added ggcorr plots combined for all params
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scripts/plotting/ggcorr_all_PS_LIG.R
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389
scripts/plotting/ggcorr_all_PS_LIG.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("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")
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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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# 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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ggcorr_all_ps = "ggcorr_all_PS.svg"
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plot_ggcorr_all_ps = paste0(plotdir,"/", ggcorr_all_ps)
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# LIG
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ggcorr_all_lig = "ggcorr_all_LIG.svg"
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plot_ggcorr_all_lig = paste0(plotdir,"/", ggcorr_all_lig )
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# combined
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ggcorr_all_combined_labelled = "ggcorr_all_combined_labelled.svg"
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plot_ggcorr_all_combined_labelled = paste0(plotdir,"/", ggcorr_all_combined_labelled)
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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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# Data for Correlation plots:PS
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#===========================
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table(df_ps$duet_outcome)
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#===========================
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# Data for Correlation plots:foldx
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#===========================
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#============================
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# adding foldx scaled values
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# scale data b/w -1 and 1
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#============================
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n = which(colnames(df_ps) == "ddg"); n
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my_min = min(df_ps[,n]); my_min
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my_max = max(df_ps[,n]); my_max
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df_ps$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$foldx_outcome)
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#======================
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# adding log cols
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#======================
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df_ps$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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# 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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, "asa"
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, "rsa"
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, "kd_values"
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, "rd_values"
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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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#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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, "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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, "ASA"
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, "RSA"
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, "KD"
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, "RD"
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, "duet_outcome"
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, drug)
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length(my_corr_colnames)
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colnames(corr_data_ps)
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colnames(corr_data_ps) <- my_corr_colnames
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colnames(corr_data_ps)
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#------------------------
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# Data for ggcorr PS plot
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#------------------------
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start = 1
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end_ggcorr = which(colnames(corr_data_ps) == "duet_outcome"); end_ggcorr # should be the last column
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offset = 1
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my_ggcorr_ps = corr_data_ps[start:(end_ggcorr-1)]
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head(my_ggcorr_ps)
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# correlation matrix
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corr1 <- round(cor(my_ggcorr_ps, method = "spearman", use = "pairwise.complete.obs"), 1)
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# p-value matrix
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pmat1 <- cor_pmat(my_ggcorr_ps, method = "spearman", use = "pairwise.complete.obs")
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corr2 = psych::corr.test(my_ggcorr_ps
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, method = "spearman"
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, use = "pairwise.complete.obs")$r
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corr2 = round(corr2, 1)
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pmat2 = psych::corr.test(my_ggcorr_ps
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, method = "spearman"
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, adjust = "none"
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, use = "pairwise.complete.obs")$p
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corr1== corr2
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pmat1==pmat2
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#------------------------
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# Generate ggcorr PS plot
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#------------------------
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cat("ggCorr plot PS:", plot_ggcorr_all_ps)
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svg(plot_ggcorr_all_ps, width = 15, height = 15)
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ggcorr_ps = ggcorrplot(corr1
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, p.mat = pmat1
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, hc.order = TRUE
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, outline.col = "black"
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, ggtheme = ggplot2::theme_gray
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, colors = c("#6D9EC1", "white", "#E46726")
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, title = "Spearman correlations with DUET and Foldx stability")
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ggcorr_ps
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#dev.off()
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#===========================
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# Data for Correlation plots: LIG
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#===========================
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table(df_lig$ligand_outcome)
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df_lig$log10_or_mychisq = log10(df_lig$or_mychisq)
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df_lig$neglog_pval_fisher = -log10(df_lig$pval_fisher)
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df_lig$log10_or_kin = log10(df_lig$or_kin)
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df_lig$neglog_pwald_kin = -log10(df_lig$pwald_kin)
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# subset data to generate pairwise correlations
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cols_to_select_lig = 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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, "asa"
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, "rsa"
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, "kd_values"
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, "rd_values"
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, "ligand_outcome"
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, drug)
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corr_data_lig = df_lig[, cols_to_select_lig]
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dim(corr_data_lig)
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# assign nice colnames (for display)
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my_corr_colnames_lig = 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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, "ASA"
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, "RSA"
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, "KD"
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, "RD"
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, "ligand_outcome"
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, drug)
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length(my_corr_colnames)
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colnames(corr_data_lig)
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colnames(corr_data_lig) <- my_corr_colnames_lig
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colnames(corr_data_lig)
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#------------------------
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# Data for ggcorr LIG plot
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#------------------------
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start = 1
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end_ggcorr_lig = which(colnames(corr_data_lig) == "ligand_outcome"); end_ggcorr_lig # should be the last column
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offset = 1
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my_ggcorr_lig = corr_data_lig[start:(end_ggcorr_lig-1)]
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head(my_ggcorr_lig); str(my_ggcorr_lig)
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# correlation matrix
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corr1_lig <- round(cor(my_ggcorr_lig, method = "spearman", use = "pairwise.complete.obs"), 1)
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# p-value matrix
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pmat1_lig <- cor_pmat(my_ggcorr_lig, method = "spearman", use = "pairwise.complete.obs")
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corr2_lig = psych::corr.test(my_ggcorr_lig
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, method = "spearman"
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, use = "pairwise.complete.obs")$r
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corr2_lig = round(corr2_lig, 1)
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pmat2_lig = psych::corr.test(my_ggcorr_lig
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, method = "spearman"
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, adjust = "none"
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, use = "pairwise.complete.obs")$p
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corr1_lig == corr2_lig
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pmat1_lig == pmat2_lig
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# for display order columns by hc order of ps
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#col_order = levels(ggcorr_ps$data[2])
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#col_order <- c("Species", "Petal.Width", "Sepal.Length",
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#"Sepal.Width", "Petal.Length")
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#my_data2 <- my_data[, col_order]
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#my_data2
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#------------------------
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# Generate ggcorr LIG plot
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#------------------------
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cat("ggCorr LIG plot:", plot_ggcorr_all_lig)
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svg(plot_ggcorr_all_lig, width = 15, height = 15)
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ggcorr_lig = ggcorrplot(corr1_lig
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, p.mat = pmat1_lig
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, hc.order = TRUE
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, outline.col = "black"
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, ggtheme = ggplot2::theme_gray
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, colors = c("#6D9EC1", "white", "#E46726")
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, title = "Spearman correlations with ligand affinty")
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ggcorr_lig
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#dev.off()
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#######################################################
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#=============================
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# combine plots for output
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#=============================
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cat("Output plot:", plot_ggcorr_all_combined_labelled)
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svg(plot_ggcorr_all_combined_labelled , width = 30, height = 10)
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ggcorr_combined = cowplot::plot_grid(ggcorr_ps, ggcorr_lig
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, nrow = 1
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, align = "h"
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, labels = c("(a)","(b)")
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#, rel_heights = c(1, 1)
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#, rel_widths = c(0.85, 1, 1)
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, label_size = 25)
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dev.off()
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