added corr data to get_plotting_dfs.R and generate corr plots
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4 changed files with 252 additions and 19 deletions
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@ -231,6 +231,221 @@ str(wide_df_or_mult)
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position_or_mult = as.numeric(colnames(wide_df_or_mult))
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####################################################################
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# Data for Corrplots
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####################################################################
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cat("\n=========================================="
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, "\nCORR PLOTS data: PS"
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, "\n===========================================")
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df_ps = merged_df2
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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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#df_ps$mutation_info_labels = ifelse(df_ps$mutation_info == dr_muts_col, 1, 0)
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#----------------------------
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# columns for corr plots:PS
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#----------------------------
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# subset data to generate pairwise correlations
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cols_to_select = c("mutationinformation"
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, "duet_scaled"
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, "foldx_scaled"
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#, "mutation_info_labels"
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, "asa"
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, "rsa"
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, "rd_values"
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, "kd_values"
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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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##, "af_kin"
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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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#--------------------------------------
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# assign nice colnames (for display)
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#--------------------------------------
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my_corr_colnames = c("Mutation"
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, "DUET"
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, "Foldx"
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#, "Mutation class"
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, "ASA"
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, "RSA"
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, "RD"
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, "KD"
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, "Log (OR)"
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, "-Log (P)"
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##, "Adjusted (OR)"
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##, "-Log (P wald)"
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, "MAF"
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##, "AF_kin"
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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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#===========================
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# Corr data for plots: PS
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# big_df ps: ~ merged_df2
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#===========================
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#corr_ps_df2 = corr_data_ps[start:(end-offset)] # without drug
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corr_ps_df2 = corr_data_ps[start:end]
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head(corr_ps_df2)
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#===========================
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# Corr data for plots: PS
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# short_df ps: ~merged_df3
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#===========================
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corr_ps_df3 = corr_ps_df2[!duplicated(corr_ps_df2$Mutation),]
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na_or = sum(is.na(corr_ps_df3$`Log (OR)`))
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check1 = nrow(corr_ps_df3) - na_or
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##na_adj_or = sum(is.na(corr_ps_df3$`adjusted (OR)`))
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##check2 = nrow(corr_ps_df3) - na_adj_or
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if (nrow(corr_ps_df3) == nrow(merged_df3) && nrow(merged_df3_comp) == check1) {
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cat( "\nPASS: No. of rows for corr_ps_df3 match"
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, "\nPASS: No. of OR values checked: " , check1)
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} else {
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cat("\nFAIL: Numbers mismatch:"
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, "\nExpected nrows: ", nrow(merged_df3)
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, "\nGot: ", nrow(corr_ps_df3)
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, "\nExpected OR values: ", nrow(merged_df3_comp)
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, "\nGot: ", check1)
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}
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#=================================
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# Data for Correlation plots: LIG
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#=================================
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cat("\n=========================================="
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, "\nCORR PLOTS data: PS"
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, "\n===========================================")
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df_lig = merged_df2_lig
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table(df_lig$ligand_outcome)
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#--------------------
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# adding log cols
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#--------------------
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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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#----------------------------
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# columns for corr plots:PS
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#----------------------------
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# subset data to generate pairwise correlations
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cols_to_select = c("mutationinformation"
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, "affinity_scaled"
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#, "mutation_info_labels"
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, "asa"
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, "rsa"
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, "rd_values"
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, "kd_values"
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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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##, "af_kin"
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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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#--------------------------------------
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# assign nice colnames (for display)
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#--------------------------------------
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my_corr_colnames = c("Mutation"
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, "Ligand Affinity"
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#, "Mutation class"
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, "ASA"
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, "RSA"
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, "RD"
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, "KD"
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, "Log (OR)"
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, "-Log (P)"
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##, "Adjusted (OR)"
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##, "-Log (P wald)"
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, "MAF"
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##, "MAF_kin"
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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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#=============================
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# Corr data for plots: LIG
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# big_df lig: ~ merged_df2_lig
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#==============================
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#corr_lig_df2 = corr_data_lig[start:(end-offset)] # without drug
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corr_lig_df2 = corr_data_lig[start:end]
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head(corr_lig_df2)
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#=============================
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# Corr data for plots: LIG
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# short_df lig: ~ merged_df3_lig
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#==============================
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corr_lig_df3 = corr_lig_df2[!duplicated(corr_lig_df2$Mutation),]
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na_or_lig = sum(is.na(corr_lig_df3$`Log (OR)`))
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check1_lig = nrow(corr_lig_df3) - na_or_lig
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if (nrow(corr_lig_df3) == nrow(merged_df3_lig) && nrow(merged_df3_comp_lig) == check1_lig) {
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cat( "\nPASS: No. of rows for corr_lig_df3 match"
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, "\nPASS: No. of OR values checked: " , check1_lig)
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} else {
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cat("\nFAIL: Numbers mismatch:"
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, "\nExpected nrows: ", nrow(merged_df3_lig)
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, "\nGot: ", nrow(corr_ps_df3_lig)
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, "\nExpected OR values: ", nrow(merged_df3_comp_lig)
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, "\nGot: ", check1_lig)
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}
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# remove unnecessary columns
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identical(corr_data_lig, corr_lig_df2)
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identical(corr_data_ps, corr_ps_df2)
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rm(df_ps, df_lig, corr_data_ps, corr_data_lig)
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########################################################################
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# End of script
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########################################################################
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########################################################################
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rm(foo)
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