added corr_data.R corr_PS_LIG_all.R corr_PS_LIG_v2.R
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166
scripts/plotting/corr_PS_LIG_all.R
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166
scripts/plotting/corr_PS_LIG_all.R
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: Corr plots for PS and Lig
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# Output: 1 svg
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#=======================================================================
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# working dir and loading libraries
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getwd()
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setwd("~/git/LSHTM_analysis/scripts/plotting/")
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getwd()
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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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source("corr_data.R")
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#=======
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# output
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#=======
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# PS
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corr_ps_all_df2 = "corr_PS_ALL_df2.svg"
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plot_corr_ps_all_df2 = paste0(plotdir,"/", corr_ps_all_df2)
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corr_ps_all_df3 = "corr_PS_ALL_df3.svg"
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plot_corr_ps_all_df3 = paste0(plotdir,"/", corr_ps_all_df3)
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# LIG
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corr_lig_all_df2 = "corr_LIG_ALL_df2.svg"
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plot_corr_lig_all_df2 = paste0(plotdir,"/", corr_lig_all_df2)
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corr_lig_all_df3 = "corr_LIG_ALL_df3.svg"
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plot_corr_lig_all_df3 = paste0(plotdir,"/", corr_lig_all_df3)
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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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# Data for plots
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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# PS
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corr_ps_df2 = corr_ps_df2[-1]
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corr_ps_df3 = corr_ps_df3[-1]
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# Lig
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corr_lig_df2 = corr_lig_df2[-1]
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corr_lig_df3 = corr_lig_df3[-1]
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#---------------------------------------
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# generate corr PS plot 1: merged_df2
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#---------------------------------------
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cat("Corr plot PS DUET with coloured dots:", plot_corr_ps_all_df2)
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svg(plot_corr_ps_all_df2, width = 30, height = 30)
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OutPlot_ps_df2 = pairs.panels(corr_ps_df2[1:(length(corr_ps_df2)-2)]
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, method = "spearman" # correlation method
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, hist.col = "grey" ##00AFBB
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, density = T # 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 = T
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, bg = c("#f8766d", "#00bfc4")[unclass(factor(corr_ps_df2$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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, 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 = 2
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, cex.cor = 1
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, smooth = F)
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print(OutPlot_ps_df2)
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dev.off()
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#----------------------------------------------
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# generate corr PS plot 2: merged_df3
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#----------------------------------------------
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cat("Corr plot PS DUET with coloured dots:", plot_corr_ps_all_df3)
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svg(plot_corr_ps_all_df3, width = 30, height = 30)
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OutPlot_ps_df3 = pairs.panels(corr_ps_df3[1:(length(corr_ps_df3)-2)]
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, method = "spearman" # correlation method
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, hist.col = "grey" ##00AFBB
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, density = T # 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 = T
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, bg = c("#f8766d", "#00bfc4")[unclass(factor(corr_ps_df3$duet_outcome))] # can't use colour as duet and foldx are opposite
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, pch = 21 # for bg
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, cex = 2
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, cex.axis = 1.6
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, cex.labels = 2
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, cex.cor = 1
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, smooth = F
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)
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print(OutPlot_ps_df3)
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dev.off()
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################################################################################################
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#---------------------------------------
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# generate corr lig plot 1: merged_df2
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#---------------------------------------
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cat("Corr plot lig DUET with coloured dots:", plot_corr_lig_all_df2)
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svg(plot_corr_lig_all_df2, width = 30, height = 30)
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OutPlot_lig_df2 = pairs.panels(corr_lig_df2[1:(length(corr_lig_df2)-2)]
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, method = "spearman" # correlation method
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, hist.col = "grey" ##00AFBB
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, density = T # show density plots
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, ellipses = F # show correlation elliliges
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, stars = T
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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(corr_lig_df2$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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, 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 = 2
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, cex.cor = 1
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, smooth = F)
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print(OutPlot_lig_df2)
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dev.off()
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#----------------------------------------------
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# generate corr lig plot 2: merged_df3
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#----------------------------------------------
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cat("Corr plot lig DUET with coloured dots:", plot_corr_lig_all_df3)
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svg(plot_corr_lig_all_df3, width = 30, height = 30)
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OutPlot_lig_df3 = pairs.panels(corr_lig_df3[1:(length(corr_lig_df3)-2)]
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, method = "spearman" # correlation method
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, hist.col = "grey" ##00AFBB
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, density = T # show density plots
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, ellipses = F # show correlation elliliges
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, stars = T
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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(corr_lig_df3$ligand_outcome))] # can't use colour as duet and foldx are opposite
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, pch = 21 # for bg
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, cex = 2
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, cex.axis = 1.6
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, cex.labels = 2
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, cex.cor = 1
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, smooth = F
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)
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print(OutPlot_lig_df3)
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dev.off()
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176
scripts/plotting/corr_PS_LIG_v2.R
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176
scripts/plotting/corr_PS_LIG_v2.R
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: Corr plots for PS and Lig
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# Output: 1 svg
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#=======================================================================
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# working dir and loading libraries
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getwd()
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setwd("~/git/LSHTM_analysis/scripts/plotting/")
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getwd()
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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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source("corr_data.R")
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#=======
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# output
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#=======
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# PS
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corrplot_ps_df2 = "corr_PS_df2.svg"
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plot_corr_ps_df2 = paste0(plotdir,"/", corrplot_ps_df2)
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corrplot_ps_df3 = "corr_PS_df3.svg"
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plot_corr_ps_df3 = paste0(plotdir,"/", corrplot_ps_df3)
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# LIG
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corrplot_lig_df2 = "corr_LIG_df2.svg"
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plot_corr_lig_df2 = paste0(plotdir,"/", corrplot_lig_df2)
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corrplot_lig_df3 = "corr_LIG_df3.svg"
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plot_corr_lig_df3 = paste0(plotdir,"/", corrplot_lig_df3)
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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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# Data for plots
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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cols_to_drop = c("ASA", "AF_kin")
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# PS
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corr_ps_df2 = corr_ps_df2[!colnames(corr_ps_df2)%in%cols_to_drop]
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corr_ps_df2 = corr_ps_df2[-1]
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corr_ps_df3 = corr_ps_df3[!colnames(corr_ps_df3)%in%cols_to_drop]
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corr_ps_df3 = corr_ps_df3[-1]
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# Lig
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corr_lig_df2 = corr_lig_df2[!colnames(corr_lig_df2)%in%cols_to_drop]
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corr_lig_df2 = corr_lig_df2[-1]
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corr_lig_df3 = corr_lig_df3[!colnames(corr_lig_df3)%in%cols_to_drop]
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corr_lig_df3 = corr_lig_df3[-1]
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#---------------------------------------
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# generate corr PS plot 1: merged_df2
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#---------------------------------------
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cat("Corr plot PS DUET with coloured dots:", plot_corr_ps_df2)
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svg(plot_corr_ps_df2, width = 30, height = 25)
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OutPlot_ps_df2 = pairs.panels(corr_ps_df2[1:(length(corr_ps_df2)-2)]
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, method = "spearman" # correlation method
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, hist.col = "grey" ##00AFBB
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, density = T # 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 = T
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, bg = c("#f8766d", "#00bfc4")[unclass(factor(corr_ps_df2$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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, 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 = 3.8
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, cex.cor = 1
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, smooth = F)
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print(OutPlot_ps_df2)
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dev.off()
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#----------------------------------------------
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# generate corr PS plot 2: merged_df3
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#----------------------------------------------
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cat("Corr plot PS DUET with coloured dots:", plot_corr_ps_df3)
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svg(plot_corr_ps_df3, width = 30, height = 25)
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OutPlot_ps_df3 = pairs.panels(corr_ps_df3[1:(length(corr_ps_df3)-2)]
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, method = "spearman" # correlation method
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, hist.col = "grey" ##00AFBB
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, density = T # 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 = T
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, bg = c("#f8766d", "#00bfc4")[unclass(factor(corr_ps_df3$duet_outcome))] # can't use colour as duet and foldx are opposite
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, pch = 21 # for bg
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, cex = 3
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, cex.axis = 1.6
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, cex.labels = 3.8
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, cex.cor = 1
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, smooth = F
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)
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print(OutPlot_ps_df3)
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dev.off()
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################################################################################################
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#---------------------------------------
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# generate corr lig plot 1: merged_df2
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#---------------------------------------
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cat("Corr plot lig DUET with coloured dots:", plot_corr_lig_df2)
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svg(plot_corr_lig_df2, width = 30, height = 25)
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OutPlot_lig_df2 = pairs.panels(corr_lig_df2[1:(length(corr_lig_df2)-2)]
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, method = "spearman" # correlation method
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, hist.col = "grey" ##00AFBB
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, density = T # show density plots
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, ellipses = F # show correlation elliliges
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, stars = T
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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(corr_lig_df2$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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, 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 = 3.8
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, cex.cor = 1
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, smooth = F)
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print(OutPlot_lig_df2)
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dev.off()
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#----------------------------------------------
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# generate corr lig plot 2: merged_df3
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#----------------------------------------------
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cat("Corr plot lig DUET with coloured dots:", plot_corr_lig_df3)
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svg(plot_corr_lig_df3, width = 30, height = 25)
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OutPlot_lig_df3 = pairs.panels(corr_lig_df3[1:(length(corr_lig_df3)-2)]
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, method = "spearman" # correlation method
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, hist.col = "grey" ##00AFBB
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, density = T # show density plots
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, ellipses = F # show correlation elliliges
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, stars = T
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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(corr_lig_df3$ligand_outcome))] # can't use colour as duet and foldx are opposite
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, pch = 21 # for bg
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, cex = 3
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, cex.axis = 1.6
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, cex.labels = 3.8
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, cex.cor = 1
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, smooth = F
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)
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print(OutPlot_lig_df3)
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dev.off()
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232
scripts/plotting/corr_data.R
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232
scripts/plotting/corr_data.R
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: Prepare for correlation data
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#=======================================================================
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# working dir and loading libraries
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getwd()
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setwd("~/git/LSHTM_analysis/scripts/plotting/")
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getwd()
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#source("Header_TT.R")
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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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# 1) merged_df2
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# 2) merged_df2_comp
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# 3) merged_df3
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# 4) merged_df3_comp
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# LIG combined:
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# 5) merged_df2_lig
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# 6) merged_df2_comp_lig
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# 7) merged_df3_lig
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# 8) merged_df3_comp_lig
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# 9) my_df_u
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# 10) my_df_u_lig
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cat(paste0("Directories imported:"
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, "\ndatadir:", datadir
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, "\nindir:", indir
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, "\noutdir:", outdir
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, "\nplotdir:", plotdir))
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cat(paste0("Variables imported:"
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, "\ndrug:", drug
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, "\ngene:", gene
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, "\ngene_match:", gene_match
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, "\nAngstrom symbol:", angstroms_symbol
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, "\nNo. of duplicated muts:", dup_muts_nu
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, "\nNA count for ORs:", na_count
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, "\nNA count in df2:", na_count_df2
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, "\nNA count in df3:", na_count_df3))
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#=======
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# output
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#=======
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# corr_ps_df2
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# corr_lig_df2
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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
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df_ps = merged_df2
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#df_lig = merged_df3_lig
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df_lig = merged_df2_lig
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#%%%%%%%%%%%%%%%%%%%%%%%%%
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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 log cols
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#======================
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df_ps$log10_or_mychisq = log10(df_ps$or_mychisq)
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df_ps$neglog_pval_fisher = -log10(df_ps$pval_fisher)
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df_ps$log10_or_kin = log10(df_ps$or_kin)
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df_ps$neglog_pwald_kin = -log10(df_ps$pwald_kin)
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#df_ps$mutation_info_labels = ifelse(df_ps$mutation_info == dr_muts_col, 1, 0)
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#===========================
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# Data for Correlation plots:PS
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#===========================
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# subset data to generate pairwise correlations
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cols_to_select = c("mutationinformation"
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, "duet_scaled"
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, "foldx_scaled"
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#, "mutation_info_labels"
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, "asa"
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, "rsa"
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, "rd_values"
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, "kd_values"
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, "log10_or_mychisq"
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, "neglog_pval_fisher"
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, "or_kin"
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, "neglog_pwald_kin"
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, "af"
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, "af_kin"
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, "duet_outcome"
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, drug)
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corr_data_ps = df_ps[cols_to_select]
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dim(corr_data_ps)
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# assign nice colnames (for display)
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my_corr_colnames = c("Mutation"
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, "DUET"
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, "Foldx"
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#, "Mutation class"
|
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, "ASA"
|
||||
, "RSA"
|
||||
, "RD"
|
||||
, "KD"
|
||||
, "Log (OR)"
|
||||
, "-Log (P)"
|
||||
, "Adjusted (OR)"
|
||||
, "-Log (P wald)"
|
||||
, "AF"
|
||||
, "AF_kin"
|
||||
, "duet_outcome"
|
||||
, drug)
|
||||
|
||||
length(my_corr_colnames)
|
||||
|
||||
colnames(corr_data_ps)
|
||||
colnames(corr_data_ps) <- my_corr_colnames
|
||||
colnames(corr_data_ps)
|
||||
|
||||
start = 1
|
||||
end = which(colnames(corr_data_ps) == drug); end # should be the last column
|
||||
offset = 1
|
||||
|
||||
#corr_ps_df2 = corr_data_ps[start:(end-offset)] # without drug
|
||||
corr_ps_df2 = corr_data_ps[start:end]
|
||||
head(corr_ps_df2)
|
||||
|
||||
#-----------------
|
||||
# short_df ps: merged_df3
|
||||
#-----------------
|
||||
corr_ps_df3 = corr_ps_df2[!duplicated(corr_ps_df2$Mutation),]
|
||||
|
||||
na_or = sum(is.na(corr_ps_df3$`Log (OR)`))
|
||||
check1 = nrow(corr_ps_df3) - na_or
|
||||
|
||||
na_adj_or = sum(is.na(corr_ps_df3$`adjusted (OR)`))
|
||||
check2 = nrow(corr_ps_df3) - na_adj_or
|
||||
|
||||
#if ( nrow(corr_ps_df3) == nrow(merged_df3) ) {
|
||||
# cat( "PASS: No. of rows for corr_ps_df3 match" )
|
||||
#}if ( nrow(merged_df3_comp) == check1 ){
|
||||
# cat( "PASS: No. of OR values checked" )
|
||||
#}
|
||||
|
||||
################################################################################################
|
||||
#===========================
|
||||
# Data for Correlation plots: LIG
|
||||
#===========================
|
||||
table(df_lig$ligand_outcome)
|
||||
|
||||
df_lig$log10_or_mychisq = log10(df_lig$or_mychisq)
|
||||
df_lig$neglog_pval_fisher = -log10(df_lig$pval_fisher)
|
||||
|
||||
df_lig$log10_or_kin = log10(df_lig$or_kin)
|
||||
df_lig$neglog_pwald_kin = -log10(df_lig$pwald_kin)
|
||||
|
||||
# subset data to generate pairwise correlations
|
||||
cols_to_select = c("mutationinformation"
|
||||
, "affinity_scaled"
|
||||
#, "mutation_info_labels"
|
||||
, "asa"
|
||||
, "rsa"
|
||||
, "rd_values"
|
||||
, "kd_values"
|
||||
, "log10_or_mychisq"
|
||||
, "neglog_pval_fisher"
|
||||
, "or_kin"
|
||||
, "neglog_pwald_kin"
|
||||
, "af"
|
||||
, "af_kin"
|
||||
, "ligand_outcome"
|
||||
, drug)
|
||||
|
||||
corr_data_lig = df_lig[, cols_to_select]
|
||||
|
||||
|
||||
dim(corr_data_lig)
|
||||
|
||||
# assign nice colnames (for display)
|
||||
my_corr_colnames = c("Mutation"
|
||||
, "Ligand Affinity"
|
||||
#, "Mutation class"
|
||||
, "ASA"
|
||||
, "RSA"
|
||||
, "RD"
|
||||
, "KD"
|
||||
, "Log (OR)"
|
||||
, "-Log (P)"
|
||||
, "Adjusted (OR)"
|
||||
, "-Log (P wald)"
|
||||
, "AF"
|
||||
, "AF_kin"
|
||||
, "ligand_outcome"
|
||||
, drug)
|
||||
|
||||
length(my_corr_colnames)
|
||||
|
||||
colnames(corr_data_lig)
|
||||
colnames(corr_data_lig) <- my_corr_colnames
|
||||
colnames(corr_data_lig)
|
||||
|
||||
start = 1
|
||||
end = which(colnames(corr_data_lig) == drug); end # should be the last column
|
||||
offset = 1
|
||||
|
||||
#corr_lig_df2 = corr_data_lig[start:(end-offset)] # without drug
|
||||
corr_lig_df2 = corr_data_lig[start:end]
|
||||
head(corr_lig_df2)
|
||||
|
||||
|
||||
#-----------------
|
||||
# short_df lig: merged_df3_lig
|
||||
#-----------------
|
||||
|
||||
corr_lig_df3 = corr_lig_df2[!duplicated(corr_lig_df2$Mutation),]
|
||||
|
||||
#######################################################
|
||||
rm(merged_df2, merged_df2_lig, merged_df3, merged_df3_lig
|
||||
, merged_df2_comp , merged_df3_comp, merged_df2_comp_lig, merged_df3_comp_lig
|
||||
, corr_data_ps, corr_data_lig)
|
Loading…
Add table
Add a link
Reference in a new issue