241 lines
7.7 KiB
R
241 lines
7.7 KiB
R
#source("~/git/LSHTM_analysis/config/pnca.R")
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#source("~/git/LSHTM_analysis/config/alr.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/katg.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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#########################################################
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# TASK: Generate averaged affinity values
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# across all affinity tools for a given structure
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# as applicable...
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#########################################################
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#=======
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# output
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#=======
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outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
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#OutFile1
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outfile_mean_aff = paste0(outdir_images, "/", tolower(gene)
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, "_mean_affinity_all.csv")
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print(paste0("Output file:", outfile_mean_aff))
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#OutFile2
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outfile_mean_aff_priorty = paste0(outdir_images, "/", tolower(gene)
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, "_mean_affinity_priority.csv")
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print(paste0("Output file:", outfile_mean_aff_priorty))
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#%%===============================================================
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#=============
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# Input
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#=============
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df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
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df3 = read.csv(df3_filename)
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length(df3$mutationinformation)
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# mut_info checks
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table(df3$mutation_info)
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table(df3$mutation_info_orig)
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table(df3$mutation_info_labels_orig)
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# used in plots and analyses
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table(df3$mutation_info_labels) # different, and matches dst_mode
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table(df3$dst_mode)
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# create column based on dst mode with different colname
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table(is.na(df3$dst))
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table(is.na(df3$dst_mode))
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#===============
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# Create column: sensitivity mapped to dst_mode
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#===============
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df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
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table(df3$sensitivity)
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length(unique((df3$mutationinformation)))
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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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, "X5uhc_position"
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, "X5uhc_offset"
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, "position"
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, "dst_mode"
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, "mutation_info_labels"
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, "sensitivity", dist_columns )
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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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#===================
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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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gene_aff_cols = colnames(df3)[colnames(df3)%in%scaled_cols_affinity]
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gene_stab_cols = colnames(df3)[colnames(df3)%in%scaled_cols_stability]
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gene_common_cols = colnames(df3)[colnames(df3)%in%common_cols]
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sel_cols = c(gene_common_cols
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, gene_stab_cols
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, gene_aff_cols)
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#########################################
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#df3_plot = df3[, cols_to_extract]
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df3_plot = df3[, sel_cols]
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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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c0u = unique(df3_affinity_filtered$position)
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length(c0u)
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#df = df3_affinity_filtered
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##########################################
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#NO FILTERING: annotate the whole df
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df = df3_plot
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sum(is.na(df))
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df2 = na.omit(df)
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c0u = unique(df2$position)
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length(c0u)
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# reassign orig
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my_df_raw = df3
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# now subset
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df3 = df2
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#######################################################
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#=================
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# affinity effect
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#=================
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give_col=function(x,y,df=df3){
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df[df$position==x,y]
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}
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for (i in unique(df3$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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# output
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write.csv(combined_df, outfile_mean_ens_st_aff
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, row.names = F)
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cat("Finished writing file:\n"
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, outfile_mean_ens_st_aff
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, "\nNo. of rows:", nrow(combined_df)
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, "\nNo. of cols:", ncol(combined_df))
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# end of script
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#===============================================================
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