added colors for tiles for gid plots and appendix tables
This commit is contained in:
parent
ac72634b48
commit
e4e8bd7278
3 changed files with 273 additions and 42 deletions
14
config/gid.R
14
config/gid.R
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@ -129,15 +129,15 @@ cat("\n==================================================="
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##############################################################
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# var for position customisation for plots
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aa_pos_drug
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aa_pos_lig1 = aa_pos_sam
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aa_pos_lig2 = aa_pos_rna
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aa_pos_lig3 = aa_pos_amp
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aa_pos_drug = c(0) #00ff00 # green # as STR doesn't bind
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aa_pos_lig1 = aa_pos_sam #2f4f4f # darkslategrey
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aa_pos_lig2 = aa_pos_rna #ff1493 #deeppink
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aa_pos_lig3 = aa_pos_amp #000080 #navyblue
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tile_map=data.frame(tile=c("SRY","SAM","RNA","AMP"),
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tile_colour=c("green","darkslategrey","darkred","navyblue"))
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tile_map=data.frame(tile=c("STR","SAM","RNA","AMP"),
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tile_colour=c("#00ff00","#2f4f4f","#ff1493","#000080"))
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# green: #00ff00
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# darkslategrey : #2f4f4f
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# darkred : #8b0000
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# deeppink : #ff1493
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# navyblue :#000080
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@ -1,9 +1,10 @@
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#!/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/embb.R")
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source("~/git/LSHTM_analysis/config/gid.R")
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#source("~/git/LSHTM_analysis/config/alr.R")
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#source("~/git/LSHTM_analysis/config/katg.R")
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#source("~/git/LSHTM_analysis/config/rpob.R")
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# get plottting dfs
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@ -1,27 +1,29 @@
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#!/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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cat("\nOutput dir for stats:", outdir_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_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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@ -31,22 +33,28 @@ 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("mutationinformation"
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, "position"
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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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, "mcsm_ppi2_affinity"
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, "mcsm_ppi2_outcome"
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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_ppi2_affinity"
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# #, "mcsm_ppi2_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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@ -54,7 +62,7 @@ cols_to_output = c("mutationinformation"
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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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@ -91,7 +99,6 @@ head(df3_output$af); head(df3_output$maf);head(df3_output$maf_percent)
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#----------
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df3_output$pval_fisher = round(df3_output$pval_fisher,2)
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class(df3_output)
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head(df3_output)
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@ -100,8 +107,9 @@ head(df3_output)
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####################################
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df_lig = df3_output[df3_output[[LigDist_colname]]<DistCutOff,]
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cols_to_output_lig = c("mutationinformation"
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, "position"
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cols_to_output_lig = c("position"
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, "sensitivity"
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, "mutationinformation"
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, LigDist_colname
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, "ligand_affinity_change"
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, "ligand_outcome"
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@ -122,8 +130,9 @@ Out_df_ligS = Out_df_lig[order(-Out_df_lig$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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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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@ -150,18 +159,20 @@ 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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# 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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cols_to_output_ppi2 = c("position"
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, "sensitivity"
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, "mutationinformation"
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, ppi2Dist_colname
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, "mcsm_ppi2_affinity"
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, "mcsm_ppi2_outcome"
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#, "mcsm_ppi2_affinity"
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#, "mcsm_ppi2_outcome"
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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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@ -176,8 +187,9 @@ Out_df_ppi2 = df_ppi2[, cols_to_output_ppi2]
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Out_df_ppi2S = Out_df_ppi2[order(-Out_df_ppi2$or_mychisq, Out_df_ppi2$maf_percent), ]
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colsNames_to_output_ppi2 = c("Mutation"
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, "position"
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colsNames_to_output_ppi2 = c("position"
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, "sensitivity"
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, "Mutation"
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, paste0("PPI2-Dist (", angstroms_symbol, ")")
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, paste0("mCSM-PPI2 (", delta_symbol,delta_symbol,"G)")
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, "mCSM-PPI2 outcome"
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@ -199,3 +211,221 @@ Out_ppi2T = paste0(outdir_stats
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cat("Output of PPI2 muts:", Out_ppi2T )
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write.csv(Out_df_ppi2S, Out_ppi2T, row.names = FALSE)
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#################################################################################
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#################################################################################
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#################################################################################
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##########################################################
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# higest or/maf and stability effects
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###########################################################
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# convert to percet
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df3$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$mutational_effect = "Most Stabilising for protomer"
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cat("\nPass : avg stability")
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}else{
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quit("Abort: colnames stability mismatch")
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}
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#-------------------
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# Filtered columns
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# most DD/SS: ligand
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#-------------------
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df3_effects_lig = df3_effects[df3_effects[[LigDist_colname]]<DistCutOff,]
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nrow(df3_effects_lig)
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mut_h_lig_dd = df3_effects_lig[df3_effects_lig$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$mutational_effect = "Most Stabilising for Ligand affinity"
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cat("\nPass: avg ligand affinity")
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}else{
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quit("Abort: colnames lig mismatch")
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}
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#-------------------
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# Filtered columns
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# most DD/SS: NA
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#-------------------
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if (tolower(gene)%in%geneL_na_v2 ){
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df3_effects_na = df3_effects[df3_effects[[naDist_colname]]<DistCutOff,]
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nrow(df3_effects_na)
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mut_h_na_dd = df3_effects_na[df3_effects_na$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_dd$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_na_dd, mut_h_na_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"
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, "Ligand affinity outcome"
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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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, "Mutational effect")
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if ( length(colnames(combined_table)) == length(colsNames_combined_table) ) {
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cat("Assiging pretty colnames for output")
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colnames(combined_table) <- colsNames_combined_table
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#colnames(combined_table)
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}else{
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stop("\nAbort: No. of cols mismatch. Cannot assign pretty colnames for output")
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}
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#--------------------
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# write output file: KS test within grpup
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#----------------------
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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