output corr plots with coloured dots
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1 changed files with 24 additions and 141 deletions
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@ -14,7 +14,7 @@ getwd()
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source("Header_TT.R")
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require(cowplot)
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source("combining_dfs_plotting.R")
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source("my_pairs_panel.R") # with lower panel turned off
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# should return the following dfs, directories and variables
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# PS combined:
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@ -51,84 +51,29 @@ cat(paste0("Variables imported:"
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#=======
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# output
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#=======
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# can't combine by cowplot because not ggplots
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#corr_plot_combined = "corr_combined.svg"
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#plot_corr_plot_combined = paste0(plotdir,"/", corr_plot_combined)
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# PS
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corr_ps = "corr_PS.svg"
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plot_corr_ps = paste0(plotdir,"/", corr_ps)
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corr_ps_duet_col = "corr_PS_duet_coloured.svg"
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plot_corr_ps_duet_col = paste0(plotdir,"/", corr_ps_duet_col)
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corr_upper_ps = "corr_upper_PS.svg"
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plot_corr_upper_ps = paste0(plotdir,"/", corr_upper_ps)
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# LIG
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corr_lig = "corr_LIG.svg"
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plot_corr_lig = paste0(plotdir,"/", corr_lig)
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corr_upper_lig = "corr_upper_LIG.svg"
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plot_corr_upper_lig = paste0(plotdir,"/", corr_upper_lig)
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####################################################################
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# end of loading libraries and functions #
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########################################################################
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#%%%%%%%%%%%%%%%%%%%%%%%%%
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df_ps = merged_df3_comp
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df_lig = merged_df3_comp_lig
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df_ps = merged_df3
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df_lig = merged_df3_lig
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#%%%%%%%%%%%%%%%%%%%%%%%%%
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rm( merged_df2, merged_df2_comp, merged_df2_lig, merged_df2_comp_lig, my_df_u, my_df_u_lig)
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########################################################################
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# end of data extraction and cleaning for plots #
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########################################################################
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#============================
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# adding foldx scaled values
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# scale data b/w -1 and 1
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#============================
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n = which(colnames(df_ps) == "ddg"); n
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my_min = min(df_ps[,n]); my_min
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my_max = max(df_ps[,n]); my_max
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df_ps$foldx_scaled = ifelse(df_ps[,n] < 0
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, df_ps[,n]/abs(my_min)
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, df_ps[,n]/my_max)
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# sanity check
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my_min = min(df_ps$foldx_scaled); my_min
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my_max = max(df_ps$foldx_scaled); my_max
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if (my_min == -1 && my_max == 1){
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cat("PASS: foldx ddg successfully scaled b/w -1 and 1"
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, "\nProceeding with assigning foldx outcome category")
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}else{
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cat("FAIL: could not scale foldx ddg values"
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, "Aborting!")
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}
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#================================
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# adding foldx outcome category
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# ddg<0 = "Stabilising" (-ve)
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#=================================
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c1 = table(df_ps$ddg < 0)
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df_ps$foldx_outcome = ifelse(df_ps$ddg < 0, "Stabilising", "Destabilising")
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c2 = table(df_ps$ddg < 0)
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if ( all(c1 == c2) ){
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cat("PASS: foldx outcome successfully created")
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}else{
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cat("FAIL: foldx outcome could not be created. Aborting!")
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exit()
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}
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table(df_ps$foldx_outcome)
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################################
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# Data for Correlation plots: PS
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#################################
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#======================
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# adding log cols
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@ -160,9 +105,6 @@ corr_data_ps = df_ps[cols_to_select]
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dim(corr_data_ps)
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#p_italic = substitute(paste("-Log(", italic('P'), ")"));p_italic
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#p_adjusted_italic = substitute(paste("-Log(", italic('P adjusted'), ")"));p_adjusted_italic
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# assign nice colnames (for display)
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my_corr_colnames = c("DUET"
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@ -171,7 +113,7 @@ my_corr_colnames = c("DUET"
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, "Log (OR)"
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, "-Log (P)"
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, "AF"
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, "MAF"
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, "duet_outcome"
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, drug)
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@ -194,15 +136,12 @@ head(my_corr_ps)
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# deep red: #ae301e
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#---------------------------------------
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# generate corr PS plot 1: both panels
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# generate corr PS plot: both panels
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#---------------------------------------
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cat("Corr plot PS DUET with coloured dots:", plot_corr_ps)
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svg(plot_corr_ps, width = 15, height = 15)
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#cat("Corr plot PS DUET with coloured dots:",plot_corr_ps_duet_col)
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#svg(plot_corr_ps_duet_col, width = 15, height = 15)
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OutPlot1 = pairs.panels(my_corr_ps[1:(length(my_corr_ps)-1)]
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pairs.panels(my_corr_ps[1:(length(my_corr_ps)-1)]
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, method = "spearman" # correlation method
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, hist.col = "grey" ##00AFBB
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, density = TRUE # show density plots
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@ -211,12 +150,10 @@ OutPlot1 = pairs.panels(my_corr_ps[1:(length(my_corr_ps)-1)]
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, rug = F
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, breaks = "Sturges"
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, show.points = T
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#, bg = c("#f8766d", "#00bfc4")[unclass(factor(my_corr_ps$duet_outcome))] # can't use colour as duet and foldx are opposite
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#, pch = 21 # for bg
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, pch = 19
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, bg = c("#f8766d", "#00bfc4")[unclass(factor(my_corr_ps$duet_outcome))] # foldx colours are reveresed
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, pch = 21 # for bg
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, jitter = T
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, alpha = 1
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#, points(pch = 19, col = c("#f8766d", "#00bfc4"))
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, cex = 1.8
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, cex.axis = 2
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, cex.labels = 4
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@ -225,40 +162,15 @@ OutPlot1 = pairs.panels(my_corr_ps[1:(length(my_corr_ps)-1)]
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)
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print(OutPlot1)
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dev.off()
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#----------------------------------------------
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# generate corr PS plot 2: upper_panel only
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#----------------------------------------------
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cat("Corr plot upper PS:", plot_corr_upper_ps)
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svg(plot_corr_upper_ps, width = 15, height = 15)
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OutPlot1_upper = my_pp(my_corr_ps[1:(length(my_corr_ps)-1)] # no lower panel
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, method = "spearman" # correlation method
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, hist.col = "grey" ##00AFBB
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, density = TRUE # show density plots
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, ellipses = F # show correlation ellipses
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, stars = T
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, rug = F
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, breaks = "Sturges"
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, show.points = F
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, cex = 3
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, cex.axis = 1.6
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, cex.labels = 4
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, cex.cor = 1
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, smooth = F
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)
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print(OutPlot1_upper)
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dev.off()
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corr_ps_rho = corr.test(my_corr_ps[1:5], method = "spearman")$r
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corr_ps_p = corr.test(my_corr_ps[1:5], method = "spearman")$p
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################################################################################################
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#===========================
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###################################
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# Data for Correlation plots: LIG
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#===========================
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##################################
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table(df_lig$ligand_outcome)
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df_lig$log10_or_mychisq = log10(df_lig$or_mychisq)
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@ -289,7 +201,7 @@ my_corr_colnames = c("Ligand Affinity"
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, "Log (OR)"
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, "-Log (P)"
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, "AF"
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, "MAF"
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, "ligand_outcome"
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, drug)
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@ -308,13 +220,13 @@ my_corr_lig = corr_data_lig[start:(end-offset)]
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head(my_corr_lig)
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#---------------------------------------
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# generate corr LIG plot 1: both panels
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# generate corr LIG plot: both panels
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#---------------------------------------
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cat("Corr LIG plot:", plot_corr_lig)
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svg(plot_corr_lig, width = 15, height = 15)
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# uncomment as necessary
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OutPlot2 = pairs.panels(my_corr_lig[1:(length(my_corr_lig)-1)]
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pairs.panels(my_corr_lig[1:(length(my_corr_lig)-1)]
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, method = "spearman" # correlation method
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, hist.col = "grey" ##00AFBB
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, density = TRUE # show density plots
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@ -323,12 +235,9 @@ OutPlot2 = pairs.panels(my_corr_lig[1:(length(my_corr_lig)-1)]
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, rug = F
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, breaks = "Sturges"
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, show.points = T
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#, bg = c("#f8766d", "#00bfc4")[unclass(factor(my_corr_lig$ligand_outcome))] # can't use colour as duet and foldx are opposite
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#, pch = 21 # for bg
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, pch = 19
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, bg = c("#f8766d", "#00bfc4")[unclass(factor(my_corr_lig$ligand_outcome))]
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, pch = 21 # for bg
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, jitter = T
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#, alpha = .05
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#, points(pch = 19, col = c("#f8766d", "#00bfc4"))
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, cex = 2
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, cex.axis = 2
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, cex.labels = 4
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@ -336,35 +245,9 @@ OutPlot2 = pairs.panels(my_corr_lig[1:(length(my_corr_lig)-1)]
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, smooth = F
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)
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print(OutPlot2)
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dev.off()
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#---------------------------------------
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# generate corr LIG plot 2: upper panels
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#---------------------------------------
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cat("Corr LIG plot:", plot_corr_upper_lig)
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svg(plot_corr_upper_lig, width = 15, height = 15)
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# uncomment as necessary
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OutPlot2_upper = my_pp(my_corr_lig[1:(length(my_corr_lig)-1)]
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, method = "spearman" # correlation method
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, hist.col = "grey" ##00AFBB
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, density = TRUE # show density plots
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, ellipses = F # show correlation ellipses
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, stars = T
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, rug = F
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, breaks = "Sturges"
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, show.points = F
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#, alpha = .05
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#, points(pch = 19, col = c("#f8766d", "#00bfc4"))
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, cex = 3
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, cex.axis = 1.6
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, cex.labels = 4
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, cex.cor = 1
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, smooth = F
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)
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print(OutPlot2_upper)
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dev.off()
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corr_lig_rho = corr.test(my_corr_lig[1:4], method = "spearman")$r
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corr_lig_p = corr.test(my_corr_lig[1:4], method = "spearman")$p
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#######################################################
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