renamed corr_plot scripts
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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") # 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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# 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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# 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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####################################################################
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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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#%%%%%%%%%%%%%%%%%%%%%%%%%
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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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# 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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, "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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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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, "AF"
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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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# generate corr PS plot
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#-----------------
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start = 1
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end = which(colnames(corr_data_ps) == drug); end # should be the last column
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offset = 1
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my_corr_ps = corr_data_ps[start:(end-offset)]
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head(my_corr_ps)
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#my_cols = c("#f8766d", "#00bfc4")
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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)
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svg(plot_corr_ps, 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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, 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 = T
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, bg = c("#f8766d", "#00bfc4")[unclass(factor(my_corr_ps$duet_outcome))]
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, pch = 21
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, jitter = T
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#, alpha = .05
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#, points(pch = 19, col = c("#f8766d", "#00bfc4"))
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, cex = 3
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, cex.axis = 1.5
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, cex.labels = 2.1
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, cex.cor = 1
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, smooth = F
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)
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print(OutPlot1)
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dev.off()
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#===========================
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# Data for Correlation plots: LIG
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#===========================
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table(df_lig$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 = c("affinity_scaled"
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, "log10_or_mychisq"
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, "neglog_pval_fisher"
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, "af"
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, "ligand_outcome"
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, drug)
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corr_data_lig = df_lig[, cols_to_select]
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dim(corr_data_lig)
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# assign nice colnames (for display)
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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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, "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
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colnames(corr_data_lig)
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#-----------------
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# generate corr LIG plot
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#-----------------
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start = 1
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end = which(colnames(corr_data_lig) == drug); end # should be the last column
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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)
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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 = 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 = T
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, bg = c("#f8766d", "#00bfc4")[unclass(factor(my_corr_lig$ligand_outcome))]
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, pch = 21
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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 = 3
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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(OutPlot2)
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dev.off()
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#######################################################
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library(lattice)
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