201 lines
6.7 KiB
R
201 lines
6.7 KiB
R
#!/usr/bin/env Rscript
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#source("~/git/LSHTM_analysis/config/alr.R")
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source("~/git/LSHTM_analysis/config/embb.R")
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#source("~/git/LSHTM_analysis/config/katg.R")
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#source("~/git/LSHTM_analysis/config/gid.R")
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#source("~/git/LSHTM_analysis/config/pnca.R")
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#source("~/git/LSHTM_analysis/config/rpob.R")
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# get plottting dfs
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source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.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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###################################################################
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# FIXME: ADD distance to NA when SP replies
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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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#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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#===========
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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("mutationinformation"
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, "position"
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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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, "mcsm_ppi2_affinity"
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, "mcsm_ppi2_outcome"
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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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#===============================================
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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$pval_fisher,2)
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class(df3_output)
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head(df3_output)
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####################################
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# Appendix: ligand affinity
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####################################
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df_lig = df3_output[df3_output[[LigDist_colname]]<DistCutOff,]
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cols_to_output_lig = c("mutationinformation"
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, "position"
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, LigDist_colname
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, "ligand_affinity_change"
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, "ligand_outcome"
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, "mmcsm_lig"
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, "mmcsm_lig_outcome"
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, "maf_percent"
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, "or_mychisq"
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, "pval_fisher"
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, "p_adj_fdr"
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, "signif_fdr")
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# select cols
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Out_df_lig = df_lig[, cols_to_output_lig]
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# sort df by OR and then MAF: highest OR and highest MAF
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#Out_df_ligS1 = Out_df_lig[order(Out_df_lig$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("Mutation"
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, "position"
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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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#--------------------
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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: PPi2 affinity
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####################################
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# Filtered data
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df_ppi2 = df3_output[df3_output[[ppi2Dist_colname]]<DistCutOff,]
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# select cols
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cols_to_output_ppi2 = c("mutationinformation"
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, "position"
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, ppi2Dist_colname
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, "mcsm_ppi2_affinity"
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, "mcsm_ppi2_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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# extract output cols
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Out_df_ppi2 = df_ppi2[, cols_to_output_ppi2]
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# sort df by OR and then MAF: Highest OR and Highest MAF
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#Out_df_ppi2S = Out_df_ppi2[order(Out_df_ppi2$or_mychisq, decreasing = T), ]
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Out_df_ppi2S = Out_df_ppi2[order(-Out_df_ppi2$or_mychisq, Out_df_ppi2$maf_percent), ]
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colsNames_to_output_ppi2 = c("Mutation"
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, "position"
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, paste0("PPI2-Dist (", angstroms_symbol, ")")
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, paste0("mCSM-PPI2 (", delta_symbol,delta_symbol,"G)")
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, "mCSM-PPI2 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_ppi2S) = colsNames_to_output_ppi2
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Out_df_ppi2S
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#--------------------
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# write output file: KS test within grpup
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#----------------------
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Out_ppi2T = paste0(outdir_stats
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, tolower(gene)
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, "_ppi2_muts.csv")
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cat("Output of PPI2 muts:", Out_ppi2T )
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write.csv(Out_df_ppi2S, Out_ppi2T, row.names = FALSE)
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