renamed file to denote corr adjusted and plain
This commit is contained in:
parent
fb0646373b
commit
1b5280145b
2 changed files with 95 additions and 95 deletions
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@ -14,6 +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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@ -77,33 +78,76 @@ rm( merged_df2, merged_df2_comp, merged_df2_lig, merged_df2_comp_lig, my_df_u, m
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# end of data extraction and cleaning for plots #
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########################################################################
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#===========================
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# Data for Correlation plots:PS
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#===========================
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table(df_ps$duet_outcome)
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#============================
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# adding foldx scaled values
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# scale data b/w -1 and 1
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#============================
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n = which(colnames(df_ps) == "ddg"); n
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my_min = min(df_ps[,n]); my_min
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my_max = max(df_ps[,n]); my_max
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df_ps$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$foldx_outcome)
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#======================
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# adding log cols
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#======================
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df_ps$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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#===========================
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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("duet_scaled"
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, "foldx_scaled"
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, "log10_or_mychisq"
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, "neglog_pval_fisher"
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, "log10_or_kin"
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, "neglog_pwald_kin"
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, "af"
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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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corr_data_ps = df_ps[cols_to_select]
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dim(corr_data_ps)
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@ -113,12 +157,11 @@ dim(corr_data_ps)
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# assign nice colnames (for display)
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my_corr_colnames = c("DUET"
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, "Foldx"
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, "Log(OR)"
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, "-Log(P)"
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, "Log(OR adjusted)"
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, "-Log(P wald)"
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, "AF"
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, "duet_outcome"
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@ -147,7 +190,8 @@ head(my_corr_ps)
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cat("Corr plot PS:", plot_corr_ps)
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svg(plot_corr_ps, width = 15, height = 15)
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OutPlot1 = pairs.panels(my_corr_ps[1:(length(my_corr_ps)-1)]
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#OutPlot1 = pairs.panels([1:(length(my_corr_ps)-1)]
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OutPlot1 = my_pp(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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@ -162,10 +206,11 @@ OutPlot1 = pairs.panels(my_corr_ps[1:(length(my_corr_ps)-1)]
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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 = 2.5
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, cex.axis = 1.5
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, cex.labels = 2.1
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, cex.cor = 1
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, smooth = F
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)
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print(OutPlot1)
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@ -190,9 +235,6 @@ cols_to_select = c("affinity_scaled"
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, "log10_or_mychisq"
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, "neglog_pval_fisher"
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, "log10_or_kin"
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, "neglog_pwald_kin"
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, "af"
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, "ligand_outcome"
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@ -209,9 +251,6 @@ my_corr_colnames = c("Ligand Affinity"
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, "Log(OR)"
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, "-Log(P)"
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, "Log(OR adjusted)"
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, "-Log(P wald)"
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, "AF"
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, "ligand_outcome"
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@ -237,7 +276,8 @@ head(my_corr_lig)
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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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OutPlot2 = pairs.panels(my_corr_lig[1:(length(my_corr_lig)-1)]
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#OutPlot2 = pairs.panels(my_corr_lig[1:(length(my_corr_lig)-1)]
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OutPlot2 = 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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@ -252,7 +292,7 @@ OutPlot2 = pairs.panels(my_corr_lig[1:(length(my_corr_lig)-1)]
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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 = 2.5
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, cex.axis = 1.5
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, cex.labels = 2.1
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, cex.cor = 1
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, smooth = F
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@ -261,5 +301,4 @@ OutPlot2 = pairs.panels(my_corr_lig[1:(length(my_corr_lig)-1)]
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print(OutPlot2)
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dev.off()
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#######################################################
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library(lattice)
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@ -14,7 +14,6 @@ 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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@ -56,12 +55,12 @@ cat(paste0("Variables imported:"
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#plot_corr_plot_combined = paste0(plotdir,"/", corr_plot_combined)
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# PS
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corr_ps_s2 = "corr_PS_style2.svg"
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plot_corr_ps_s2 = paste0(plotdir,"/", corr_ps_s2)
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corr_ps_adjusted = "corr_PS_adjusted.svg"
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plot_corr_ps_adjusted = paste0(plotdir,"/", corr_ps)
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# LIG
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corr_lig_s2 = "corr_LIG_style2.svg"
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plot_corr_lig_s2 = paste0(plotdir,"/", corr_lig_s2)
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corr_lig_adjusted = "corr_LIG_adjusted.svg"
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plot_corr_lig_adjusted = paste0(plotdir,"/", corr_lig)
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####################################################################
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# end of loading libraries and functions #
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@ -78,76 +77,33 @@ rm( merged_df2, merged_df2_comp, merged_df2_lig, merged_df2_comp_lig, my_df_u, m
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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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#===========================
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# Data for Correlation plots:PS
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#===========================
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table(df_ps$duet_outcome)
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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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# 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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#===========================
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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("duet_scaled"
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, "foldx_scaled"
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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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, "duet_outcome"
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, drug)
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corr_data_ps = df_ps[cols_to_select]
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corr_data_ps = df_ps[, cols_to_select]
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dim(corr_data_ps)
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@ -157,11 +113,12 @@ dim(corr_data_ps)
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# assign nice colnames (for display)
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my_corr_colnames = c("DUET"
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, "Foldx"
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, "Log(OR)"
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, "-Log(P)"
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#, "OR adjusted"
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#, "-Log(P wald)"
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, "AF"
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, "duet_outcome"
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@ -187,11 +144,10 @@ head(my_corr_ps)
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# deep blue :#007d85
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# deep red: #ae301e
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cat("Corr plot PS:", plot_corr_ps_s2)
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svg(plot_corr_ps_s2, width = 15, height = 15)
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cat("Corr plot PS:", plot_corr_ps_adjusted)
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svg(plot_corr_ps_adjusted, width = 15, height = 15)
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#OutPlot1 = pairs.panels([1:(length(my_corr_ps)-1)]
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OutPlot1 = my_pp(my_corr_ps[1:(length(my_corr_ps)-1)]
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OutPlot1 = 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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@ -206,11 +162,10 @@ OutPlot1 = my_pp(my_corr_ps[1:(length(my_corr_ps)-1)]
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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 = 1.5
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, cex.axis = 2.5
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, cex.labels = 2.1
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, cex.cor = 1
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, smooth = F
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)
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print(OutPlot1)
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@ -235,6 +190,9 @@ cols_to_select = c("affinity_scaled"
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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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, "ligand_outcome"
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@ -251,6 +209,9 @@ my_corr_colnames = c("Ligand Affinity"
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, "Log(OR)"
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, "-Log(P)"
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#, "OR adjusted"
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#, "-Log(P wald)"
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, "AF"
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, "ligand_outcome"
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@ -273,11 +234,10 @@ offset = 1
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my_corr_lig = corr_data_lig[start:(end-offset)]
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head(my_corr_lig)
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cat("Corr LIG plot:", plot_corr_lig_s2)
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svg(plot_corr_lig_s2, width = 15, height = 15)
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cat("Corr LIG plot:", plot_corr_lig_adjusted)
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svg(plot_corr_lig_adjusted, width = 15, height = 15)
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#OutPlot2 = pairs.panels(my_corr_lig[1:(length(my_corr_lig)-1)]
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OutPlot2 = my_pp(my_corr_lig[1:(length(my_corr_lig)-1)]
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OutPlot2 = 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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@ -292,7 +252,7 @@ OutPlot2 = my_pp(my_corr_lig[1:(length(my_corr_lig)-1)]
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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 = 1.5
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, cex.axis = 2.5
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, cex.labels = 2.1
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, cex.cor = 1
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, smooth = F
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@ -301,4 +261,5 @@ OutPlot2 = my_pp(my_corr_lig[1:(length(my_corr_lig)-1)]
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print(OutPlot2)
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dev.off()
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#######################################################
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library(lattice)
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