separted cols
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2 changed files with 129 additions and 156 deletions
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@ -60,145 +60,87 @@ all_colnames = as.data.frame(colnames(df3))
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common_cols = c("mutationinformation"
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, "position"
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, "dst_mode"
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#, "mutation_info_labels"
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, "mutation_info_labels"
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, "sensitivity"
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, "ligand_distance")
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all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)]
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scaled_cols = c("duet_scaled" , "duet_stability_change"
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, "deepddg_scaled" , "deepddg"
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, "ddg_dynamut2_scaled" , "ddg_dynamut2"
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, "foldx_scaled" , "ddg_foldx"
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, "affinity_scaled" , "ligand_affinity_change"
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, "mmcsm_lig_scaled" , "mmcsm_lig"
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, "mcsm_ppi2_scaled" , "mcsm_ppi2_affinity"
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, "mcsm_na_scaled" , "mcsm_na_affinity"
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#, "consurf_scaled" , "consurf_score"
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#, "snap2_scaled" , "snap2_score"
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#, "provean_scaled" , "provean_score"
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)
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all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)]
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outcome_cols_aff = c("duet_outcome"
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, "deepddg_outcome"
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, "ddg_dynamut2_outcome"
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, "foldx_outcome"
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#, "ddg_foldx", "foldx_scaled"
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, "ligand_outcome"
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, "mmcsm_lig_outcome"
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, "mcsm_ppi2_outcome"
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, "mcsm_na_outcome"
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# consurf outcome doesn't exist
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#,"provean_outcome"
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#,"snap2_outcome"
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)
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#===================
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# stability cols
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#===================
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raw_cols_stability = c("duet_stability_change"
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, "deepddg"
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, "ddg_dynamut2"
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, "ddg_foldx")
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scaled_cols_stability = c("duet_scaled"
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, "deepddg_scaled"
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, "ddg_dynamut2_scaled"
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, "foldx_scaled")
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outcome_cols_stability = c("duet_outcome"
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, "deepddg_outcome"
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, "ddg_dynamut2_outcome"
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, "foldx_outcome")
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#===================
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# affinity cols
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#===================
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raw_cols_affinity = c("ligand_affinity_change"
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, "mmcsm_lig"
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, "mcsm_ppi2_affinity"
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, "mcsm_na_affinity")
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scaled_cols_affinity = c("affinity_scaled"
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, "mmcsm_lig_scaled"
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, "mcsm_ppi2_scaled"
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, "mcsm_na_scaled" )
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outcome_cols_affinity = c( "ligand_outcome"
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, "mmcsm_lig_outcome"
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, "mcsm_ppi2_outcome"
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, "mcsm_na_outcome")
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#===================
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# conservation cols
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#===================
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# raw_cols_conservation = c("consurf_score"
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# , "snap2_score"
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# , "provean_score")
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#
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# scaled_cols_conservation = c("consurf_scaled"
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# , "snap2_scaled"
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# , "provean_scaled")
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#
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# # CANNOT strictly be used, as categories are not identical with conssurf missing altogether
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# outcome_cols_conservation = c("provean_outcome"
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# , "snap2_outcome"
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# #consurf outcome doesn't exist
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# )
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######################################################################
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cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols
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, raw_cols
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, scaled_cols
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, outcome_cols_aff)]
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cols_to_extract = cols_to_consider[cols_to_consider%in%c(common_cols
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, outcome_cols_aff)]
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, outcome_cols_affinity)]
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# cols_to_extract = cols_to_consider[cols_to_consider%in%c(common_cols
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# , outcome_cols_affinity)]
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##############################################################
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#####################
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# Ensemble affinity
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# Ensemble affinity: affinity_cols
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#####################
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# extract outcome cols and map numeric values to the categories
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# Destabilising == 0, and stabilising == 1 so rescaling can let -1 be destabilising
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df3_plot = df3[, cols_to_extract]
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df3_plot[, outcome_cols_aff] <- sapply(df3_plot[, outcome_cols_aff]
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, function(x){ifelse(x == "Destabilising", 0, 1)})
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#=====================================
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# Stability (4 cols): average the scores
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# across predictors ==> average by
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# position ==> scale b/w -1 and 1
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# column to average: ens_stability
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#=====================================
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cols_to_average = which(colnames(df3_plot)%in%outcome_cols_aff)
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# ensemble average across predictors
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df3_plot$ens_stability = rowMeans(df3_plot[,cols_to_average])
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head(df3_plot$position); head(df3_plot$mutationinformation)
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head(df3_plot$ens_stability)
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table(df3_plot$ens_stability)
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# ensemble average of predictors by position
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mean_ens_stability_by_position <- df3_plot %>%
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dplyr::group_by(position) %>%
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dplyr::summarize(avg_ens_stability = mean(ens_stability))
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# REscale b/w -1 and 1
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#en_stab_min = min(mean_ens_stability_by_position['avg_ens_stability'])
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#en_stab_max = max(mean_ens_stability_by_position['avg_ens_stability'])
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# scale the average stability value between -1 and 1
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# mean_ens_by_position['averaged_stability3_scaled'] = lapply(mean_ens_by_position['averaged_stability3']
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# , function(x) ifelse(x < 0, x/abs(en3_min), x/en3_max))
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mean_ens_stability_by_position['avg_ens_stability_scaled'] = lapply(mean_ens_stability_by_position['avg_ens_stability']
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, function(x) {
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scales::rescale(x, to = c(-1,1)
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#, from = c(en_stab_min,en_stab_max))
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, from = c(0,1))
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})
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cat(paste0('Average stability scores:\n'
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, head(mean_ens_stability_by_position['avg_ens_stability'])
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, '\n---------------------------------------------------------------'
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, '\nAverage stability scaled scores:\n'
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, head(mean_ens_stability_by_position['avg_ens_stability_scaled'])))
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# convert to a data frame
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mean_ens_stability_by_position = as.data.frame(mean_ens_stability_by_position)
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#FIXME: sanity checks
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# TODO: predetermine the bounds
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# l_bound_ens = min(mean_ens_stability_by_position['avg_ens_stability_scaled'])
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# u_bound_ens = max(mean_ens_stability_by_position['avg_ens_stability_scaled'])
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# df3_plot = df3[, cols_to_extract]
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#
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# if ( (l_bound_ens == -1) && (u_bound_ens == 1) ){
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# cat(paste0("PASS: ensemble stability scores averaged by position and then scaled"
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# , "\nmin ensemble averaged stability: ", l_bound_ens
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# , "\nmax ensemble averaged stability: ", u_bound_ens))
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# }else{
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# cat(paste0("FAIL: avergaed duet scores could not be scaled b/w -1 and 1"
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# , "\nmin ensemble averaged stability: ", l_bound_ens
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# , "\nmax ensemble averaged stability: ", u_bound_ens))
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# quit()
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# }
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##################################################################
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#%%
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affinity_outcome_colnames = c("ligand_outcome", "mmcsm_lig_outcome"
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, "mcsm_ppi2_outcome"
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, "mcsm_na_outcome")
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outcome_cols_affinity = colnames(df3)[colnames(df3)%in%affinity_outcome_colnames]
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outcome_cols_affinity = c("ligand_outcome"
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,"mmcsm_lig_outcome")
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cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols, scaled_cols, outcome_cols_aff, outcome_cols_affinity)]
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cols_to_extract = cols_to_consider[cols_to_consider%in%c(common_cols, outcome_cols_aff)]
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foo = df3[, cols_to_consider]
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df3_plot_orig = df3[, cols_to_extract]
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############################
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# Ensemble affinity: ligand
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############################
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# extract ligand affinity outcome cols and map numeric values to the categories
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# Destabilising == 1, and stabilising == 0
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cols_to_extract_affinity = cols_to_consider[cols_to_consider%in%c(common_cols
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, outcome_cols_affinity)]
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df3_plot_affinity = df3[, cols_to_extract_affinity]
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names(df3_plot_affinity)
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df3_plot_affinity[, outcome_cols_affinity] <- sapply(df3_plot_affinity[, outcome_cols_affinity]
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, function(x){ifelse(x == "Destabilising", 1, 0)})
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# df3_plot[, outcome_cols_affinity] <- sapply(df3_plot[, outcome_cols_affinity]
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# , function(x){ifelse(x == "Destabilising", 0, 1)})
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df3_plot = df3[, c(common_cols, scaled_cols)]
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#=====================================
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# Affintiy (2 cols): average the scores
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# across predictors ==> average by
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@ -206,7 +148,7 @@ df3_plot_affinity[, outcome_cols_affinity] <- sapply(df3_plot_affinity[, outcome
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# column to average: ens_affinity
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#=====================================
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cols_to_average_affinity = which(colnames(df3_plot_affinity)%in%outcome_cols_affinity)
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cols_to_average_affinity = which(colnames(df3_plot)%in%outcome_cols_affinity)
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cols_to_average_affinity
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# ensemble average across predictors
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@ -53,54 +53,85 @@ all_colnames = as.data.frame(colnames(df3))
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common_cols = c("mutationinformation"
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, "position"
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, "dst_mode"
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#, "mutation_info_labels"
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, "mutation_info_labels"
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, "sensitivity"
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, "ligand_distance")
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all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)]
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scaled_cols = c("duet_scaled" , "duet_stability_change"
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, "deepddg_scaled" , "deepddg"
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, "ddg_dynamut2_scaled" , "ddg_dynamut2"
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, "foldx_scaled" , "ddg_foldx"
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, "affinity_scaled" , "ligand_affinity_change"
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, "mmcsm_lig_scaled" , "mmcsm_lig"
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, "mcsm_ppi2_scaled" , "mcsm_ppi2_affinity"
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, "mcsm_na_scaled" , "mcsm_na_affinity"
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#, "consurf_scaled" , "consurf_score"
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#, "snap2_scaled" , "snap2_score"
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#, "provean_scaled" , "provean_score"
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)
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all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)]
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outcome_cols_aff = c("duet_outcome"
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, "deepddg_outcome"
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, "ddg_dynamut2_outcome"
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, "foldx_outcome"
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#, "ddg_foldx", "foldx_scaled"
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, "ligand_outcome"
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, "mmcsm_lig_outcome"
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, "mcsm_ppi2_outcome"
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, "mcsm_na_outcome"
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# consurf outcome doesn't exist
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#,"provean_outcome"
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#,"snap2_outcome"
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)
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#===================
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# stability cols
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#===================
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raw_cols_stability = c("duet_stability_change"
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, "deepddg"
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, "ddg_dynamut2"
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, "ddg_foldx")
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scaled_cols_stability = c("duet_scaled"
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, "deepddg_scaled"
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, "ddg_dynamut2_scaled"
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, "foldx_scaled")
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outcome_cols_stability = c("duet_outcome"
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, "deepddg_outcome"
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, "ddg_dynamut2_outcome"
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, "foldx_outcome")
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#===================
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# affinity cols
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#===================
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raw_cols_affinity = c("ligand_affinity_change"
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, "mmcsm_lig"
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, "mcsm_ppi2_affinity"
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, "mcsm_na_affinity")
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scaled_cols_affinity = c("affinity_scaled"
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, "mmcsm_lig_scaled"
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, "mcsm_ppi2_scaled"
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, "mcsm_na_scaled" )
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outcome_cols_affinity = c( "ligand_outcome"
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, "mmcsm_lig_outcome"
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, "mcsm_ppi2_outcome"
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, "mcsm_na_outcome")
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#===================
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# conservation cols
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#===================
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# raw_cols_conservation = c("consurf_score"
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# , "snap2_score"
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# , "provean_score")
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#
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# scaled_cols_conservation = c("consurf_scaled"
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# , "snap2_scaled"
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# , "provean_scaled")
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#
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# # CANNOT strictly be used, as categories are not identical with conssurf missing altogether
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# outcome_cols_conservation = c("provean_outcome"
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# , "snap2_outcome"
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# #consurf outcome doesn't exist
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# )
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###########################################################
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cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols
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, scaled_cols
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, outcome_cols)]
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, raw_cols_stability
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, scaled_cols_stability
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, outcome_cols_stability)]
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cols_to_extract = cols_to_consider[cols_to_consider%in%c(common_cols
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, outcome_cols)]
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, outcome_cols_stability)]
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##############################################################
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#####################
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# Ensemble stability
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# Ensemble stability: outcome_cols_stability
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#####################
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# extract outcome cols and map numeric values to the categories
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# Destabilising == 0, and stabilising == 1, so rescaling can let -1 be destabilising
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df3_plot = df3[, cols_to_extract]
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# assign numeric values to outcome
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df3_plot[, outcome_cols] <- sapply(df3_plot[, outcome_cols]
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df3_plot[, outcome_cols_stability] <- sapply(df3_plot[, outcome_cols_stability]
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, function(x){ifelse(x == "Destabilising", 0, 1)})
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table(df3$duet_outcome)
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table(df3_plot$duet_outcome)
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@ -111,7 +142,7 @@ table(df3_plot$duet_outcome)
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# column to average: ens_stability
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#=====================================
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cols_to_average = which(colnames(df3_plot)%in%outcome_cols)
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cols_to_average = which(colnames(df3_plot)%in%outcome_cols_stability)
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# ensemble average across predictors
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df3_plot$ens_stability = rowMeans(df3_plot[,cols_to_average])
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