added embb plotting scripts
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scripts/plotting/plotting_thesis/embb/embb_appendix_tables.R
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scripts/plotting/plotting_thesis/embb/embb_appendix_tables.R
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#!/usr/bin/env Rscript
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#source("~/git/LSHTM_analysis/config/katg.R")
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#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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#=======
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# output
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#=======
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outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
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outdir_stats = paste0(outdir_images,"stats/")
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cat("\nOutput dir for stats:", outdir_stats)
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###################################################################
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geneL_normal = c("pnca")
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#geneL_na = c("gid", "rpob")
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geneL_na_v2 = c("gid")
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geneL_ppi2 = c("alr", "embb", "katg", "rpob")
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geneL_both = c("rpob")
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if (tolower(gene)%in%geneL_na_v2) {
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gene_colnames = c("mcsm_na_affinity", "mcsm_na_outcome")
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}
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if (tolower(gene)%in%geneL_ppi2) {
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gene_colnames = c("mcsm_ppi2_affinity", "mcsm_ppi2_outcome")
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}
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#from plotting_globals()
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LigDist_colname
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ppi2Dist_colname
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naDist_colname
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delta_symbol #delta_symbol = "\u0394"; delta_symbol
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angstroms_symbol
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cat("\nAffinity Distance colnames:", length(affinity_dist_colnames)
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, "\nThese are:", affinity_dist_colnames)
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#===========
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# Data used
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#===========
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df3 = merged_df3
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cols_to_output = c("position"
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, "sensitivity"
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, "mutationinformation"
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, affinity_dist_colnames[1]
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, "ligand_affinity_change"
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, "ligand_outcome"
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, "mmcsm_lig"
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, "mmcsm_lig_outcome"
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, affinity_dist_colnames[2]
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# #, affinity_dist_colnames[3]
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# , "mcsm_na_affinity"
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# , "mcsm_na_outcome"
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# #, "mcsm_nca_affinity"
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# #, "mcsm_nca_outcome"
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, gene_colnames
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, "maf"
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, "or_mychisq"
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, "pval_fisher")
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cols_to_output
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df3_output = df3[, cols_to_output]
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colnames(df3_output)
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cat("\nSelecting columns:", length(colnames(df3_output)))
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#===============================================
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# Add COLS and rounding: adjusted P-values + MAF
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#==============================================
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#-----------------------------
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# adjusted P-values
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#-----------------------------
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# add cols: p_adj_fdr and signif_fdr
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df3_output$p_adj_fdr = p.adjust(df3_output$pval_fisher, method = "fdr")
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df3_output$signif_fdr = df3_output$p_adj_fdr
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df3_output = dplyr::mutate(df3_output
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, signif_fdr = case_when(signif_fdr == 0.05 ~ "."
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, signif_fdr <=0.0001 ~ '****'
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, signif_fdr <=0.001 ~ '***'
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, signif_fdr <=0.01 ~ '**'
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, signif_fdr <0.05 ~ '*'
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, TRUE ~ 'ns'))
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# rounding
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df3_output$or_mychisq = round(df3_output$or_mychisq,2)
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df3_output$p_adj_fdr = round(df3_output$p_adj_fdr,2)
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head(df3_output)
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#----------
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# MAF (%)
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#----------
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# add col maf_percent
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df3_output$maf_percent = df3_output$maf*100
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# rounding
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df3_output$maf_percent = round(df3_output$maf_percent,2)
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head(df3_output$af); head(df3_output$maf);head(df3_output$maf_percent)
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#----------
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# P-value
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#----------
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df3_output$pval_fisher = round(df3_output$pval_fisher,2)
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class(df3_output)
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head(df3_output)
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####################################
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# Appendix: ligand affinity
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####################################
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df_lig = df3_output[df3_output[[LigDist_colname]]<DistCutOff,]
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cols_to_output_lig = c("position"
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, "sensitivity"
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, "mutationinformation"
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, LigDist_colname
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, "ligand_affinity_change"
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, "ligand_outcome"
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, "mmcsm_lig"
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, "mmcsm_lig_outcome"
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, "maf_percent"
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, "or_mychisq"
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, "pval_fisher"
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, "p_adj_fdr"
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, "signif_fdr")
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# select cols
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Out_df_lig = df_lig[, cols_to_output_lig]
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# sort df by OR and then MAF: highest OR and highest MAF
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#Out_df_ligS1 = Out_df_lig[order(Out_df_lig$or_mychisq, decreasing = T), ]
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Out_df_ligS = Out_df_lig[order(-Out_df_lig$or_mychisq, Out_df_lig$maf_percent), ]
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#head(Out_df_ligS1); tail(Out_df_ligS1)
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head(Out_df_ligS); tail(Out_df_ligS)
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colsNames_to_output_lig = c("position"
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, "sensitivity"
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, "Mutation"
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, paste0("Lig-Dist (", angstroms_symbol, ")")
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, "mCSM-ligand affinity"
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, "mCSM ligand_outcome"
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, "mmCSM-ligand affinity"
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, "mmCSM ligand_outcome"
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, paste0("MAF ","(%)")
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, "Odds Ratio"
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, "P-value"
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, "Adjusted P-value"
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, "Adjusted P-value significance")
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colnames(Out_df_ligS) = colsNames_to_output_lig
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head(Out_df_ligS)
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# ADD: active site annot
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nrow(Out_df_ligS)
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Out_df_ligS$drug_site = ifelse(Out_df_ligS$position%in%aa_pos_drug, "drug", "no")
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table(Out_df_ligS$drug_site)
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Out_df_ligS$heme_site = ifelse(Out_df_ligS$position%in%aa_pos_hem, "heme", "no")
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table(Out_df_ligS$heme_site)
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#--------------------
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# write output file: KS test within grpup
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#----------------------
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Out_ligT = paste0(outdir_stats
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, tolower(gene)
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, "_lig_muts.csv")
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cat("Output of Ligand muts:", Out_ligT )
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write.csv(Out_df_ligS, Out_ligT, row.names = FALSE)
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########################################################################
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####################################
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# Appendix: NA/PPI2 affinity
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# naDist_colname
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# ppi2Dist_colname
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####################################
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# Filtered data
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#df_nca = df3_output[df3_output[[naDist_colname]]<DistCutOff,]
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df_nca = df3_output[df3_output[[ppi2Dist_colname]]<DistCutOff,]
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# select cols
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cols_to_output_nca = c("position"
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, "sensitivity"
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, "mutationinformation"
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#, naDist_colname
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, ppi2Dist_colname
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, gene_colnames
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, "maf_percent"
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, "or_mychisq"
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, "pval_fisher"
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, "p_adj_fdr"
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, "signif_fdr")
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# extract output cols
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Out_df_nca = df_nca[, cols_to_output_nca]
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# sort df by OR and then MAF: Highest OR and Highest MAF
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#Out_df_ncaS = Out_df_nca[order(Out_df_nca$or_mychisq, decreasing = T), ]
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Out_df_ncaS = Out_df_nca[order(-Out_df_nca$or_mychisq, Out_df_nca$maf_percent), ]
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colsNames_to_output_nca = c("position"
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, "sensitivity"
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, "Mutation"
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# , paste0("NA-Dist (", angstroms_symbol, ")")
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# , paste0("mCSM-NA (", delta_symbol,delta_symbol,"G)")
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# , "mCSM-NA outcome"
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, paste0("PPI-Dist (", angstroms_symbol, ")")
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, paste0("mCSM-PPI (", delta_symbol,delta_symbol,"G)")
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, "mCSM-PPI outcome"
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, paste0("MAF ","(%)")
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, "Odds Ratio"
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, "P-value"
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, "Adjusted P-value"
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, "Adjusted P-value significance")
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colnames(Out_df_ncaS) = colsNames_to_output_nca
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Out_df_ncaS
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# ADD: active site annot
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nrow(Out_df_ncaS)
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Out_df_ncaS$drug_site = ifelse(Out_df_ncaS$position%in%aa_pos_drug, "drug", "no")
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table(Out_df_ncaS$drug_site)
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Out_df_ncaS$heme_site = ifelse(Out_df_ncaS$position%in%aa_pos_hem, "heme", "no")
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table(Out_df_ncaS$heme_site)
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#--------------------
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# write output file: KS test within grpup
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#----------------------
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Out_ncaT = paste0(outdir_stats
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, tolower(gene)
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, "_ppi2_muts.csv")
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cat("Output of NA muts:", Out_ncaT )
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write.csv(Out_df_ncaS, Out_ncaT, row.names = FALSE)
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#################################################################################
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#################################################################################
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#################################################################################
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##########################################################
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# higest or/maf and stability effects
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###########################################################
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# convert to percet
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df3$maf_percent = df3$maf*100
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cols_to_output_effects = c("position"
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, "sensitivity"
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, "mutationinformation"
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, "avg_stability"
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, "avg_stability_outcome"
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, affinity_dist_colnames[1]
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, "avg_lig_affinity"
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, "avg_lig_affinity_outcome"
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, affinity_dist_colnames[2]
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, gene_colnames
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, "maf_percent"
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, "or_mychisq"
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, "pval_fisher")
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df3_effects = df3[, cols_to_output_effects]
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nrow(df3_effects); ncol(df3_effects)
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# add cols: p_adj_fdr and signif_fdr
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df3_effects$p_adj_fdr = p.adjust(df3_effects$pval_fisher, method = "fdr")
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df3_effects$signif_fdr = df3_effects$p_adj_fdr
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df3_effects = dplyr::mutate(df3_effects
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, signif_fdr = case_when(signif_fdr == 0.05 ~ "."
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, signif_fdr <=0.0001 ~ '****'
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, signif_fdr <=0.001 ~ '***'
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, signif_fdr <=0.01 ~ '**'
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, signif_fdr <0.05 ~ '*'
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, TRUE ~ 'ns'))
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# rounding
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df3_effects$or_mychisq = round(df3_effects$or_mychisq,2)
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df3_effects$p_adj_fdr = round(df3_effects$p_adj_fdr,2)
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head(df3_effects)
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#-------------------
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# Highest OR and MAF
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#-------------------
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mut_hor = df3_effects[df3_effects$or_mychisq%in%(max(df3_effects$or_mychisq, na.rm = T)),]
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mut_hmaf = df3_effects[df3_effects$maf_percent%in%(max(df3_effects$maf_percent, na.rm = T)),]
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if (identical(colnames(mut_hor), colnames(mut_hmaf)) ){
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# add cols
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mut_hor$mutational_effect = "Mutation with highest OR"
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mut_hmaf$mutational_effect = "Most frequent mutation"
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cat("\nPass: or and maf")
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}else{
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quit("Abort: colnames or and maf mismatch")
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}
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#-------------------
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# Avg stability
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# most DD/SS: average stability
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#-------------------
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mut_h_avs_dd = df3_effects[df3_effects$avg_stability%in%(min(df3_effects$avg_stability, na.rm = T)),]
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mut_h_avs_ss = df3_effects[df3_effects$avg_stability%in%(max(df3_effects$avg_stability, na.rm = T)),]
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if (identical(colnames(mut_h_avs_dd), colnames(mut_h_avs_ss)) ){
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# add cols
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mut_h_avs_dd$mutational_effect = "Most Destabilising for protomer"
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mut_h_avs_ss$mutational_effect = "Most Stabilising for protomer"
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cat("\nPass : avg stability")
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}else{
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quit("Abort: colnames stability mismatch")
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}
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#-------------------
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# Filtered columns
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# most DD/SS: ligand
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# FIXME DUBIOUS as min and max can be both negative
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#-------------------
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df3_effects_lig = df3_effects[df3_effects[[LigDist_colname]]<DistCutOff,]
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nrow(df3_effects_lig)
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mut_h_lig_dd = df3_effects_lig[df3_effects_lig$avg_lig_affinity%in%(min(df3_effects_lig$avg_lig_affinity, na.rm = T)),]
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mut_h_lig_ss = df3_effects_lig[df3_effects_lig$avg_lig_affinity%in%(max(df3_effects_lig$avg_lig_affinity, na.rm = T)),]
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if (identical(colnames(mut_h_lig_dd), colnames(mut_h_lig_ss)) ){
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# add cols
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mut_h_lig_dd$mutational_effect = "Most Destabilising for Ligand affinity"
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mut_h_lig_ss$mutational_effect = "CAUTION: Most DE/Stabilising for Ligand affinity"
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cat("\nPass: avg ligand affinity")
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}else{
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quit("Abort: colnames lig mismatch")
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}
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#-------------------
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# Filtered columns
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# most DD/SS: NA
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#-------------------
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if (tolower(gene)%in%geneL_na_v2 ){
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df3_effects_na = df3_effects[df3_effects[[naDist_colname]]<DistCutOff,]
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nrow(df3_effects_na)
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mut_h_na_dd = df3_effects_na[df3_effects_na$mcsm_na_affinity%in%(min(df3_effects_na$mcsm_na_affinity, na.rm = T)),]
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mut_h_na_ss = df3_effects_na[df3_effects_na$mcsm_na_affinity%in%(max(df3_effects_na$mcsm_na_affinity, na.rm = T)),]
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# add cols
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mut_h_na_dd$mutational_effect = "Most Destabilising for NA affinity"
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mut_h_na_ss$mutational_effect = "Most Stabilising for NA affinity"
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if (identical(colnames(mut_h_na_dd), colnames(mut_h_na_ss)) ){
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cat("\nPass 1: NCA")
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}else{
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quit("Abort: colnames nca mismatch")
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}
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if (identical(colnames(mut_h_na_dd), colnames(mut_h_lig_dd)) ){
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cat("\nPass 2: NCA")
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}else{
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quit("Abort: colnames ppi2 mismatch")
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}
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#combine
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gene_aff_combined = rbind(mut_h_na_dd, mut_h_na_ss)
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}
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#-------------------
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# Filtered columns
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# most DD/SS: ppi2
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#-------------------
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if (tolower(gene)%in%geneL_ppi2 ){
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df3_effects_ppi2 = df3_effects[df3_effects[[ppi2Dist_colname]]<DistCutOff,]
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nrow(df3_effects_ppi2)
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mut_h_ppi2_dd = df3_effects_ppi2[df3_effects_ppi2$mcsm_ppi2_affinity%in%(min(df3_effects_ppi2$mcsm_ppi2_affinity, na.rm = T)),]
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mut_h_ppi2_ss = df3_effects_ppi2[df3_effects_ppi2$mcsm_ppi2_affinity%in%(max(df3_effects_ppi2$mcsm_ppi2_affinity, na.rm = T)),]
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# add cols
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mut_h_ppi2_dd$mutational_effect = "Most Destabilising for PPI affinity"
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mut_h_ppi2_ss$mutational_effect = "Most Stabilising for PPI affinity"
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if (identical(colnames(mut_h_ppi2_dd), colnames(mut_h_ppi2_ss)) ){
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cat("\nPass 1: ppi2")
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}else{
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quit("Abort: colnames ppi2 mismatch")
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}
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if (identical(colnames(mut_h_ppi2_dd), colnames(mut_h_lig_dd)) ){
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cat("\nPass 2 : ppi2")
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}else{
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quit("Abort: colnames ppi2 mismatch")
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}
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#combine
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gene_aff_combined = rbind(mut_h_ppi2_dd, mut_h_ppi2_ss)
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}
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#============
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# final combine
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#============
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if ( identical(colnames(mut_hor), colnames(mut_h_lig_dd)) ){
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cat("PASS: all")
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combined_table = rbind(mut_hor, mut_hmaf,
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mut_h_avs_dd, mut_h_avs_ss,
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mut_h_lig_dd, mut_h_lig_ss,
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gene_aff_combined)
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cat("\nCombined table dim:", "\nnrow:", nrow(combined_table), "\nncol:", ncol(combined_table))
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}else{
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quit("Abort: colnames ppi2 mismatch")
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}
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# Assign pretty colnames
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colnames(combined_table)
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colsNames_combined_table = c("position"
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, "sensitivity"
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, "Mutation"
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, paste0("Avg stability (", delta_symbol,delta_symbol,"G)")
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, "avg stability outcome"
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, paste0("Lig-Dist (", angstroms_symbol, ")")
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, "Avg ligand affinity"
|
||||
, "Ligand affinity outcome"
|
||||
|
||||
# , paste0("NA-Dist (", angstroms_symbol, ")")
|
||||
# , paste0("mCSM-NA (", delta_symbol,delta_symbol,"G)")
|
||||
# , "mCSM-NA outcome"
|
||||
|
||||
, paste0("PPI-Dist (", angstroms_symbol, ")")
|
||||
, paste0("mCSM-PPI (", delta_symbol,delta_symbol,"G)")
|
||||
, "mCSM-PPI outcome"
|
||||
|
||||
, paste0("MAF ","(%)")
|
||||
, "Odds Ratio"
|
||||
, "P-value"
|
||||
, "Adjusted P-value"
|
||||
, "Adjusted P-value significance"
|
||||
, "Mutational effect")
|
||||
|
||||
if ( length(colnames(combined_table)) == length(colsNames_combined_table) ) {
|
||||
cat("Assiging pretty colnames for output")
|
||||
colnames(combined_table) <- colsNames_combined_table
|
||||
#colnames(combined_table)
|
||||
}else{
|
||||
stop("\nAbort: No. of cols mismatch. Cannot assign pretty colnames for output")
|
||||
}
|
||||
|
||||
|
||||
nrow(combined_table)
|
||||
combined_table$drug_site = ifelse(combined_table$position%in%aa_pos_drug, "drug", "no")
|
||||
table(combined_table$drug_site)
|
||||
|
||||
combined_table$heme_site = ifelse(combined_table$position%in%aa_pos_hem, "heme", "no")
|
||||
table(combined_table$heme_site)
|
||||
|
||||
#--------------------
|
||||
# write output file: KS test within grpup
|
||||
#----------------------
|
||||
Out_combined_effectsT = paste0(outdir_stats
|
||||
, tolower(gene)
|
||||
, "_mut_effects.csv")
|
||||
|
||||
cat("Output of effects:", Out_combined_effectsT )
|
||||
write.csv(combined_table, Out_combined_effectsT, row.names = FALSE)
|
||||
Loading…
Add table
Add a link
Reference in a new issue