134 lines
5.4 KiB
R
134 lines
5.4 KiB
R
#!/usr/bin/env Rscript
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#########################################################
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# TASK: Script to format data for Correlation plots:
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# corr_data_extract()
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# INPUT:
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# df: data with all parameters (my_use case)
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# merged_df3 or merged_df2!?
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# gene: [sanity check]
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# drug: relates to a column name that will need to extracted
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# ligand_dist_colname = LigDist_colname (variable from plotting_globals()
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# colnames_to_extract = c("mutationinformation", "duet_affinity_change")
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# display_colnames_key = c(mutationinformation = "MUT" , duet_affinity_change = "DUET")
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# extract_scaled_cols = T or F, so that parameters with the _scaled suffix can be extracted.
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# NOTE*: No formatting applied to these cols i.e display name
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# RETURNS: DF
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# containing all the columns required for generating downstream correlation plots
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# TODO: ADD
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#lineage_count_all
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#lineage_count_unique
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#my_df['lineage_proportion'] = my_df['lineage_count_unique']/my_df['lineage_count_all']
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#my_df['dist_lineage_proportion'] = my_df['lineage_count_unique']/total_mtblineage_uc
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##################################################################
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# LigDist_colname #from globals: plotting_globals.R
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# ppi2Dist_colname #from globals: plotting_globals.R
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# naDist_colname #from globals: plotting_globals.R
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corr_data_extract <- function(df
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, gene
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, drug
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#, ligand_dist_colname = LigDist_colname
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, colnames_to_extract
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, colnames_display_key
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, extract_scaled_cols = F){
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if ( missing(colnames_to_extract) || missing(colnames_display_key) ){
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#if ( missing(colnames_to_extract) ){
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cat("\n=========================================="
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, "\nCORR PLOTS data: ALL params"
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, "\n=========================================")
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cat("\nExtracting default columns for"
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, "\nGene name:", gene
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, "\nDrug name:", drug)
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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_colnames = c(drug, "dst_mode"
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, "duet_stability_change" , "ddg_foldx" , "deepddg" , "ddg_dynamut2"
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, "asa" , "rsa" , "kd_values" , "rd_values"
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, "maf" , "log10_or_mychisq" , "neglog_pval_fisher"
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, LigDist_colname
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, "consurf_score" , "snap2_score" , "provean_score"
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, "ligand_affinity_change"
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#, "ddg_dynamut", "ddg_encom", "dds_encom", "ddg_mcsm", "ddg_sdm", "ddg_duet"
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)
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display_common_colnames = c( drug, "dst_mode"
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, "DUET" , "FoldX" , "DeepDDG", "Dynamut2"
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, "ASA" , "RSA" , "KD" , "RD"
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, "MAF" , "Log(OR)" , "-Log(P)"
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, "Lig-Dist"
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, "ConSurf" , "SNAP2" , "PROVEAN"
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, "mCSM-lig"
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# , "Dynamut" , "ENCoM-DDG" , "mCSM" , "SDM" , "DUET-d" , "ENCoM-DDS"
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)
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if (tolower(gene)%in%geneL_normal){
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colnames_to_extract = c(common_colnames)
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display_colnames = c(display_common_colnames)
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corr_df = df[,colnames_to_extract]
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colnames(corr_df) = display_colnames
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}
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if (tolower(gene)%in%geneL_ppi2){
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colnames_to_extract = c(common_colnames ,"mcsm_ppi2_affinity", ppi2Dist_colname)
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display_colnames = c(display_common_colnames,"mCSM-PPI2" , "PPI-Dist")
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corr_df = df[,colnames_to_extract]
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colnames(corr_df) = display_colnames
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}
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if (tolower(gene)%in%geneL_na){
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colnames_to_extract = c(common_colnames,"mcsm_na_affinity", naDist_colname)
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display_colnames = c(display_common_colnames, "mCSM-NA", "NA-Dist")
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corr_df = df[,colnames_to_extract]
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colnames(corr_df) = display_colnames
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}
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# [optional] arg: extract_scaled_cols
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if (extract_scaled_cols){
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cat("\nExtracting scaled columns as well...\n")
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all_scaled_cols = colnames(merged_df3)[grep(".*scaled", colnames(merged_df3))]
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colnames_to_extract = c(colnames_to_extract, all_scaled_cols)
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corr_df = df[,colnames_to_extract]
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colnames(corr_df) = display_colnames
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}else{
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colnames_to_extract = colnames_to_extract
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corr_df = df[,colnames_to_extract]
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colnames(corr_df) = display_colnames
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}
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# WORKED:
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# # extract df based on gene
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# corr_df = df[,colnames_to_extract]
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# colnames(corr_df)
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# display_colnames
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#
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# # arg: colnames_display_key
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# colnames(corr_df)[colnames(corr_df)%in%colnames_to_extract] <- display_colnames
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# colnames(corr_df)
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cat("\nExtracted ncols:", ncol(corr_df)
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,"\nRenaming successful")
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cat("\nSneak peak...")
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print(head(corr_df))
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# Move drug column to the end
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last_col = colnames(corr_df[ncol(corr_df)])
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#corr_df_f = corr_df %>% dplyr::relocate(all_of(drug), .after = last_col)
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#return(corr_df_f)
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return(corr_df)
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}
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}
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