tidied script plotting_data.R by removing superceded code
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@ -31,62 +31,6 @@ dup_muts = data.frame()
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cat("\nInput dimensions:", dim(df))
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cat("\nInput dimensions:", dim(df))
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#==================================
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# add foldx outcome category
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# and foldx scaled values
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# This will enable to always have these variables available
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# when calling for plots
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# included this now in combine_dfs.py!!!! finallyS
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#==================================
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#------------------------------
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# # adding foldx scaled values
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# # scale data b/w -1 and 1
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# #------------------------------
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# n = which(colnames(df) == "ddg"); n
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#
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# my_min = min(df[,n]); my_min
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# my_max = max(df[,n]); my_max
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#
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# df$foldx_scaled = ifelse(df[,n] < 0
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# , df[,n]/abs(my_min)
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# , df[,n]/my_max)
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# # sanity check
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# my_min = min(df$foldx_scaled); my_min
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# my_max = max(df$foldx_scaled); my_max
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#
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# if (my_min == -1 && my_max == 1){
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# cat("\nPASS: foldx ddg successfully scaled b/w -1 and 1"
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# , "\nProceeding with assigning foldx outcome category")
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# }else{
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# cat("\nFAIL: could not scale foldx ddg values"
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# , "Aborting!\n")
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# }
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#------------------------------
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# adding foldx outcome category
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# ddg<0 = "Stabilising" (-ve)
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#------------------------------
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# c1 = table(df$ddg < 0)
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# df$foldx_outcome = ifelse(df$ddg < 0, "Stabilising", "Destabilising")
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# c2 = table(df$ddg < 0)
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#
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# if ( all(c1 == c2) ){
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# cat("\nPASS: foldx outcome successfully created")
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# }else{
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# cat("\nFAIL: foldx outcome could not be created. Aborting!\n")
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# exit()
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# }
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#------------------------------
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# renaming foldx column from
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# "ddg" --> "ddg_foldx"
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#------------------------------
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# # change name to foldx
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# colnames(df)[n] <- "ddg_foldx"
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#==================================
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#==================================
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# extract unique mutation entries
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# extract unique mutation entries
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#==================================
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#==================================
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