still trying affinity phew
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1 changed files with 112 additions and 114 deletions
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@ -3,7 +3,7 @@
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#source("~/git/LSHTM_analysis/config/gid.R")
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#source("~/git/LSHTM_analysis/config/gid.R")
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#source("~/git/LSHTM_analysis/config/embb.R")
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#source("~/git/LSHTM_analysis/config/embb.R")
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#source("~/git/LSHTM_analysis/config/katg.R")
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#source("~/git/LSHTM_analysis/config/katg.R")
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source("~/git/LSHTM_analysis/config/rpob.R")
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#source("~/git/LSHTM_analysis/config/rpob.R")
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source("/home/tanu/git/LSHTM_analysis/my_header.R")
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source("/home/tanu/git/LSHTM_analysis/my_header.R")
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#########################################################
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#########################################################
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@ -57,15 +57,17 @@ table(df3$sensitivity)
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length(unique((df3$mutationinformation)))
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length(unique((df3$mutationinformation)))
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all_colnames = as.data.frame(colnames(df3))
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all_colnames = as.data.frame(colnames(df3))
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# FIXME: ADD distance to NA when SP replies
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dist_columns = c("ligand_distance", "interface_dist")
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DistCutOff = 10
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common_cols = c("mutationinformation"
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common_cols = c("mutationinformation"
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, "X5uhc_position"
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, "X5uhc_position"
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, "X5uhc_offset"
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, "X5uhc_offset"
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, "position"
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, "position"
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, "dst_mode"
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, "dst_mode"
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, "mutation_info_labels"
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, "mutation_info_labels"
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, "sensitivity"
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, "sensitivity", dist_columns )
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, "ligand_distance"
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, "interface_dist")
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all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)]
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all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)]
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all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)]
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all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)]
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@ -73,20 +75,20 @@ all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)]
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#===================
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#===================
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# stability cols
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# stability cols
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#===================
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#===================
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# raw_cols_stability = c("duet_stability_change"
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raw_cols_stability = c("duet_stability_change"
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# , "deepddg"
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, "deepddg"
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# , "ddg_dynamut2"
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, "ddg_dynamut2"
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# , "ddg_foldx")
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, "ddg_foldx")
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#
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# scaled_cols_stability = c("duet_scaled"
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scaled_cols_stability = c("duet_scaled"
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# , "deepddg_scaled"
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, "deepddg_scaled"
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# , "ddg_dynamut2_scaled"
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, "ddg_dynamut2_scaled"
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# , "foldx_scaled")
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, "foldx_scaled")
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#
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# outcome_cols_stability = c("duet_outcome"
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outcome_cols_stability = c("duet_outcome"
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# , "deepddg_outcome"
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, "deepddg_outcome"
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# , "ddg_dynamut2_outcome"
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, "ddg_dynamut2_outcome"
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# , "foldx_outcome")
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, "foldx_outcome")
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#===================
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#===================
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# affinity cols
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# affinity cols
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@ -123,113 +125,109 @@ outcome_cols_affinity = c( "ligand_outcome"
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# #consurf outcome doesn't exist
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# #consurf outcome doesn't exist
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# )
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# )
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######################################################################
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gene_aff_cols = colnames(df3)[colnames(df3)%in%scaled_cols_affinity]
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cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols
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gene_stab_cols = colnames(df3)[colnames(df3)%in%scaled_cols_stability]
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, raw_cols_affinity
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gene_common_cols = colnames(df3)[colnames(df3)%in%common_cols]
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, scaled_cols_affinity
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, outcome_cols_affinity
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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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)]
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cols_to_extract = cols_to_consider[cols_to_consider%in%c(common_cols
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sel_cols = c(gene_common_cols
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, raw_cols_affinity
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, gene_stab_cols
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, scaled_cols_affinity)]
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, gene_aff_cols)
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df3_plot = df3[, cols_to_extract]
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#########################################
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#df3_plot = df3[, cols_to_extract]
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DistCutOff_colnames = c("ligand_distance", "interface_dist")
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df3_plot = df3[, sel_cols]
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DistCutOff = 10
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######################
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#FILTERING HMMMM!
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#all dist <10
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#for embb this results in 2 muts
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######################
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df3_affinity_filtered = df3_plot[ (df3_plot$ligand_distance<10 | df3_plot$interface_dist <10),]
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df3_affinity_filtered = df3_plot[ (df3_plot$ligand_distance<10 | df3_plot$interface_dist <10),]
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df3_affinity_filtered = df3_plot[ (df3_plot$ligand_distance<10 & df3_plot$interface_dist <10),]
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c0u = unique(df3_affinity_filtered$position)
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c0u = unique(df3_affinity_filtered$position)
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length(c0u)
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length(c0u)
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foo = df3_affinity_filtered[df3_affinity_filtered$ligand_distance<10,]
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#df = df3_affinity_filtered
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bar = df3_affinity_filtered[df3_affinity_filtered$interface_dist<10,]
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##########################################
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#NO FILTERING: annotate the whole df
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wilcox.test(foo$mmcsm_lig_scaled~foo$sensitivity)
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df = df3_plot
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wilcox.test(foo$mmcsm_lig~foo$sensitivity)
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wilcox.test(foo$affinity_scaled~foo$sensitivity)
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wilcox.test(foo$ligand_affinity_change~foo$sensitivity)
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wilcox.test(bar$mcsm_na_scaled~bar$sensitivity)
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wilcox.test(bar$mcsm_na_affinity~bar$sensitivity)
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wilcox.test(bar$mcsm_ppi2_scaled~bar$sensitivity)
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wilcox.test(bar$mcsm_ppi2_affinity~bar$sensitivity)
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##############################################################
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df = df3_affinity_filtered
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sum(is.na(df))
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sum(is.na(df))
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df2 = na.omit(df) # Apply na.omit function
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df2 = na.omit(df)
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c0u = unique(df2$position)
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length(c0u)
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a = df2[df2$position==37,]
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# reassign orig
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sel_cols = c("mutationinformation", "position", scaled_cols_affinity)
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my_df_raw = df3
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a = a[, sel_cols]
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##############################################################
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# now subset
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# FIXME: ADD distance to NA when SP replies
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df3 = df2
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#######################################################
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#####################
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#=================
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# Ensemble affinity: affinity_cols
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# affinity effect
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# mcsm_lig, mmcsm_lig and mcsm_na
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#=================
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#####################
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give_col=function(x,y,df=df3){
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# extract outcome cols and map numeric values to the categories
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df[df$position==x,y]
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# Destabilising == 0, and stabilising == 1 so rescaling can let -1 be destabilising
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#########################################
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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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# position ==> scale b/w -1 and 1
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# column to average: ens_affinity
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#=====================================
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cols_mcsm_lig = c("mutationinformation"
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, "position"
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, "sensitivity"
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, "X5uhc_position"
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, "X5uhc_offset"
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, "ligand_distance"
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, "ligand_outcome"
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, "mmcsm_lig_outcome")
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######################################################################
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##################
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# merge: mean ensemble stability and affinity by_position
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####################
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# if ( class(mean_ens_stability_by_position) && class(mean_ens_affinity_by_position) != "data.frame"){
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# cat("Y")
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# }
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common_cols = intersect(colnames(mean_ens_stability_by_position), colnames(mean_ens_affinity_by_position))
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if (dim(mean_ens_stability_by_position) && dim(mean_ens_affinity_by_position)){
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print(paste0("PASS: dim's match, mering dfs by column :", common_cols))
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#combined = as.data.frame(cbind(mean_duet_by_position, mean_affinity_by_position ))
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combined_df = as.data.frame(merge(mean_ens_stability_by_position
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, mean_ens_affinity_by_position
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, by = common_cols
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, all = T))
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cat(paste0("\nnrows combined_df:", nrow(combined_df)
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, "\nnrows combined_df:", ncol(combined_df)))
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}else{
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cat(paste0("FAIL: dim's mismatch, aborting cbind!"
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, "\nnrows df1:", nrow(mean_duet_by_position)
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, "\nnrows df2:", nrow(mean_affinity_by_position)))
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quit()
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}
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}
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for (i in unique(df3$position) ){
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#print(i)
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biggest = max(abs(give_col(i,gene_aff_cols)))
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df3[df3$position==i,'abs_max_effect'] = biggest
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df3[df3$position==i,'effect_type']= names(
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give_col(i,gene_aff_cols)[which(
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abs(
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give_col(i,gene_aff_cols)
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) == biggest, arr.ind=T
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)[, "col"]])
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# effect_name = unique(df3[df3$position==i,'effect_type'])
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effect_name = df3[df3$position==i,'effect_type'][1] # pick first one in case we have multiple exact values
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ind = rownames(which(abs(df3[df3$position==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
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df3[df3$position==i,'effect_sign'] = sign(df3[effect_name][ind,])
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}
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df3$effect_type = sub("\\.[0-9]+", "", df3$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
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df3U = df3[!duplicated(df3[c('position')]), ]
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table(df3U$effect_type)
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#########################################################
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#%% consider stability as well
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df4 = df2
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#=================
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# stability + affinity effect
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#=================
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effect_cols = c(gene_aff_cols, gene_stab_cols)
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give_col=function(x,y,df=df4){
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df[df$position==x,y]
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}
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for (i in unique(df4$position) ){
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#print(i)
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biggest = max(abs(give_col(i,effect_cols)))
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df4[df4$position==i,'abs_max_effect'] = biggest
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df4[df4$position==i,'effect_type']= names(
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give_col(i,effect_cols)[which(
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abs(
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give_col(i,effect_cols)
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) == biggest, arr.ind=T
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)[, "col"]])
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# effect_name = unique(df4[df4$position==i,'effect_type'])
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effect_name = df4[df4$position==i,'effect_type'][1] # pick first one in case we have multiple exact values
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ind = rownames(which(abs(df4[df4$position==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
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df4[df4$position==i,'effect_sign'] = sign(df4[effect_name][ind,])
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}
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df4$effect_type = sub("\\.[0-9]+", "", df4$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
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df4U = df4[!duplicated(df4[c('position')]), ]
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table(df4U$effect_type)
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#%%============================================================
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#%%============================================================
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# output
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# output
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write.csv(combined_df, outfile_mean_ens_st_aff
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write.csv(combined_df, outfile_mean_ens_st_aff
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