=========================
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# mcsm-ppi2 affinity
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# data filtered by cut off
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#========================
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if (tolower(gene_name)%in%geneL_ppi2){
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#-----------------
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# mCSM-PPI2: WF and lF
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#-----------------
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# WF data: mcsm-ppi2
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cols_to_select_mcsm_ppi2 = c(static_cols_start, c("mcsm_ppi2_outcome", mcsm_ppi2_dn), static_cols_end)
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#wf_mcsm_ppi2 = comb_df_sl[, cols_to_select_mcsm_ppi2]
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wf_mcsm_ppi2 = comb_df_sl_ppi2[, cols_to_select_mcsm_ppi2]
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pivot_cols_mcsm_ppi2 = cols_to_select_mcsm_ppi2[1: (length(static_cols_start) + 1)]; pivot_cols_mcsm_ppi2
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expected_rows_lf = nrow(wf_mcsm_ppi2) * (length(wf_mcsm_ppi2) - length(pivot_cols_mcsm_ppi2))
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expected_rows_lf
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# LF data: mcsm-ppi2
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lf_mcsm_ppi2 = gather(wf_mcsm_ppi2
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, key = param_type
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, value = param_value
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, all_of(mcsm_ppi2_dn):tail(static_cols_end,1)
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, factor_key = TRUE)
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if (nrow(lf_mcsm_ppi2) == expected_rows_lf){
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cat("\nPASS: long format data created for ", mcsm_ppi2_dn)
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}else{
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cat("\nFAIL: long format data could not be created for duet")
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quit()
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}
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# Assign them to the output list
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wf_lf_dataL[['wf_mcsm_ppi2']] = wf_mcsm_ppi2
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wf_lf_dataL[['lf_mcsm_ppi2']] = lf_mcsm_ppi2
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}
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return(wf_lf_dataL)
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}
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############################################################################
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