From ccc877e8118476eedd4f6cfb62fb0f08bc066c58 Mon Sep 17 00:00:00 2001 From: Tanushree Tunstall Date: Mon, 1 Aug 2022 21:41:02 +0100 Subject: [PATCH] attempting affintiy stuff --- .../plotting/mcsm_mean_affinity_ensemble.R | 189 ++++++++++++++---- .../plotting/mcsm_mean_stability_ensemble.R | 9 +- 2 files changed, 151 insertions(+), 47 deletions(-) diff --git a/scripts/plotting/mcsm_mean_affinity_ensemble.R b/scripts/plotting/mcsm_mean_affinity_ensemble.R index ef6efcc..d21b263 100644 --- a/scripts/plotting/mcsm_mean_affinity_ensemble.R +++ b/scripts/plotting/mcsm_mean_affinity_ensemble.R @@ -59,10 +59,14 @@ length(unique((df3$mutationinformation))) all_colnames = as.data.frame(colnames(df3)) common_cols = c("mutationinformation" , "position" + , "X5uhc_position" + , "X5uhc_offset" , "dst_mode" , "mutation_info_labels" , "sensitivity" - , "ligand_distance") + + , "ligand_distance" + , "interface_dist") all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)] all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)] @@ -122,25 +126,28 @@ outcome_cols_affinity = c( "ligand_outcome" ###################################################################### cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols - , raw_cols - , scaled_cols - , outcome_cols_affinity)] + , raw_cols_affinity + , scaled_cols_affinity + , outcome_cols_affinity + , raw_cols_stability + , scaled_cols_stability + , outcome_cols_stability + )] -# cols_to_extract = cols_to_consider[cols_to_consider%in%c(common_cols -# , outcome_cols_affinity)] +cols_to_extract = cols_to_consider[cols_to_consider%in%c(common_cols + , scaled_cols_affinity)] + +df3_plot = df3[, cols_to_extract] ############################################################## +# FIXME: ADD distance to NA when SP replies + ##################### # Ensemble affinity: affinity_cols +# mcsm_lig, mmcsm_lig and mcsm_na ##################### # extract outcome cols and map numeric values to the categories # Destabilising == 0, and stabilising == 1 so rescaling can let -1 be destabilising - -# df3_plot = df3[, cols_to_extract] -# -# df3_plot[, outcome_cols_affinity] <- sapply(df3_plot[, outcome_cols_affinity] -# , function(x){ifelse(x == "Destabilising", 0, 1)}) - -df3_plot = df3[, c(common_cols, scaled_cols)] +######################################### #===================================== # Affintiy (2 cols): average the scores # across predictors ==> average by @@ -148,20 +155,61 @@ df3_plot = df3[, c(common_cols, scaled_cols)] # column to average: ens_affinity #===================================== -cols_to_average_affinity = which(colnames(df3_plot)%in%outcome_cols_affinity) -cols_to_average_affinity +cols_mcsm_lig = c("mutationinformation" + , "position" + , "sensitivity" + , "X5uhc_position" + , "X5uhc_offset" + , "ligand_distance" + , "ligand_outcome" + , "mmcsm_lig_outcome") +cols_mcsm_lig +df3_lig_ens = df3[, cols_mcsm_lig] + +cols_to_numeric = c("ligand_outcome","mmcsm_lig_outcome") +df3_lig_ens[, cols_to_numeric] <- sapply(df3_lig_ens[, cols_to_numeric] + , function(x){ifelse(x == "Destabilising", 0, 1)}) + +cols_to_average_lig = which(colnames(df3_lig_ens)%in%cols_to_numeric) +cols_to_average_lig # ensemble average across predictors -df3_plot_affinity$ens_affinity = rowMeans(df3_plot_affinity[,cols_to_average_affinity]) +df3_lig_ens$ens_lig = rowMeans(df3_lig_ens[,cols_to_average_lig]) -head(df3_plot_affinity$position); head(df3_plot_affinity$mutationinformation) -head(df3_plot_affinity$ens_affinity) -table(df3_plot_affinity$ens_affinity) +head(df3_lig_ens$position); head(df3_lig_ens$mutationinformation) +head(df3_lig_ens$ens_lig) +table(df3_lig_ens$ens_lig) +#=============================== +# Filter ligand distance <10 +# from globals else uncomment +#LigDist_cutoff = 10 +#LigDist_colname = "ligand_distance" +#=============================== +table(df3_lig_ens[LigDist_colname]% +mean_ens_lig_by_position <- df3_lig_ens_filtered %>% dplyr::group_by(position) %>% - dplyr::summarize(avg_ens_affinity = mean(ens_affinity)) + dplyr::summarize(avg_ens_lig = mean(ens_lig)) + +class(mean_ens_lig_by_position) + +# convert to a df +mean_ens_lig_by_position = as.data.frame(mean_ens_lig_by_position) + +table(mean_ens_lig_by_position$avg_ens_lig) # REscale b/w -1 and 1 #en_aff_min = min(mean_ens_affinity_by_position['ens_affinity']) @@ -171,38 +219,91 @@ mean_ens_affinity_by_position <- df3_plot_affinity %>% # mean_ens_affinity_by_position['avg_ens_affinity_scaled'] = lapply(mean_ens_affinity_by_position['avg_ens_affinity'] # , function(x) ifelse(x < 0, x/abs(en_aff_min), x/en_aff_max)) - -mean_ens_affinity_by_position['avg_ens_affinity_scaled'] = lapply(mean_ens_affinity_by_position['avg_ens_affinity'] +mean_ens_lig_by_position['avg_ens_lig_scaled'] = lapply(mean_ens_lig_by_position['avg_ens_lig'] , function(x) { scales::rescale(x, to = c(-1,1) #, from = c(en_aff_min,en_aff_max)) , from = c(0,1)) }) -cat(paste0('Average affintiy scores:\n' - , head(mean_ens_affinity_by_position['avg_ens_affinity']) + +cat(paste0('Average (mcsm-lig+mmcsm-lig) scores:\n' + , head(mean_ens_lig_by_position['avg_ens_lig']) , '\n---------------------------------------------------------------' - , '\nAverage affintiy scaled scores:\n' - , head(mean_ens_affinity_by_position['avg_ens_affinity_scaled']))) + , '\nAverage (mcsm-lig+mmcsm-lig) scaled scores:\n' + , head(mean_ens_lig_by_position['avg_ens_lig_scaled']))) + +if ( nrow(mean_ens_lig_by_position) == expected_npos ){ + cat("\nPASS: Generated ensemble average values for ligand affinity" ) +}else{ + stop(paste0("\nAbort: length mismatch for ligand affinity data")) +} + +################################################################# + +#===================================== +# Affintiy (mCSM-ppi2): +#D1148G for rpob DOES NOT EXIST for 5UHC +#===================================== +cols_mcsm_ppi2 = c("mutationinformation" + , "position" + , "X5uhc_position" + , "X5uhc_offset" + , "sensitivity" + , "interface_dist" + #, "mcsm_ppi2_affinity" + #, "mcsm_ppi2_scaled" + , "mcsm_ppi2_outcome" + ) +cols_mcsm_ppi2 +df3_ppi2_raw = df3[, c(cols_mcsm_ppi2, "mcsm_ppi2_affinity", "mcsm_ppi2_scaled") ] + +table(df3_ppi2_raw$mcsm_ppi2_outcome) + +df3_ppi2 = df3[, cols_mcsm_ppi2] + +cols_to_numeric_ppi2 = c("mcsm_ppi2_outcome") +df3_ppi2[, cols_to_numeric_ppi2] <- sapply(df3_ppi2[, cols_to_numeric_ppi2] + , function(x){ifelse(x == "Descreasing", 0, 1)}) + +cols_to_average_ppi2 = which(colnames(df3_ppi2)%in%cols_to_numeric_ppi2) +cols_to_average_ppi2 + +#=============================== +# Filter interface <10 +Dist_cutoff = 10 +ppi2Dist_colname = "interface_dist" +#=============================== +table(df3_ppi2[ppi2Dist_colname]