251 lines
No EOL
7.1 KiB
R
251 lines
No EOL
7.1 KiB
R
#!/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("../functions/combining_dfs_plotting.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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# 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
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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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################################
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# Data for Correlation plots: PS
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#################################
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#======================
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# adding log cols
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#======================
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df_ps$log10_or_mychisq = log10(df_ps$or_mychisq)
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df_ps$neglog_pval_fisher = -log10(df_ps$pval_fisher)
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df_ps$log10_or_kin = log10(df_ps$or_kin)
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df_ps$neglog_pwald_kin = -log10(df_ps$pwald_kin)
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#===============================
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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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# 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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, "MAF"
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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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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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#---------------------------------------
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# generate corr PS plot: both panels
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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")$p
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################################################################################################
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###################################
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# Data for Correlation plots: LIG
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##################################
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table(df_lig$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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, "MAF"
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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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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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#---------------------------------------
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# generate corr LIG plot: both panels
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#---------------------------------------
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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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# uncomment as necessary
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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$ligand_outcome))]
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, pch = 21 # for bg
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, jitter = T
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, cex = 2
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, cex.axis = 2
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, cex.labels = 4
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, cex.cor = 1
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, smooth = F
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)
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dev.off()
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corr_lig_rho = corr.test(my_corr_lig[1:4], method = "spearman")$r
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corr_lig_p = corr.test(my_corr_lig[1:4], method = "spearman")$p
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####################################################### |