moved old corr files
This commit is contained in:
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dab8294a01
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6 changed files with 10 additions and 1392 deletions
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@ -1,330 +0,0 @@
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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("~/git/LSHTM_analysis/scripts/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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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_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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########################################################################
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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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# 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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# 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_adjusted)
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svg(plot_corr_ps_adjusted, width = 15, height = 15)
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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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, 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 = 2
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, cex.axis = 1.5
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, cex.labels = 1.5
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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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#, "or_kin"
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#, "neglog_pwald_kin"
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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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#, "OR adjusted"
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#, "-Log(P wald)"
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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_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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, 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 = 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(OutPlot2)
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dev.off()
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#######################################################
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@ -1,242 +0,0 @@
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#!/usr/bin/env Rscript
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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("~/git/LSHTM_analysis/scripts/Header_TT.R")
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spec = matrix(c(
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"drug" , "d", 1, "character",
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"gene" , "g", 1, "character",
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"data_file1" , "fa", 2, "character",
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"data_file2" , "fb", 2, "character"
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), byrow = TRUE, ncol = 4)
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opt = getopt(spec)
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drug = opt$drug
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gene = opt$gene
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infile_params = opt$data_file1
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infile_metadata = opt$data_file2
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if(is.null(drug)|is.null(gene)) {
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stop("Missing arguments: --drug and --gene must both be specified (case-sensitive)")
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}
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#===========
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# Input
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#===========
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source("get_plotting_dfs.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 = "corr_PS.svg"
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plot_corr_ps = paste0(plotdir,"/", corr_ps)
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corr_ps_all = "corr_PS_all.svg"
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plot_corr_ps_all = paste0(plotdir,"/", corr_ps_all)
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# LIG
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corr_lig = "corr_LIG.svg"
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plot_corr_lig = paste0(plotdir,"/", corr_lig)
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corr_lig_all = "corr_LIG_all.svg"
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plot_corr_lig_all = paste0(plotdir,"/", corr_lig_all)
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##############################################################################
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foo = corr_ps_df3
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#foo2 = corr_ps_df2
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bar = corr_lig_df3
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#bar2 = corr_lig_df2
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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"
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, "Foldx"
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, "Log (OR)"
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, "-Log (P)"
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, "MAF"
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, "duet_outcome"
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, drug)
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corr_data_ps = foo[names(foo)%in%cols_to_select]
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length(cols_to_select)
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colnames(corr_data_ps)
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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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#---------------------
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# Corr plot PS: short
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# data: corr_ps_df3
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# cols: 7
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#---------------------
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cat("Corr plot PS DUET with coloured dots:", plot_corr_ps)
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svg(plot_corr_ps, width = 15, height = 15)
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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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, 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$duet_outcome))] # foldx colours are reveresed
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, pch = 21 # for bg
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, jitter = T
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, alpha = 1
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, cex = 1.8
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, cex.axis = 2
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, cex.labels = 4
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, cex.cor = 1
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, smooth = F
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)
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dev.off()
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corr_ps_rho = corr.test(my_corr_ps[1:5], method = "spearman")$r
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corr_ps_p = corr.test(my_corr_ps[1:5], method = "spearman")$p
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#---------------------
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# Corr plot PS: ALL
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# data: corr_ps_df3
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# cols: 10
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#---------------------
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end_ps_all = which(colnames(foo) == drug); end_ps_all # should be the last column
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my_corr_ps_all = foo[start:(end_ps_all - offset)]
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cols_to_drop = "Mutation"
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my_corr_ps_all = my_corr_ps_all[, !(names(my_corr_ps_all)%in%cols_to_drop)]
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head(my_corr_ps_all)
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length(colnames(my_corr_ps_all))
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cat("Corr plot PS DUET with coloured dots:", plot_corr_ps_all)
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svg(plot_corr_ps_all, width = 15, height = 15)
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pairs.panels(my_corr_ps_all[1:(length(my_corr_ps_all)-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_all$duet_outcome))] # foldx colours are reveresed
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, pch = 21 # for bg
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, jitter = T
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, alpha = 1
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, cex = 1.5
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, cex.axis = 2
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, cex.labels = 2.5
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, cex.cor = 1
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, smooth = F
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)
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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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cols_to_select_lig = c("Ligand Affinity"
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, "Log (OR)"
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, "-Log (P)"
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, "MAF"
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, "ligand_outcome"
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, drug)
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corr_data_lig = bar[names(bar)%in%cols_to_select_lig]
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length(cols_to_select_lig)
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colnames(corr_data_lig)
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start_lig = 1
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end_lig = which(colnames(corr_data_lig) == drug); end_lig # should be the last column
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offset_lig = 1
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my_corr_lig = corr_data_lig[start_lig:(end_lig-offset_lig)]
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head(my_corr_lig)
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||||
|
||||
#---------------------
|
||||
# Corr plot LIG: short
|
||||
# data: corr_lig_df3
|
||||
# cols: 7
|
||||
#---------------------
|
||||
cat("Corr LIG plot with coloured dots:", plot_corr_lig)
|
||||
svg(plot_corr_lig, width = 15, height = 15)
|
||||
|
||||
pairs.panels(my_corr_lig[1:(length(my_corr_lig)-1)]
|
||||
, method = "spearman" # correlation method
|
||||
, hist.col = "grey" ##00AFBB
|
||||
, density = TRUE # show density plots
|
||||
, ellipses = F # show correlation ellipses
|
||||
, stars = T
|
||||
, rug = F
|
||||
, breaks = "Sturges"
|
||||
, show.points = T
|
||||
, bg = c("#f8766d", "#00bfc4")[unclass(factor(my_corr_lig$ligand_outcome))]
|
||||
, pch = 21 # for bg
|
||||
, jitter = T
|
||||
, cex = 2
|
||||
, cex.axis = 2
|
||||
, cex.labels = 4
|
||||
, cex.cor = 1
|
||||
, smooth = F
|
||||
)
|
||||
|
||||
dev.off()
|
||||
|
||||
corr_lig_rho = corr.test(my_corr_lig[1:4], method = "spearman")$r
|
||||
corr_lig_p = corr.test(my_corr_lig[1:4], method = "spearman")$p
|
||||
|
||||
#---------------------
|
||||
# Corr plot LIG: ALL
|
||||
# data: corr_lig_df3
|
||||
# cols: 9
|
||||
#---------------------
|
||||
end_lig_all = which(colnames(bar) == drug); end_lig_all # should be the last column
|
||||
|
||||
my_corr_lig_all = bar[start_lig:(end_lig_all - offset_lig)]
|
||||
cols_to_drop = "Mutation"
|
||||
my_corr_lig_all = my_corr_lig_all[, !(names(my_corr_lig_all)%in%cols_to_drop)]
|
||||
head(my_corr_lig_all)
|
||||
length(colnames(my_corr_lig_all))
|
||||
|
||||
cat("Corr plot LIG with coloured dots:", plot_corr_lig_all)
|
||||
svg(plot_corr_lig_all, width = 15, height = 15)
|
||||
|
||||
pairs.panels(my_corr_lig_all[1:(length(my_corr_lig_all)-1)]
|
||||
, method = "spearman" # correlation method
|
||||
, hist.col = "grey" ##00AFBB
|
||||
, density = TRUE # show density plots
|
||||
, ellipses = F # show correlation ellipses
|
||||
, stars = T
|
||||
, rug = F
|
||||
, breaks = "Sturges"
|
||||
, show.points = T
|
||||
, bg = c("#f8766d", "#00bfc4")[unclass(factor(my_corr_lig_all$ligand_outcome))] # foldx colours are reveresed
|
||||
, pch = 21 # for bg
|
||||
, jitter = T
|
||||
, alpha = 1
|
||||
, cex = 1.5
|
||||
, cex.axis = 2
|
||||
, cex.labels = 2.2
|
||||
, cex.cor = 1
|
||||
, smooth = F
|
||||
)
|
||||
dev.off()
|
||||
|
||||
|
||||
######################################################################=
|
||||
# End of script
|
||||
######################################################################=
|
||||
|
|
@ -1,276 +0,0 @@
|
|||
#!/usr/bin/env Rscript
|
||||
source("~/git/LSHTM_analysis/config/gid.R")
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
|
||||
#===================================================================
|
||||
corr_data = corr_data_extract(merged_df3, drug_name = drug)
|
||||
#corr_data = corr_data_extract(merged_df2, drug_name = drug)
|
||||
|
||||
geneL_normal = c("pnca")
|
||||
geneL_na_dy = c("gid")
|
||||
geneL_na = c("rpob")
|
||||
geneL_ppi2 = c("alr", "embb", "katg", "rpob")
|
||||
|
||||
core_cols <- c( "Log (OR)" , "MAF", "-Log (P)"
|
||||
, "DUET", "FoldX"
|
||||
, "DeepDDG", "Dynamut2"
|
||||
, "ASA", "RSA", "RD", "KD"
|
||||
, "Consurf", "SNAP2"
|
||||
#, "mutation_info_labels"
|
||||
)
|
||||
|
||||
|
||||
if (tolower(gene)%in%geneL_normal){
|
||||
corrplot_cols = core_cols
|
||||
}
|
||||
|
||||
if (tolower(gene)%in%geneL_na_dy){
|
||||
additional_cols = c("mCSM-NA"
|
||||
, "Dynamut"
|
||||
, "ENCoM-DDG"
|
||||
, "ENCoM-DDS"
|
||||
, "mCSM"
|
||||
, "SDM"
|
||||
, "DUET-d"
|
||||
, "mutation_info_labels")
|
||||
corrplot_cols = c(core_cols, additional_cols)
|
||||
}
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
additional_cols = c("mCSM-NA"
|
||||
, "mutation_info_labels")
|
||||
corrplot_cols = c(core_cols, additional_cols)
|
||||
|
||||
}
|
||||
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
additional_cols = c("mCSM-PPI2"
|
||||
, "mutation_info_labels")
|
||||
corrplot_cols = c(core_cols, additional_cols)
|
||||
}
|
||||
|
||||
#========================================
|
||||
# corrplot_cols <- c( "Log (OR)"
|
||||
# , "MAF"
|
||||
# , "-Log (P)"
|
||||
# , "DUET"
|
||||
# , "FoldX"
|
||||
# , "DeepDDG"
|
||||
# , "Dynamut2"
|
||||
# , "mCSM-NA"
|
||||
# , "Dynamut"
|
||||
# , "ENCoM-DDG"
|
||||
# , "ENCoM-DDS"
|
||||
# , "mCSM"
|
||||
# , "SDM"
|
||||
# , "DUET-d"
|
||||
# , "ASA"
|
||||
# , "RSA"
|
||||
# , "RD"
|
||||
# , "KD"
|
||||
# , "mutation_info_labels"
|
||||
# )
|
||||
|
||||
corr_df <- corr_data[, corrplot_cols] # col order is according to corrplot_cols
|
||||
head(corr_df); names(corr_df)
|
||||
|
||||
if ( all( corrplot_cols%in%names(corr_df) ) ){
|
||||
cat("\nPASS: Successfully selected"
|
||||
, length(corrplot_cols)
|
||||
, "columns for building correlation df")
|
||||
} else {
|
||||
cat("\nFAIl: Something went wrong, numbers mismatch"
|
||||
, "\nExpected cols:", length(corrplot_cols)
|
||||
, "\nGot:", length(corr_df) )
|
||||
}
|
||||
|
||||
#=====================================================
|
||||
corrplot_df <- corr_df
|
||||
|
||||
# stat_df = corrplot_df[, c("Log (OR)"
|
||||
# , "MAF"
|
||||
# , "-Log (P)")]
|
||||
|
||||
plot_title <- "Correlation plots (stability)"
|
||||
|
||||
# Checkbox Names
|
||||
# FIXME: select columns conditionally based on gene and grey out the ones that are not present!
|
||||
|
||||
cBCorrNames = c( "Odds Ratio"
|
||||
, "Allele Frequency"
|
||||
, "P-value"
|
||||
, "DUET"
|
||||
, "FoldX"
|
||||
, "DeepDDG"
|
||||
, "Dynamut2"
|
||||
, "ASA"
|
||||
, "RSA"
|
||||
, "RD"
|
||||
, "KD"
|
||||
, "Consurf"
|
||||
, "SNAP2"
|
||||
, "Nucleic Acid affinity"
|
||||
, "PPi2 affinity"
|
||||
|
||||
#, "Dynamut"
|
||||
#, "ENCoM-Stability"
|
||||
#, "ENCoM-Flexibility"
|
||||
#, "mCSM"
|
||||
#, "SDM"
|
||||
#, "DUET-d"
|
||||
)
|
||||
|
||||
# Checkbox Values (aka Column Names that are in corrplot_df)
|
||||
cBCorrVals = c("Log (OR)"
|
||||
, "MAF"
|
||||
, "-Log (P)"
|
||||
, "DUET"
|
||||
, "FoldX"
|
||||
, "DeepDDG"
|
||||
, "Dynamut2"
|
||||
, "ASA"
|
||||
, "RSA"
|
||||
, "RD"
|
||||
, "KD"
|
||||
, "Consurf"
|
||||
, "SNAP2"
|
||||
, "mCSM-NA"
|
||||
, "mCSM-PPI2"
|
||||
# , "Dynamut"
|
||||
# , "ENCoM-DDG"
|
||||
# , "ENCoM-DDS"
|
||||
# , "mCSM"
|
||||
# , "SDM"
|
||||
# , "DUET-d"
|
||||
)
|
||||
|
||||
# Pre-selected checkboxes
|
||||
cBCorrSelected = c("Log (OR)"
|
||||
, "MAF"
|
||||
, "-Log (P)")
|
||||
|
||||
#################
|
||||
# Define UI
|
||||
#################
|
||||
u_corr <- fluidPage(
|
||||
|
||||
headerPanel(plot_title),
|
||||
|
||||
sidebarLayout(position = "left"
|
||||
, sidebarPanel(
|
||||
checkboxGroupInput("variable", "Choose parameter:"
|
||||
, choiceNames = cBCorrNames
|
||||
, choiceValues = cBCorrVals
|
||||
, selected = cBCorrSelected
|
||||
)
|
||||
|
||||
# could be a fluid Row
|
||||
, actionButton("add_col" , "Render")
|
||||
, actionButton("reset_graph" , "Reset Graphs")
|
||||
, actionButton("select_all" , "Select All")
|
||||
|
||||
)
|
||||
|
||||
# output/display
|
||||
, mainPanel(plotOutput(outputId = 'corrplot'
|
||||
, height = "1200px"
|
||||
, width = "1500px")
|
||||
# , height = "800px"
|
||||
# , width = "600px")
|
||||
, textOutput("txt")
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
#################
|
||||
# Define server
|
||||
#################
|
||||
s_corr <- shinyServer(function(input, output, session)
|
||||
|
||||
{
|
||||
|
||||
#================
|
||||
# Initial render
|
||||
#================
|
||||
output$corrplot <- renderPlot({
|
||||
|
||||
#---------------------
|
||||
# My correlation plot: initial plot
|
||||
#---------------------
|
||||
c_plot <- my_corr_pairs(corr_data_all = corrplot_df
|
||||
, corr_cols = cBCorrSelected
|
||||
, corr_method = "spearman"
|
||||
, dot_size = 2
|
||||
, ats = 1.5
|
||||
, corr_lab_size = length(cBCorrNames)/length(cBCorrSelected) * 1.3
|
||||
, corr_value_size = 1)
|
||||
})
|
||||
|
||||
#====================
|
||||
# Interactive render
|
||||
#====================
|
||||
observeEvent(
|
||||
input$add_col, {
|
||||
|
||||
# select cols for corrplot
|
||||
corr_cols_s <- c(input$variable)
|
||||
|
||||
# render plot
|
||||
if (length(c(input$variable)) >= 2) {
|
||||
output$corrplot <- renderPlot({
|
||||
|
||||
#---------------------
|
||||
# My correlation plot: user selects columns
|
||||
#---------------------
|
||||
c_plot <- my_corr_pairs(corr_data_all = corrplot_df
|
||||
, corr_cols = corr_cols_s
|
||||
, dot_size = 2
|
||||
, ats = 1.5
|
||||
, corr_lab_size = length(cBCorrNames)/length(corr_cols_s) * 1.3
|
||||
, corr_value_size = 1)
|
||||
|
||||
})
|
||||
} else{ output$txt = renderText({"Argh, common! It's a correlation plot. Select >=2 vars!"})
|
||||
|
||||
}
|
||||
|
||||
})
|
||||
|
||||
#==================================
|
||||
# Add button: Select All checkbox
|
||||
#==================================
|
||||
observeEvent(
|
||||
input$select_all,{
|
||||
|
||||
updateCheckboxGroupInput(session, "variable", selected = cBCorrVals)
|
||||
}
|
||||
)
|
||||
|
||||
#================
|
||||
# Reset render
|
||||
#================
|
||||
observeEvent(
|
||||
input$reset_graph,{
|
||||
|
||||
# reset checkboxes to default selection
|
||||
updateCheckboxGroupInput(session, "variable", selected = cBCorrSelected)
|
||||
|
||||
|
||||
# render plot
|
||||
output$corrplot <- renderPlot({
|
||||
|
||||
#---------------------
|
||||
# My correlation plot: reset plot
|
||||
#---------------------
|
||||
c_plot <- my_corr_pairs(corr_data_all = corrplot_df
|
||||
, corr_cols = cBCorrSelected
|
||||
, dot_size = 1.2
|
||||
, ats = 1.5
|
||||
, corr_lab_size = length(cBCorrNames)/length(cBCorrSelected) * 1.3
|
||||
, corr_value_size = 1)
|
||||
})
|
||||
}
|
||||
)
|
||||
}
|
||||
)
|
||||
|
||||
shinyApp(ui = u_corr, server = s_corr)
|
||||
|
|
@ -1,220 +0,0 @@
|
|||
#!/usr/bin/env Rscript
|
||||
|
||||
source("~/git/LSHTM_analysis/config/gid.R")
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
|
||||
#===================================================================
|
||||
corr_data = corr_data_extract(merged_df3, drug_name = drug)
|
||||
#corr_data = corr_data_extract(merged_df2, drug_name = drug)
|
||||
#================================================================
|
||||
#other globals
|
||||
dist_colname <- LigDist_colname # ligand_distance (from globals)
|
||||
dist_cutoff <- LigDist_cutoff # 10 (from globals)
|
||||
|
||||
cat("\nLigand distance cut off, colname:", dist_colname
|
||||
, "\nThe max distance", gene, "structure df" , ":", max_ang, "\u212b"
|
||||
, "\nThe min distance", gene, "structure df" , ":", min_ang, "\u212b")
|
||||
|
||||
########################################################################
|
||||
|
||||
#==========================================
|
||||
#####################
|
||||
# Correlation plot
|
||||
#####################
|
||||
colnames(corr_df_m3_f)
|
||||
|
||||
corrplot_cols_lig <- c( "Log (OR)"
|
||||
, "MAF"
|
||||
, "-Log (P)"
|
||||
, "mCSM-lig"
|
||||
, "mCSM-NA"
|
||||
, "ASA"
|
||||
, "RSA"
|
||||
, "RD"
|
||||
, "KD"
|
||||
, dist_colname
|
||||
, "mutation_info_labels"
|
||||
)
|
||||
|
||||
corr_df_lig <- corr_df_m3_f[, corrplot_cols_lig]
|
||||
head(corr_df_lig)
|
||||
|
||||
corrplot_df_lig <- corr_df_lig
|
||||
|
||||
# static df
|
||||
# stat_df = corrplot_df_lig[, c("Log (OR)"
|
||||
# , "MAF"
|
||||
# , "-Log (P)"
|
||||
# )]
|
||||
|
||||
plot_title_lig <- "Correlation plots (ligand affinity)"
|
||||
|
||||
# Checkbox Names
|
||||
cCorrNames = c( "Odds Ratio"
|
||||
, "Allele Frequency"
|
||||
, "P-value"
|
||||
, "Ligand affinity"
|
||||
, "Nucleic Acid affinity"
|
||||
, "ASA"
|
||||
, "RSA"
|
||||
, "RD"
|
||||
, "KD"
|
||||
, "Ligand Distance")
|
||||
|
||||
# Checkbox Values (aka Column Names that are in corrplot_df_lig)
|
||||
cCorrVals = c("Log (OR)"
|
||||
, "MAF"
|
||||
, "-Log (P)"
|
||||
, "mCSM-lig"
|
||||
, "mCSM-NA"
|
||||
, "ASA"
|
||||
, "RSA"
|
||||
, "RD"
|
||||
, "KD"
|
||||
, dist_colname)
|
||||
|
||||
# Pre-selected checkboxes
|
||||
cCorrSelected = c("Log (OR)"
|
||||
, "MAF"
|
||||
, "-Log (P)")
|
||||
#============
|
||||
# Define UI
|
||||
#============
|
||||
u_corr_lig<- fluidPage(
|
||||
headerPanel(plot_title_lig),
|
||||
sidebarLayout(position = "left"
|
||||
, sidebarPanel("Correlations: Filtered data data"
|
||||
, numericInput(inputId = "lig_dist"
|
||||
, label = "Ligand distance cutoff"
|
||||
, value = dist_cutoff # 10 default from globals
|
||||
, min = min_ang
|
||||
, max = max_ang)
|
||||
, checkboxGroupInput("variable", "Choose parameter:"
|
||||
, choiceNames = cCorrNames
|
||||
, choiceValues = cCorrVals
|
||||
, selected = cCorrSelected
|
||||
)
|
||||
# could be a fluid Row
|
||||
, actionButton("add_col" , "Render")
|
||||
, actionButton("reset_graph" , "Reset Graphs")
|
||||
, actionButton("select_all" , "Select All")
|
||||
|
||||
)
|
||||
|
||||
# output/display
|
||||
, mainPanel(plotOutput(outputId = 'corrplot'
|
||||
, height = "1000px"
|
||||
, width = "1200px")
|
||||
# , height = "800px"
|
||||
# , width = "600px")
|
||||
, textOutput("txt")
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
#===============
|
||||
# Define server
|
||||
#===============
|
||||
s_corr_lig <- shinyServer(function(input, output, session)
|
||||
|
||||
{
|
||||
|
||||
#================
|
||||
# Initial render
|
||||
#================
|
||||
output$corrplot <- renderPlot({
|
||||
|
||||
# get the user-specified lig_list
|
||||
dist_cutoff_ini = input$lig_dist
|
||||
|
||||
# subset data for plot
|
||||
corrplot_df_lig_ini = corrplot_df_lig[corrplot_df_lig[[dist_colname]] < dist_cutoff_ini,]
|
||||
|
||||
#---------------------
|
||||
# My correlation plot: initial plot
|
||||
#---------------------
|
||||
c_plot <- my_corr_pairs(
|
||||
#corr_data_all = corrplot_df_lig
|
||||
corr_data_all = corrplot_df_lig_ini
|
||||
, corr_cols = cCorrSelected
|
||||
, dot_size = 2
|
||||
, ats = 1.5
|
||||
, corr_lab_size = length(cCorrNames)/length(cCorrSelected) * 1.3
|
||||
, corr_value_size = 1)
|
||||
|
||||
})
|
||||
|
||||
#====================
|
||||
# Interactive render
|
||||
#====================
|
||||
observeEvent(
|
||||
input$add_col, {
|
||||
|
||||
# get the user-specified lig_list
|
||||
dist_cutoff_user = input$lig_dist
|
||||
|
||||
# subset data for plot
|
||||
corrplot_df_lig_s = corrplot_df_lig[corrplot_df_lig[[dist_colname]] < dist_cutoff_user,]
|
||||
|
||||
# select cols for corrplot
|
||||
corr_cols_s = c(input$variable)
|
||||
|
||||
# render plot
|
||||
if (length(c(input$variable)) >= 2) {
|
||||
|
||||
output$corrplot <- renderPlot({
|
||||
|
||||
#---------------------
|
||||
# My correlation plot: user selects columns
|
||||
#---------------------
|
||||
c_plot <- my_corr_pairs(corr_data_all = corrplot_df_lig_s
|
||||
, corr_cols = corr_cols_s
|
||||
, dot_size = 1.6
|
||||
, ats = 1.5
|
||||
, corr_lab_size = length(cCorrNames)/length(corr_cols_s) * 1.3
|
||||
, corr_value_size = 1)
|
||||
})
|
||||
} else { output$txt = renderText({"Fuddu! It's a correlation plot. Select >=2 vars bewakoof!"})}
|
||||
|
||||
})
|
||||
|
||||
#==================================
|
||||
# Add button: Select All checkbox
|
||||
#==================================
|
||||
observeEvent(
|
||||
input$select_all,{
|
||||
|
||||
updateCheckboxGroupInput(session, "variable", selected = cCorrVals)
|
||||
}
|
||||
)
|
||||
|
||||
#================
|
||||
# Reset render
|
||||
#================
|
||||
observeEvent(
|
||||
input$reset_graph,{
|
||||
|
||||
# reset checkboxes
|
||||
updateCheckboxGroupInput(session, "variable", selected = cCorrSelected)
|
||||
|
||||
# render plot
|
||||
output$corrplot <- renderPlot({
|
||||
|
||||
#---------------------
|
||||
# My correlation plot: reset plot
|
||||
#---------------------
|
||||
c_plot <- my_corr_pairs(corr_data_all = corrplot_df_lig
|
||||
, corr_cols = cCorrSelected
|
||||
, dot_size = 2
|
||||
, ats = 1.5
|
||||
, corr_lab_size = length(cCorrNames)/length(cCorrSelected) * 1.3
|
||||
, corr_value_size = 1)
|
||||
|
||||
})
|
||||
}
|
||||
)
|
||||
}
|
||||
)
|
||||
|
||||
shinyApp(ui = u_corr_lig, server = s_corr_lig)
|
||||
|
||||
|
|
@ -1,323 +0,0 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#########################################################
|
||||
# TASK: Corr plots for PS and Lig
|
||||
|
||||
# Output: 1 svg
|
||||
|
||||
#=======================================================================
|
||||
# working dir and loading libraries
|
||||
getwd()
|
||||
setwd("~/git/LSHTM_analysis/scripts/plotting/")
|
||||
getwd()
|
||||
|
||||
source("~/git/LSHTM_analysis/scripts/Header_TT.R")
|
||||
require(cowplot)
|
||||
source("combining_dfs_plotting.R")
|
||||
#source("my_pairs_panel.R")
|
||||
# should return the following dfs, directories and variables
|
||||
|
||||
# FIXME: Can't output from here
|
||||
|
||||
# PS combined:
|
||||
# 1) merged_df2
|
||||
# 2) merged_df2_comp
|
||||
# 3) merged_df3
|
||||
# 4) merged_df3_comp
|
||||
|
||||
# LIG combined:
|
||||
# 5) merged_df2_lig
|
||||
# 6) merged_df2_comp_lig
|
||||
# 7) merged_df3_lig
|
||||
# 8) merged_df3_comp_lig
|
||||
|
||||
# 9) my_df_u
|
||||
# 10) my_df_u_lig
|
||||
|
||||
cat(paste0("Directories imported:"
|
||||
, "\ndatadir:", datadir
|
||||
, "\nindir:", indir
|
||||
, "\noutdir:", outdir
|
||||
, "\nplotdir:", plotdir))
|
||||
|
||||
cat(paste0("Variables imported:"
|
||||
, "\ndrug:", drug
|
||||
, "\ngene:", gene
|
||||
, "\ngene_match:", gene_match
|
||||
, "\nAngstrom symbol:", angstroms_symbol
|
||||
, "\nNo. of duplicated muts:", dup_muts_nu
|
||||
, "\nNA count for ORs:", na_count
|
||||
, "\nNA count in df2:", na_count_df2
|
||||
, "\nNA count in df3:", na_count_df3))
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
# can't combine by cowplot because not ggplots
|
||||
#corr_plot_combined = "corr_combined.svg"
|
||||
#plot_corr_plot_combined = paste0(plotdir,"/", corr_plot_combined)
|
||||
|
||||
# PS
|
||||
#ggcorr_all_ps = "ggcorr_all_PS.svg"
|
||||
ggcorr_all_ps = "ggcorr_all_PS.png"
|
||||
plot_ggcorr_all_ps = paste0(plotdir,"/", ggcorr_all_ps)
|
||||
|
||||
# LIG
|
||||
#ggcorr_all_lig = "ggcorr_all_LIG.svg"
|
||||
ggcorr_all_lig = "ggcorr_all_LIG.png"
|
||||
plot_ggcorr_all_lig = paste0(plotdir,"/", ggcorr_all_lig )
|
||||
|
||||
# combined
|
||||
ggcorr_all_combined_labelled = "ggcorr_all_combined_labelled.png"
|
||||
plot_ggcorr_all_combined_labelled = paste0(plotdir,"/", ggcorr_all_combined_labelled)
|
||||
|
||||
####################################################################
|
||||
# end of loading libraries and functions #
|
||||
########################################################################
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#df_ps = merged_df3_comp
|
||||
#df_lig = merged_df3_comp_lig
|
||||
merged_df3 = as.data.frame(merged_df3)
|
||||
df_ps = merged_df3
|
||||
df_lig = merged_df3_lig
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
rm( merged_df2, merged_df2_comp, merged_df2_lig, merged_df2_comp_lig, my_df_u, my_df_u_lig)
|
||||
|
||||
########################################################################
|
||||
# end of data extraction and cleaning for plots #
|
||||
########################################################################
|
||||
|
||||
|
||||
#======================
|
||||
# adding log cols
|
||||
#======================
|
||||
# subset data to generate pairwise correlations
|
||||
cols_to_select = c("duet_scaled"
|
||||
|
||||
, "foldx_scaled"
|
||||
|
||||
, "log10_or_mychisq"
|
||||
, "neglog_pval_fisher"
|
||||
|
||||
#, "or_kin"
|
||||
#, "neglog_pwald_kin"
|
||||
|
||||
, "af"
|
||||
|
||||
, "asa"
|
||||
, "rsa"
|
||||
, "kd_values"
|
||||
, "rd_values"
|
||||
|
||||
, "duet_outcome"
|
||||
, drug)
|
||||
|
||||
corr_data_ps = df_ps[, cols_to_select]
|
||||
|
||||
dim(corr_data_ps)
|
||||
|
||||
#p_italic = substitute(paste("-Log(", italic('P'), ")"));p_italic
|
||||
#p_adjusted_italic = substitute(paste("-Log(", italic('P adjusted'), ")"));p_adjusted_italic
|
||||
|
||||
# assign nice colnames (for display)
|
||||
my_corr_colnames = c("DUET"
|
||||
|
||||
, "Foldx"
|
||||
|
||||
, "Log (OR)"
|
||||
, "-Log (P)"
|
||||
|
||||
#, "OR (adjusted)"
|
||||
#, "-Log (P wald)"
|
||||
|
||||
, "AF"
|
||||
|
||||
, "ASA"
|
||||
, "RSA"
|
||||
, "KD"
|
||||
, "RD"
|
||||
|
||||
, "duet_outcome"
|
||||
, drug)
|
||||
|
||||
length(my_corr_colnames)
|
||||
|
||||
colnames(corr_data_ps)
|
||||
colnames(corr_data_ps) <- my_corr_colnames
|
||||
colnames(corr_data_ps)
|
||||
|
||||
#------------------------
|
||||
# Data for ggcorr PS plot
|
||||
#------------------------
|
||||
start = 1
|
||||
end_ggcorr = which(colnames(corr_data_ps) == "duet_outcome"); end_ggcorr # should be the last column
|
||||
offset = 1
|
||||
|
||||
my_ggcorr_ps = corr_data_ps[start:(end_ggcorr-1)]
|
||||
head(my_ggcorr_ps)
|
||||
|
||||
# correlation matrix
|
||||
corr1 <- round(cor(my_ggcorr_ps, method = "spearman", use = "pairwise.complete.obs"), 1)
|
||||
|
||||
# p-value matrix
|
||||
pmat1 <- cor_pmat(my_ggcorr_ps, method = "spearman", use = "pairwise.complete.obs"
|
||||
, conf.level = 0.99)
|
||||
|
||||
corr2 = psych::corr.test(my_ggcorr_ps
|
||||
, method = "spearman"
|
||||
, use = "pairwise.complete.obs")$r
|
||||
corr2 = round(corr2, 1)
|
||||
|
||||
pmat2 = psych::corr.test(my_ggcorr_ps
|
||||
, method = "spearman"
|
||||
, adjust = "none"
|
||||
, use = "pairwise.complete.obs")$p
|
||||
|
||||
corr1== corr2
|
||||
pmat1==pmat2
|
||||
|
||||
#------------------------
|
||||
# Generate ggcorr PS plot
|
||||
#------------------------
|
||||
cat("ggCorr plot PS:", plot_ggcorr_all_ps)
|
||||
#png(filename = plot_ggcorr_all_ps, width = 1024, height = 768, units = "px", pointsize = 20)
|
||||
ggcorr_ps = ggcorrplot(corr1
|
||||
, p.mat = pmat1
|
||||
, hc.order = TRUE
|
||||
, outline.col = "black"
|
||||
, ggtheme = ggplot2::theme_gray
|
||||
, colors = c("#6D9EC1", "white", "#E46726")
|
||||
, title = "DUET and Foldx stability")
|
||||
|
||||
|
||||
ggcorr_ps
|
||||
#dev.off()
|
||||
|
||||
#===========================
|
||||
# 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$pwald_kin)
|
||||
|
||||
# subset data to generate pairwise correlations
|
||||
cols_to_select_lig = c("affinity_scaled"
|
||||
|
||||
, "log10_or_mychisq"
|
||||
, "neglog_pval_fisher"
|
||||
|
||||
, "or_kin"
|
||||
, "neglog_pwald_kin"
|
||||
|
||||
, "af"
|
||||
|
||||
, "asa"
|
||||
, "rsa"
|
||||
, "kd_values"
|
||||
, "rd_values"
|
||||
|
||||
, "ligand_outcome"
|
||||
, drug)
|
||||
|
||||
corr_data_lig = df_lig[, cols_to_select_lig]
|
||||
|
||||
dim(corr_data_lig)
|
||||
|
||||
# assign nice colnames (for display)
|
||||
my_corr_colnames_lig = c("Ligand Affinity"
|
||||
|
||||
, "Log (OR)"
|
||||
, "-Log (P)"
|
||||
|
||||
, "OR (adjusted)"
|
||||
, "-Log(P wald)"
|
||||
|
||||
, "AF"
|
||||
|
||||
, "ASA"
|
||||
, "RSA"
|
||||
, "KD"
|
||||
, "RD"
|
||||
|
||||
, "ligand_outcome"
|
||||
, drug)
|
||||
|
||||
length(my_corr_colnames)
|
||||
|
||||
colnames(corr_data_lig)
|
||||
colnames(corr_data_lig) <- my_corr_colnames_lig
|
||||
colnames(corr_data_lig)
|
||||
|
||||
#------------------------
|
||||
# Data for ggcorr LIG plot
|
||||
#------------------------
|
||||
|
||||
start = 1
|
||||
end_ggcorr_lig = which(colnames(corr_data_lig) == "ligand_outcome"); end_ggcorr_lig # should be the last column
|
||||
offset = 1
|
||||
|
||||
my_ggcorr_lig = corr_data_lig[start:(end_ggcorr_lig-1)]
|
||||
head(my_ggcorr_lig); str(my_ggcorr_lig)
|
||||
|
||||
# correlation matrix
|
||||
corr1_lig <- round(cor(my_ggcorr_lig, method = "spearman", use = "pairwise.complete.obs"), 1)
|
||||
|
||||
# p-value matrix
|
||||
pmat1_lig <- cor_pmat(my_ggcorr_lig, method = "spearman", use = "pairwise.complete.obs")
|
||||
|
||||
corr2_lig = psych::corr.test(my_ggcorr_lig
|
||||
, method = "spearman"
|
||||
, use = "pairwise.complete.obs")$r
|
||||
|
||||
corr2_lig = round(corr2_lig, 1)
|
||||
|
||||
pmat2_lig = psych::corr.test(my_ggcorr_lig
|
||||
, method = "spearman"
|
||||
, adjust = "none"
|
||||
, use = "pairwise.complete.obs")$p
|
||||
|
||||
corr1_lig == corr2_lig
|
||||
pmat1_lig == pmat2_lig
|
||||
|
||||
|
||||
# for display order columns by hc order of ps
|
||||
|
||||
#col_order = levels(ggcorr_ps$data[2])
|
||||
|
||||
#col_order <- c("Species", "Petal.Width", "Sepal.Length",
|
||||
#"Sepal.Width", "Petal.Length")
|
||||
#my_data2 <- my_data[, col_order]
|
||||
#my_data2
|
||||
|
||||
#------------------------
|
||||
# Generate ggcorr LIG plot
|
||||
#------------------------
|
||||
cat("ggCorr LIG plot:", plot_ggcorr_all_lig)
|
||||
#svg(plot_ggcorr_all_lig, width = 15, height = 15)
|
||||
#png(plot_ggcorr_all_lig, width = 1024, height = 768, units = "px", pointsize = 20)
|
||||
|
||||
ggcorr_lig = ggcorrplot(corr1_lig
|
||||
, p.mat = pmat1_lig
|
||||
, hc.order = TRUE
|
||||
, outline.col = "black"
|
||||
|
||||
, ggtheme = ggplot2::theme_gray
|
||||
, colors = c("#6D9EC1", "white", "#E46726")
|
||||
, title = "Ligand affinty")
|
||||
|
||||
|
||||
ggcorr_lig
|
||||
#dev.off()
|
||||
|
||||
#######################################################
|
||||
#=============================
|
||||
# combine plots for output
|
||||
#=============================
|
||||
+
|
||||
|
|
@ -88,7 +88,7 @@ all_cols = c(common_cols
|
|||
#=======
|
||||
# output
|
||||
#=======
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
|
||||
####################################
|
||||
# merged_df3: NECESSARY pre-processing
|
||||
|
|
@ -228,6 +228,15 @@ corr_lig_colnames = c("mCSM-lig"
|
|||
, drug)
|
||||
|
||||
corr_ppi2_colnames = c("mCSM-PPI2"
|
||||
, "SNAP2"
|
||||
, "Log (OR)"
|
||||
, "-Log (P)"
|
||||
, "interface_dist"
|
||||
, "dst_mode"
|
||||
, drug)
|
||||
|
||||
|
||||
corr_conservation = c("Consurf"
|
||||
, "MAF"
|
||||
, "Log (OR)"
|
||||
, "-Log (P)"
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue