From b8169b62ce5fc9ffc7e48ea0317331331ede2bf7 Mon Sep 17 00:00:00 2001
From: Thomas  OBADIA <thomas.obadia@pasteur.fr>
Date: Tue, 3 May 2022 15:02:31 +0200
Subject: [PATCH] Add support for Bioplex MFI outputs, including guessing the
 type of machine...

---
 ...e_3_BIOPLEX_MFI-WITH-BEAD-COUNTS_XLSX.xlsx | Bin 0 -> 27169 bytes
 .../Example_3_plate_layout_XLSX.xlsx          | Bin 0 -> 10612 bytes
 ..._BIOPLEX_MFI-WITHOUT-BEAD-COUNTS_XLSX.xlsx | Bin 0 -> 19603 bytes
 .../Example_4_plate_layout_XLSX.xlsx          | Bin 0 -> 10614 bytes
 FUNCTIONS.R                                   | 625 ++++++++++--------
 SHINY_APP.R                                   |   4 +-
 6 files changed, 357 insertions(+), 272 deletions(-)
 create mode 100644 Example data files/Example_3_BIOPLEX_MFI-WITH-BEAD-COUNTS_XLSX.xlsx
 create mode 100644 Example data files/Example_3_plate_layout_XLSX.xlsx
 create mode 100644 Example data files/Example_4_BIOPLEX_MFI-WITHOUT-BEAD-COUNTS_XLSX.xlsx
 create mode 100644 Example data files/Example_4_plate_layout_XLSX.xlsx

diff --git a/Example data files/Example_3_BIOPLEX_MFI-WITH-BEAD-COUNTS_XLSX.xlsx b/Example data files/Example_3_BIOPLEX_MFI-WITH-BEAD-COUNTS_XLSX.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..6ef56a6d0c5f4e685b57c2bd8fdedb99cbf12ad6
GIT binary patch
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diff --git a/Example data files/Example_4_BIOPLEX_MFI-WITHOUT-BEAD-COUNTS_XLSX.xlsx b/Example data files/Example_4_BIOPLEX_MFI-WITHOUT-BEAD-COUNTS_XLSX.xlsx
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diff --git a/FUNCTIONS.R b/FUNCTIONS.R
index 31371cb..e3bdc23 100644
--- a/FUNCTIONS.R
+++ b/FUNCTIONS.R
@@ -41,7 +41,7 @@ runRelativeAntibodyUnits = function(fname1, fname2, MFI_CSV, MFI_N_ANTIGENS, TEM
   ## converts the median fluorescence intensity (MFI) values into relative antibody units.    ##
   ##                                                                                          ##
   ## Input:                                                                                   ##
-  ## - Luminex-200 or Magpix output file (required)                                           ##
+  ## - Luminex-200 or Magpix output file or BioPlex raw MFI output file(required)             ##
   ## - Plate layout with bleedcode information (optional)                                     ##
   ##                                                                                          ##
   ## Output:                                                                                  ##
@@ -56,7 +56,9 @@ runRelativeAntibodyUnits = function(fname1, fname2, MFI_CSV, MFI_N_ANTIGENS, TEM
   #### READ DATA
   ##########################################################################################################
   
-  # ## The first 41 rows are not relevant and are not imported into R.
+  ## The first rows are headers not relevant and are quickly discarded into R.
+  ## The number of rows to discard depends on platform used to generate the input.
+  
   
   if(MFI_CSV){
     
@@ -101,7 +103,7 @@ runRelativeAntibodyUnits = function(fname1, fname2, MFI_CSV, MFI_N_ANTIGENS, TEM
     dim(L)
     
     # MFI values as numeric
-    L[,-which(colnames(L) %in% c("Location","Sample","Total Events","TotalEvents"))] = lapply(L[,-which(colnames(L) %in% c("Location","Sample","Total Events"))], as.numeric)
+    L[,-which(colnames(L) %in% c("Location","Sample","Total Events","TotalEvents"))] = lapply(L[,-which(colnames(L) %in% c("Location","Sample","Total Events","TotalEvents"))], as.numeric)
     
     ## Load the counts to check for run quality control
     C = L_full[(count_row_number+1):(count_row_number+1+nrow(L)),1:(3+as.integer(MFI_N_ANTIGENS))]
@@ -133,70 +135,138 @@ runRelativeAntibodyUnits = function(fname1, fname2, MFI_CSV, MFI_N_ANTIGENS, TEM
     # Read
     L_full <- as.data.frame(read_excel(fname1))
     
-    # Identify rows
-    median_row_number <- which(L_full$xPONENT == "Median")
-    count_row_number <- which(L_full$xPONENT == "Count")
-    endcount_row_number <- which(L_full$xPONENT == "Avg Net MFI")
-    
-    # Load Excel file
-    L <- as.data.frame(read_excel(fname1, skip = median_row_number+1, col_types = "text"))
+    # Guess the platform from the header structure
+    # This should be improved in the future (made into a radio button instead ?)
+    PLATFORM <- ifelse(any(grepl("MAGPIX", colnames(L_full))), "Magpix", 
+                       ifelse(any(grepl("Reader Serial Number", L_full[, 1])), "Bioplex", "Unknown"))
     
-    ## Find all blank rows (i.e. rows that are all NA).
-    ## Then keep rows preceding the first blank row.
-    blank.row.number <- which(rowSums(is.na(L)) == length(names(L)))[1]
-    if(is.na(blank.row.number)){
-      L = L
-    }else{
-      L <- L[1:(blank.row.number-1),]
+    ## Magpix data input detected
+    if (PLATFORM == "Magpix") {
+      cat("Guessed MFI input file from a Magpix platform")
+      
+      # Identify rows
+      median_row_number <- which(L_full$xPONENT == "Median")
+      count_row_number <- which(L_full$xPONENT == "Count")
+      endcount_row_number <- which(L_full$xPONENT == "Avg Net MFI")
+      
+      # Load Excel file
+      L <- as.data.frame(read_excel(fname1, skip = median_row_number+1, col_types = "text"))
+      
+      ## Find all blank rows (i.e. rows that are all NA).
+      ## Then keep rows preceding the first blank row.
+      blank.row.number <- which(rowSums(is.na(L)) == length(names(L)))[1]
+      if(is.na(blank.row.number)){
+        L = L
+      }else{
+        L <- L[1:(blank.row.number-1),]
+      }
+      
+      ## Exclude column that corresponds to "Total events"
+      L <- L[, !(colnames(L) %in% c("Total Events","TotalEvents"))]
+      
+      # Antigen names clean-up
+      colnames(L) = gsub("\\..*", "", colnames(L))
+      # Remove any values in (parentheses)
+      colnames(L) = gsub("\\s*\\([^\\)]+\\)","", colnames(L))
+      # Remove spaces
+      colnames(L) = gsub(" ","", colnames(L))
+      
+      dim(L)
+      
+      # Change "NaN" to 0s
+      L <- L %>% mutate_all(funs(gsub("NaN", 0, .)))
+      
+      # MFI values as numeric
+      L[,-which(colnames(L) %in% c("Location","Sample","Total Events","TotalEvents"))] = lapply(L[,-which(colnames(L) %in% c("Location","Sample","Total Events","TotalEvents"))], as.numeric)
+      
+      C <- as.data.frame(read_excel(fname1, skip = count_row_number+1, col_types = "text"))
+      
+      ## Find all blank rows (i.e. rows that are all NA).
+      ## Then keep rows preceding the first blank row.
+      blank.row.number <- which(rowSums(is.na(C)) == length(names(C)))[1]
+      if(is.na(blank.row.number)){
+        C = C
+      }else{
+        C <- C[1:(blank.row.number-1),]
+      }
+      
+      ## Exclude column that corresponds to "Total events"
+      C <- C[, !(colnames(C) %in% c("Total Events","TotalEvents"))]
+      
+      # Antigen names clean-up
+      colnames(C) = gsub("\\..*", "", colnames(C))
+      # Remove any values in (parentheses)
+      colnames(C) = gsub("\\s*\\([^\\)]+\\)","", colnames(C))
+      # Remove spaces
+      colnames(C) = gsub(" ","", colnames(C))
+      dim(C)
+      
+      ## Save the MFI values for the blank sample(s) for run quality control
+      B <- L %>% filter(grepl(c("^B"), Sample, ignore.case = T))
+      dim(B)
+      
+      ## Save the MFI values for the standards for run quality control
+      S <- L %>% filter(grepl("^S", Sample, ignore.case = T))
+      dim(S) #there should be 10 rows
     }
     
-    ## Exclude column that corresponds to "Total events"
-    L <- L[, !(colnames(L) %in% c("Total Events","TotalEvents"))]
-    
-    # Antigen names clean-up
-    colnames(L) = gsub("\\..*", "", colnames(L))
-    # Remove any values in (parentheses)
-    colnames(L) = gsub("\\s*\\([^\\)]+\\)","", colnames(L))
-    # Remove spaces
-    colnames(L) = gsub(" ","", colnames(L))
-    
-    dim(L)
-    
-    # Change "NaN" to 0s
-    L <- L %>% mutate_all(funs(gsub("NaN", 0, .)))
-    
-    # MFI values as numeric
-    L[,-which(colnames(L) %in% c("Location","Sample","Total Events","TotalEvents"))] = lapply(L[,-which(colnames(L) %in% c("Location","Sample","Total Events"))], as.numeric)
-    
-    C <- as.data.frame(read_excel(fname1, skip = count_row_number+1, col_types = "text"))
-    
-    ## Find all blank rows (i.e. rows that are all NA).
-    ## Then keep rows preceding the first blank row.
-    blank.row.number <- which(rowSums(is.na(C)) == length(names(C)))[1]
-    if(is.na(blank.row.number)){
-      C = C
-    }else{
-      C <- C[1:(blank.row.number-1),]
+    ## BioPlex data input detected
+    else if (PLATFORM == "Bioplex") {
+      cat("Guessed MFI input file from a BioPlex platform")
+      
+      # In BioPlex outputs, MFI and bead counts are in the same cell with format MFI (counts).
+      # Note that the bead counts may not be present, in that case only the MFI is reported.
+      
+      # Identify rows (-1 because we match the header, contrary as one row before in Magpix)
+      median_row_number <- which(L_full[, 1] == "Well") - 1
+      
+      # Load again Excel file reading only after the 1st row of interest
+      L <- as.data.frame(read_excel(fname1, skip = median_row_number+1, col_types = "text")) %>% 
+        dplyr::rename(Location = Well, 
+                       Sample = Type) %>% 
+        # Pad the Location column to match the syntax used by Luminex outputs,
+        # so that future regex will work regardless
+        dplyr::mutate(Location = paste0(1:n(), "(1,", Location, ")"))
+      
+      # Remove columns that are not MFI readings or sample code / location
+      L <- L[, !(colnames(L) %in% c("Region", "Gate", "Total", "% Agg Beads", "Sampling Errors"))]
+      
+      # Antigen names clean-up
+      colnames(L) = gsub("\\..*", "", colnames(L))
+      # Remove any values in (parentheses)
+      colnames(L) = gsub("\\s*\\([^\\)]+\\)","", colnames(L))
+      # Remove spaces
+      colnames(L) = gsub(" ","", colnames(L))
+      
+      dim(L)
+      
+      # Change "NaN" to 0s
+      L <- L %>% mutate_all(funs(gsub("NaN", 0, .)))
+      
+      # Bead counts, when available, will be extracted from the table
+      C <- L %>% 
+        dplyr::mutate(dplyr::across(!c(Location, Sample), gsub, pattern = "^(\\d+(?:\\.\\d+)?)(\\s*\\((\\d+(?:\\.\\d+)?)\\))?$", replacement = "\\3")) %>% 
+        # Set to NA in case bead counts were not available
+        dplyr::mutate(dplyr::across(!c(Location, Sample), gsub, pattern = "^$", replacement = NA))
+      
+      dim(C)
+      
+      # Remove bead counts from L when they have been isolated in C
+      L <- L %>% 
+        dplyr::mutate(dplyr::across(!c(Location, Sample), gsub, pattern = "^(\\d+(?:\\.\\d+)?)(\\s*\\((\\d+(?:\\.\\d+)?)\\))?$", replacement = "\\1"))
+      
+      ## Save the MFI values for the blank sample(s) for run quality control
+      B <- L %>% filter(grepl(c("^B"), Sample, ignore.case = T))
+      dim(B)
+      
+      ## Save the MFI values for the standards for run quality control
+      S <- L %>% filter(grepl("^S", Sample, ignore.case = T))
+      dim(S) #there should be 10 rows
     }
     
-    ## Exclude column that corresponds to "Total events"
-    C <- C[, !(colnames(C) %in% c("Total Events","TotalEvents"))]
-    
-    # Antigen names clean-up
-    colnames(C) = gsub("\\..*", "", colnames(C))
-    # Remove any values in (parentheses)
-    colnames(C) = gsub("\\s*\\([^\\)]+\\)","", colnames(C))
-    # Remove spaces
-    colnames(C) = gsub(" ","", colnames(C))
-    dim(C)
-    
-    ## Save the MFI values for the blank sample(s) for run quality control
-    B <- L %>% filter(grepl(c("^B"), Sample, ignore.case = T))
-    dim(B)
-    
-    ## Save the MFI values for the standards for run quality control
-    S <- L %>% filter(grepl("^S", Sample, ignore.case = T))
-    dim(S) #there should be 10 rows
+    else {
+      stop("Execution halted: the platform used to generate the input could not be determined.")
+    }
     
   }
   
@@ -214,9 +284,9 @@ runRelativeAntibodyUnits = function(fname1, fname2, MFI_CSV, MFI_N_ANTIGENS, TEM
     dplyr::filter(grepl("^\\d+$",count)) %>% 
     dplyr::mutate(count = as.numeric(count),
                   warning = case_when(
-      count<15~1,
-      count>=15~0
-    )) %>%
+                    count<15~1,
+                    count>=15~0
+                  )) %>%
     dplyr::select(Location, warning) %>%
     dplyr::group_by(Location) %>%
     dplyr::summarise(sum = sum(warning)) %>%
@@ -246,8 +316,8 @@ runRelativeAntibodyUnits = function(fname1, fname2, MFI_CSV, MFI_N_ANTIGENS, TEM
   
   ## Protein name list obtained after removing variable names: "Location" and "Sample"
   proteins <- names(L[, -c(1:2)]); proteins
-  ## Add new variable to data frame to indicate the first letter of sample type ("B", "C", "S", "U","N")
-  ## "B" = blank, "C"=control, "S"=standard (dilution of the pool), "U"=sample, "N"=negative control
+  ## Add new variable to data frame to indicate the first letter of sample type ("B", "C", "S", "U" or "X","N")
+  ## "B" = blank, "C"=control, "S"=standard (dilution of the pool), "U" or "X"=sample, "N"=negative control
   L$type.letter <- substr(L$Sample, start=1, stop=1)
   dilution <- c(1/50, 1/100, 1/200, 1/400, 1/800, 1/1600, 1/3200, 1/6400, 1/12800, 1/25600)
   dilution.scaled <- dilution*25600; dilution.scaled
@@ -303,7 +373,7 @@ runRelativeAntibodyUnits = function(fname1, fname2, MFI_CSV, MFI_N_ANTIGENS, TEM
     
     for (r in 1:nrow(L)){
       results <- NULL
-      if (L$type.letter[r]=="U"){
+      if (L$type.letter[r] %in% c("U", "X")){
         mfi.X <- as.numeric(L[r, i])
         y <- log(mfi.X)
         
@@ -367,7 +437,7 @@ runRelativeAntibodyUnits = function(fname1, fname2, MFI_CSV, MFI_N_ANTIGENS, TEM
   # Make all columns after 1st 4 numeric
   results.df.wide[,5:ncol(results.df.wide)] = lapply(results.df.wide[,5:ncol(results.df.wide)], as.character)
   results.df.wide[,5:ncol(results.df.wide)] = lapply(results.df.wide[,5:ncol(results.df.wide)], as.numeric)
-
+  
   
   ##########################################################################################################
   #### OUTPUT
@@ -428,7 +498,7 @@ getRelativeAntibodyUnits = function(RAW_MFI_FILE_NAME, RAW_MFI_FILE_PATH,
   
   cat("******************Running RAU function******************\n")
   
-  # Get Luminex-MagPix full file path
+  # Get Luminex-MagPix-BioPlex full file path
   fname1 = RAW_MFI_FILE_PATH
   
   # Extract working directly of this file
@@ -461,46 +531,53 @@ getRelativeAntibodyUnits = function(RAW_MFI_FILE_NAME, RAW_MFI_FILE_PATH,
   bead_counts = results[[4]]
   MFI_RAU_results = results[[5]]
   model_results = results[[6]]
+  plot_stdcurve <- plot_counts <- plot_blank <- plots_model <- all_model_plots <- NULL
   
   ## Plot standard curve raw MFI
-  plot_stdcurve <- std_curve %>% 
-    dplyr::select(-Location) %>% 
-    dplyr::mutate(Sample = c("S1", "S2", "S3", "S4", "S5", "S6", "S7", "S8", "S9", "S10"),
-           Sample_dilution = factor(paste0(Sample,": ",dilution_plot),levels=paste0(Sample,": ",dilution_plot))) %>% 
-    tidyr::pivot_longer(-c(Sample,dilution,dilution_plot,Sample_dilution), names_to = "protein", values_to = "MFI") %>% 
-    # mutate(Sample = factor(Sample, c("S1", "S2", "S3", "S4", "S5", "S6", "S7", "S8", "S9", "S10"))) %>% 
-    dplyr::mutate(MFI = as.numeric(MFI)) %>% 
-    ggplot(aes(x = Sample_dilution, y = MFI, color = protein, group = protein)) + 
-    geom_point() + 
-    geom_line() +
-    scale_y_log10(breaks = c(0, 10, 100, 1000, 10000)) +
-    labs(x = "standard curve", 
-         y = "log(MFI)") +
-    facet_wrap(~protein) +
-    theme_bw() +
-    theme(axis.text.x = element_text(angle = 90, hjust = 1)) 
+  if (nrow(std_curve) > 0) {
+    plot_stdcurve <- std_curve %>% 
+      dplyr::select(-Location) %>% 
+      dplyr::mutate(Sample = c("S1", "S2", "S3", "S4", "S5", "S6", "S7", "S8", "S9", "S10"),
+                    Sample_dilution = factor(paste0(Sample,": ",dilution_plot),levels=paste0(Sample,": ",dilution_plot))) %>% 
+      tidyr::pivot_longer(-c(Sample,dilution,dilution_plot,Sample_dilution), names_to = "protein", values_to = "MFI") %>% 
+      # mutate(Sample = factor(Sample, c("S1", "S2", "S3", "S4", "S5", "S6", "S7", "S8", "S9", "S10"))) %>% 
+      dplyr::mutate(MFI = as.numeric(MFI)) %>% 
+      ggplot(aes(x = Sample_dilution, y = MFI, color = protein, group = protein)) + 
+      geom_point() + 
+      geom_line() +
+      scale_y_log10(breaks = c(0, 10, 100, 1000, 10000)) +
+      labs(x = "standard curve", 
+           y = "log(MFI)") +
+      facet_wrap(~protein) +
+      theme_bw() +
+      theme(axis.text.x = element_text(angle = 90, hjust = 1)) 
+  }
   
   ## Plot plate counts
-  plot_counts <- bead_counts %>% 
-    ggplot(mapping = aes(x = col, y = fct_rev(row), fill = colour), fill = summary)+
-    geom_tile(aes(height = 0.90, width = 0.90)) +
-    scale_x_continuous(breaks = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12))+
-    scale_fill_manual(values = c("sufficient beads" = "#91bfdb", "repeat" = "#d73027"))+
-    theme_linedraw()+
-    labs(x = "columns", y = "rows", fill = "")
+  if (nrow(bead_counts) > 0) {
+    plot_counts <- bead_counts %>% 
+      ggplot(mapping = aes(x = col, y = fct_rev(row), fill = colour), fill = summary)+
+      geom_tile(aes(height = 0.90, width = 0.90)) +
+      scale_x_continuous(breaks = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12))+
+      scale_fill_manual(values = c("sufficient beads" = "#91bfdb", "repeat" = "#d73027"))+
+      theme_linedraw()+
+      labs(x = "columns", y = "rows", fill = "")
+  }
   
   ## Plot blank sample MFI for each protein - if there is more than one blank sample label as "Blank1", "Blank2" etc
-  plot_blank <- blank_MFI %>% 
-    dplyr::select(-Location) %>% 
-    dplyr::mutate(Sample = paste0(Sample,1:n())) %>% 
-    tidyr::pivot_longer(-Sample, names_to = "protein", values_to = "MFI") %>% 
-    ggplot(aes(x = factor(protein), y = as.numeric(MFI), fill = Sample)) +
-    geom_bar(stat = "identity", position = "dodge") +
-    geom_hline(yintercept = 50, linetype = "dashed", color = "grey") +
-    labs(x = "protein", 
-         y = "MFI") +
-    theme_linedraw()  +
-    theme(axis.text.x = element_text(angle = 90, hjust = 1)) 
+  if (nrow(blank_MFI) > 0) {
+    plot_blank <- blank_MFI %>% 
+      dplyr::select(-Location) %>% 
+      dplyr::mutate(Sample = paste0(Sample,1:n())) %>% 
+      tidyr::pivot_longer(-Sample, names_to = "protein", values_to = "MFI") %>% 
+      ggplot(aes(x = factor(protein), y = as.numeric(MFI), fill = Sample)) +
+      geom_bar(stat = "identity", position = "dodge") +
+      geom_hline(yintercept = 50, linetype = "dashed", color = "grey") +
+      labs(x = "protein", 
+           y = "MFI") +
+      theme_linedraw()  +
+      theme(axis.text.x = element_text(angle = 90, hjust = 1)) 
+  }
   
   ## Plot model curves
   plots_model <- lapply(seq_along(model_results), function(x){
@@ -526,32 +603,40 @@ getRelativeAntibodyUnits = function(RAW_MFI_FILE_NAME, RAW_MFI_FILE_PATH,
   # saveRDS(std_curve, file = paste0(EXP_DIR, "STD_CURVES.rds"))
   
   # Save PDF of std curve plots
-  ggsave(plot_stdcurve,
-         filename = paste0(EXP_DIR,"STD_CURVES_PLOT.pdf"), 
-         height = 8, 
-         width = 12, 
-         units = "in")
+  if (!is.null(plot_stdcurve)) {
+    ggsave(plot_stdcurve,
+           filename = paste0(EXP_DIR,"STD_CURVES_PLOT.pdf"), 
+           height = 8, 
+           width = 12, 
+           units = "in")
+  }
   
   # Save PDF of bead count plot
-  ggsave(plot_counts,
-         filename = paste0(EXP_DIR,"PLATE_BEADS_COUNT_PLOT.pdf"),
-         height = 8,
-         width = 12,
-         units = "in")
+  if(!is.null(plot_counts)) {
+    ggsave(plot_counts,
+           filename = paste0(EXP_DIR,"PLATE_BEADS_COUNT_PLOT.pdf"),
+           height = 8,
+           width = 12,
+           units = "in")
+  }
   
   # Save PDF ofblank sample QC plot
-  ggsave(plot_blank,
-         filename = paste0(EXP_DIR,"BLANK_SAMPLE_PLOT.pdf"), 
-         height = 4, 
-         width = 6, 
-         units = "in")
+  if (!is.null(plot_blank)) {
+    ggsave(plot_blank,
+           filename = paste0(EXP_DIR,"BLANK_SAMPLE_PLOT.pdf"), 
+           height = 4, 
+           width = 6, 
+           units = "in")
+  }
   
   # Save PDF of model plots
-  ggsave(all_model_plots, 
-         filename = paste0(EXP_DIR,"MODEL_PLOTS.pdf"), 
-         height = 8.27, 
-         width = 11.69, 
-         units = "in")
+  if (!is.null(all_model_plots)) {
+    ggsave(all_model_plots, 
+           filename = paste0(EXP_DIR,"MODEL_PLOTS.pdf"), 
+           height = 8.27, 
+           width = 11.69, 
+           units = "in")
+  }
   
   ## Write to file
   write.csv(results.df.wide, 
@@ -625,25 +710,25 @@ getAntigenNames = function(RAU_Dilution, ANTIGEN_FILE_PATH, ANTIGEN_CSV){
 ################# Function 4: Random forest serology analysis
 
 getSeropositiveResults_RF = function(PATHWAY_1, 
-                                  RAU_user_id_columns, RAU_user_RAU_columns, 
-                                  RAU_RESULTS, 
-                                  ANTIGEN_FILE_PATH, ANTIGEN_CSV, 
-                                  CHECK_NAME, CHECK_ID, ID, DATE,
-                                  EXP_DIR,
-                                  MODEL_W16,
-                                  MODEL_W16_3_TARGETS,
-                                  MODEL_W16_EQUAL_TARGET,
-                                  MODEL_W16_HIGH_SP_TARGET,
-                                  MODEL_W16_HIGH_SE_TARGET,
-                                  MODEL_W16_OTHER_SE,
-                                  MODEL_W16_OTHER_SP,
-                                  MODEL_W47,
-                                  MODEL_W47_3_TARGETS,
-                                  MODEL_W47_EQUAL_TARGET,
-                                  MODEL_W47_HIGH_SP_TARGET,
-                                  MODEL_W47_HIGH_SE_TARGET,
-                                  MODEL_W47_OTHER_SE,
-                                  MODEL_W47_OTHER_SP){
+                                     RAU_user_id_columns, RAU_user_RAU_columns, 
+                                     RAU_RESULTS, 
+                                     ANTIGEN_FILE_PATH, ANTIGEN_CSV, 
+                                     CHECK_NAME, CHECK_ID, ID, DATE,
+                                     EXP_DIR,
+                                     MODEL_W16,
+                                     MODEL_W16_3_TARGETS,
+                                     MODEL_W16_EQUAL_TARGET,
+                                     MODEL_W16_HIGH_SP_TARGET,
+                                     MODEL_W16_HIGH_SE_TARGET,
+                                     MODEL_W16_OTHER_SE,
+                                     MODEL_W16_OTHER_SP,
+                                     MODEL_W47,
+                                     MODEL_W47_3_TARGETS,
+                                     MODEL_W47_EQUAL_TARGET,
+                                     MODEL_W47_HIGH_SP_TARGET,
+                                     MODEL_W47_HIGH_SE_TARGET,
+                                     MODEL_W47_OTHER_SE,
+                                     MODEL_W47_OTHER_SP){
   
   ##############################################################################################
   ##                                                                                          ##
@@ -1164,25 +1249,25 @@ getSeropositiveResults_RF = function(PATHWAY_1,
 ################# Function 5: SVM serology analysis
 
 getSeropositiveResults_SVM = function(PATHWAY_1, 
-                                     RAU_user_id_columns, RAU_user_RAU_columns, 
-                                     RAU_RESULTS, 
-                                     ANTIGEN_FILE_PATH, ANTIGEN_CSV, 
-                                     CHECK_NAME, CHECK_ID, ID, DATE,
-                                     EXP_DIR,
-                                     MODEL_W16,
-                                     MODEL_W16_3_TARGETS,
-                                     MODEL_W16_EQUAL_TARGET,
-                                     MODEL_W16_HIGH_SP_TARGET,
-                                     MODEL_W16_HIGH_SE_TARGET,
-                                     MODEL_W16_OTHER_SE,
-                                     MODEL_W16_OTHER_SP,
-                                     MODEL_W47,
-                                     MODEL_W47_3_TARGETS,
-                                     MODEL_W47_EQUAL_TARGET,
-                                     MODEL_W47_HIGH_SP_TARGET,
-                                     MODEL_W47_HIGH_SE_TARGET,
-                                     MODEL_W47_OTHER_SE,
-                                     MODEL_W47_OTHER_SP){
+                                      RAU_user_id_columns, RAU_user_RAU_columns, 
+                                      RAU_RESULTS, 
+                                      ANTIGEN_FILE_PATH, ANTIGEN_CSV, 
+                                      CHECK_NAME, CHECK_ID, ID, DATE,
+                                      EXP_DIR,
+                                      MODEL_W16,
+                                      MODEL_W16_3_TARGETS,
+                                      MODEL_W16_EQUAL_TARGET,
+                                      MODEL_W16_HIGH_SP_TARGET,
+                                      MODEL_W16_HIGH_SE_TARGET,
+                                      MODEL_W16_OTHER_SE,
+                                      MODEL_W16_OTHER_SP,
+                                      MODEL_W47,
+                                      MODEL_W47_3_TARGETS,
+                                      MODEL_W47_EQUAL_TARGET,
+                                      MODEL_W47_HIGH_SP_TARGET,
+                                      MODEL_W47_HIGH_SE_TARGET,
+                                      MODEL_W47_OTHER_SE,
+                                      MODEL_W47_OTHER_SP){
   
   ##############################################################################################
   ##                                                                                          ##
@@ -1370,30 +1455,30 @@ getSeropositiveResults_SVM = function(PATHWAY_1,
       # Create binary seropositivity value
       if(PATHWAY_1){
         SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:4],
-                                                    exp(RAU_Dilution_Subset), 
-                                                    SVM_MODEL_VOTES, 
-                                                    SEROPOSITIVE_79SE_79SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative"),
-                                                    SEROPOSITIVE_63SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative"),
-                                                    SEROPOSITIVE_90SE_59SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
+                                          exp(RAU_Dilution_Subset), 
+                                          SVM_MODEL_VOTES, 
+                                          SEROPOSITIVE_79SE_79SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative"),
+                                          SEROPOSITIVE_63SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative"),
+                                          SEROPOSITIVE_90SE_59SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
         
       }else{
         if(RAU_user_id_columns == 1){
           col_name = colnames(RAU_RESULTS)[1]
           SVM_SEROPOS = as.data.frame(cbind(as.character(RAU_RESULTS[,colnames(RAU_RESULTS)[1]]),
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      SEROPOSITIVE_79SE_79SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative"),
-                                                      SEROPOSITIVE_63SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative"),
-                                                      SEROPOSITIVE_90SE_59SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            SEROPOSITIVE_79SE_79SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative"),
+                                            SEROPOSITIVE_63SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative"),
+                                            SEROPOSITIVE_90SE_59SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
           colnames(SVM_SEROPOS) = c(col_name, colnames(SVM_SEROPOS[2:ncol(SVM_SEROPOS)]))
         }
         if(RAU_user_id_columns > 1){
           SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:as.integer(RAU_user_id_columns)],
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      SEROPOSITIVE_79SE_79SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative"),
-                                                      SEROPOSITIVE_63SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative"),
-                                                      SEROPOSITIVE_90SE_59SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            SEROPOSITIVE_79SE_79SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative"),
+                                            SEROPOSITIVE_63SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative"),
+                                            SEROPOSITIVE_90SE_59SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
         }
         
       }
@@ -1405,30 +1490,30 @@ getSeropositiveResults_SVM = function(PATHWAY_1,
       # Create binary seropositivity value
       if(PATHWAY_1){
         SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:4],
-                                                    exp(RAU_Dilution_Subset), 
-                                                    SVM_MODEL_VOTES, 
-                                                    SEROPOSITIVE_80SE_80SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative"),
-                                                    SEROPOSITIVE_64SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative"),
-                                                    SEROPOSITIVE_90SE_60SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
+                                          exp(RAU_Dilution_Subset), 
+                                          SVM_MODEL_VOTES, 
+                                          SEROPOSITIVE_80SE_80SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative"),
+                                          SEROPOSITIVE_64SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative"),
+                                          SEROPOSITIVE_90SE_60SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
         
       }else{
         if(RAU_user_id_columns == 1){
           col_name = colnames(RAU_RESULTS)[1]
           SVM_SEROPOS = as.data.frame(cbind(as.character(RAU_RESULTS[,colnames(RAU_RESULTS)[1]]),
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      SEROPOSITIVE_80SE_80SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative"),
-                                                      SEROPOSITIVE_64SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative"),
-                                                      SEROPOSITIVE_90SE_60SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            SEROPOSITIVE_80SE_80SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative"),
+                                            SEROPOSITIVE_64SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative"),
+                                            SEROPOSITIVE_90SE_60SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
           colnames(SVM_SEROPOS) = c(col_name, colnames(SVM_SEROPOS[2:ncol(SVM_SEROPOS)]))
         }
         if(RAU_user_id_columns > 1){
           SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:as.integer(RAU_user_id_columns)],
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      SEROPOSITIVE_80SE_80SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative"),
-                                                      SEROPOSITIVE_64SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative"),
-                                                      SEROPOSITIVE_90SE_60SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            SEROPOSITIVE_80SE_80SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative"),
+                                            SEROPOSITIVE_64SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative"),
+                                            SEROPOSITIVE_90SE_60SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
         }
       }
     }
@@ -1443,24 +1528,24 @@ getSeropositiveResults_SVM = function(PATHWAY_1,
       # Create binary seropositivity value
       if(PATHWAY_1){
         SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:4],
-                                                    exp(RAU_Dilution_Subset), 
-                                                    SVM_MODEL_VOTES, 
-                                                    SEROPOSITIVE_79SE_79SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative")), stringsAsFactors = F)
+                                          exp(RAU_Dilution_Subset), 
+                                          SVM_MODEL_VOTES, 
+                                          SEROPOSITIVE_79SE_79SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative")), stringsAsFactors = F)
         
       }else{
         if(RAU_user_id_columns == 1){
           col_name = colnames(RAU_RESULTS)[1]
           SVM_SEROPOS = as.data.frame(cbind(as.character(RAU_RESULTS[,colnames(RAU_RESULTS)[1]]),
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      SEROPOSITIVE_79SE_79SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative")), stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            SEROPOSITIVE_79SE_79SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative")), stringsAsFactors = F)
           colnames(SVM_SEROPOS) = c(col_name, colnames(SVM_SEROPOS[2:ncol(SVM_SEROPOS)]))
         }
         if(RAU_user_id_columns > 1){
           SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:as.integer(RAU_user_id_columns)],
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      SEROPOSITIVE_79SE_79SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative")), stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            SEROPOSITIVE_79SE_79SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative")), stringsAsFactors = F)
         }
         
       }
@@ -1472,24 +1557,24 @@ getSeropositiveResults_SVM = function(PATHWAY_1,
       # Create binary seropositivity value
       if(PATHWAY_1){
         SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:4],
-                                                    exp(RAU_Dilution_Subset), 
-                                                    SVM_MODEL_VOTES, 
-                                                    SEROPOSITIVE_80SE_80SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative")), stringsAsFactors = F)
+                                          exp(RAU_Dilution_Subset), 
+                                          SVM_MODEL_VOTES, 
+                                          SEROPOSITIVE_80SE_80SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative")), stringsAsFactors = F)
         
       }else{
         if(RAU_user_id_columns == 1){
           col_name = colnames(RAU_RESULTS)[1]
           SVM_SEROPOS = as.data.frame(cbind(as.character(RAU_RESULTS[,colnames(RAU_RESULTS)[1]]),
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      SEROPOSITIVE_80SE_80SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative")), stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            SEROPOSITIVE_80SE_80SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative")), stringsAsFactors = F)
           colnames(SVM_SEROPOS) = c(col_name, colnames(SVM_SEROPOS[2:ncol(SVM_SEROPOS)]))
         }
         if(RAU_user_id_columns > 1){
           SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:as.integer(RAU_user_id_columns)],
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      SEROPOSITIVE_80SE_80SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative")), stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            SEROPOSITIVE_80SE_80SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_1, "Positive", "Negative")), stringsAsFactors = F)
         }
       }
     }
@@ -1504,24 +1589,24 @@ getSeropositiveResults_SVM = function(PATHWAY_1,
       # Create binary seropositivity value
       if(PATHWAY_1){
         SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:4],
-                                                    exp(RAU_Dilution_Subset), 
-                                                    SVM_MODEL_VOTES, 
-                                                    SEROPOSITIVE_63SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative")), stringsAsFactors = F)
+                                          exp(RAU_Dilution_Subset), 
+                                          SVM_MODEL_VOTES, 
+                                          SEROPOSITIVE_63SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative")), stringsAsFactors = F)
         
       }else{
         if(RAU_user_id_columns == 1){
           col_name = colnames(RAU_RESULTS)[1]
           SVM_SEROPOS = as.data.frame(cbind(as.character(RAU_RESULTS[,colnames(RAU_RESULTS)[1]]),
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      SEROPOSITIVE_63SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative")), stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            SEROPOSITIVE_63SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative")), stringsAsFactors = F)
           colnames(SVM_SEROPOS) = c(col_name, colnames(SVM_SEROPOS[2:ncol(SVM_SEROPOS)]))
         }
         if(RAU_user_id_columns > 1){
           SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:as.integer(RAU_user_id_columns)],
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      SEROPOSITIVE_63SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative")), stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            SEROPOSITIVE_63SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative")), stringsAsFactors = F)
         }
         
       }
@@ -1533,24 +1618,24 @@ getSeropositiveResults_SVM = function(PATHWAY_1,
       # Create binary seropositivity value
       if(PATHWAY_1){
         SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:4],
-                                                    exp(RAU_Dilution_Subset), 
-                                                    SVM_MODEL_VOTES, 
-                                                    SEROPOSITIVE_64SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative")), stringsAsFactors = F)
+                                          exp(RAU_Dilution_Subset), 
+                                          SVM_MODEL_VOTES, 
+                                          SEROPOSITIVE_64SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative")), stringsAsFactors = F)
         
       }else{
         if(RAU_user_id_columns == 1){
           col_name = colnames(RAU_RESULTS)[1]
           SVM_SEROPOS = as.data.frame(cbind(as.character(RAU_RESULTS[,colnames(RAU_RESULTS)[1]]),
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      SEROPOSITIVE_64SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative")), stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            SEROPOSITIVE_64SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative")), stringsAsFactors = F)
           colnames(SVM_SEROPOS) = c(col_name, colnames(SVM_SEROPOS[2:ncol(SVM_SEROPOS)]))
         }
         if(RAU_user_id_columns > 1){
           SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:as.integer(RAU_user_id_columns)],
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      SEROPOSITIVE_64SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative")), stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            SEROPOSITIVE_64SE_90SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_2, "Positive", "Negative")), stringsAsFactors = F)
         }
       }
     }
@@ -1565,24 +1650,24 @@ getSeropositiveResults_SVM = function(PATHWAY_1,
       # Create binary seropositivity value
       if(PATHWAY_1){
         SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:4],
-                                                    exp(RAU_Dilution_Subset), 
-                                                    SVM_MODEL_VOTES, 
-                                                    SEROPOSITIVE_90SE_59SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
+                                          exp(RAU_Dilution_Subset), 
+                                          SVM_MODEL_VOTES, 
+                                          SEROPOSITIVE_90SE_59SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
         
       }else{
         if(RAU_user_id_columns == 1){
           col_name = colnames(RAU_RESULTS)[1]
           SVM_SEROPOS = as.data.frame(cbind(as.character(RAU_RESULTS[,colnames(RAU_RESULTS)[1]]),
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      SEROPOSITIVE_90SE_59SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            SEROPOSITIVE_90SE_59SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
           colnames(SVM_SEROPOS) = c(col_name, colnames(SVM_SEROPOS[2:ncol(SVM_SEROPOS)]))
         }
         if(RAU_user_id_columns > 1){
           SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:as.integer(RAU_user_id_columns)],
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      SEROPOSITIVE_90SE_59SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            SEROPOSITIVE_90SE_59SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
         }
         
       }
@@ -1594,24 +1679,24 @@ getSeropositiveResults_SVM = function(PATHWAY_1,
       # Create binary seropositivity value
       if(PATHWAY_1){
         SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:4],
-                                                    exp(RAU_Dilution_Subset), 
-                                                    SVM_MODEL_VOTES, 
-                                                    SEROPOSITIVE_90SE_60SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
+                                          exp(RAU_Dilution_Subset), 
+                                          SVM_MODEL_VOTES, 
+                                          SEROPOSITIVE_90SE_60SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
         
       }else{
         if(RAU_user_id_columns == 1){
           col_name = colnames(RAU_RESULTS)[1]
           SVM_SEROPOS = as.data.frame(cbind(as.character(RAU_RESULTS[,colnames(RAU_RESULTS)[1]]),
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      SEROPOSITIVE_90SE_60SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            SEROPOSITIVE_90SE_60SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
           colnames(SVM_SEROPOS) = c(col_name, colnames(SVM_SEROPOS[2:ncol(SVM_SEROPOS)]))
         }
         if(RAU_user_id_columns > 1){
           SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:as.integer(RAU_user_id_columns)],
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      SEROPOSITIVE_90SE_60SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            SEROPOSITIVE_90SE_60SP=ifelse(SVM_MODEL_VOTES <= CUTOFF_3, "Positive", "Negative")), stringsAsFactors = F)
         }
       }
     }
@@ -1626,27 +1711,27 @@ getSeropositiveResults_SVM = function(PATHWAY_1,
       # Create binary seropositivity value
       if(PATHWAY_1){
         SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:4],
-                                                    exp(RAU_Dilution_Subset), 
-                                                    SVM_MODEL_VOTES, 
-                                                    assign(paste0("SEROPOSITIVE_",as.character(round(as.numeric(SE_Other)*100,digits = 0)), "SE_",as.character(round(as.numeric(SP_Other)*100,digits = 0)),"SP"),ifelse(SVM_MODEL_VOTES <= CUTOFF_4, "Positive", "Negative"))), 
-                                              stringsAsFactors = F)
+                                          exp(RAU_Dilution_Subset), 
+                                          SVM_MODEL_VOTES, 
+                                          assign(paste0("SEROPOSITIVE_",as.character(round(as.numeric(SE_Other)*100,digits = 0)), "SE_",as.character(round(as.numeric(SP_Other)*100,digits = 0)),"SP"),ifelse(SVM_MODEL_VOTES <= CUTOFF_4, "Positive", "Negative"))), 
+                                    stringsAsFactors = F)
         
       }else{
         if(RAU_user_id_columns == 1){
           col_name = colnames(RAU_RESULTS)[1]
           SVM_SEROPOS = as.data.frame(cbind(as.character(RAU_RESULTS[,colnames(RAU_RESULTS)[1]]),
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      assign(paste0("SEROPOSITIVE_",as.character(round(as.numeric(SE_Other)*100,digits = 0)), "SE_",as.character(round(as.numeric(SP_Other)*100,digits = 0)),"SP"),ifelse(SVM_MODEL_VOTES <= CUTOFF_4, "Positive", "Negative"))), 
-                                                stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            assign(paste0("SEROPOSITIVE_",as.character(round(as.numeric(SE_Other)*100,digits = 0)), "SE_",as.character(round(as.numeric(SP_Other)*100,digits = 0)),"SP"),ifelse(SVM_MODEL_VOTES <= CUTOFF_4, "Positive", "Negative"))), 
+                                      stringsAsFactors = F)
           colnames(SVM_SEROPOS) = c(col_name, colnames(SVM_SEROPOS[2:ncol(SVM_SEROPOS)]))
         }
         if(RAU_user_id_columns > 1){
           SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:as.integer(RAU_user_id_columns)],
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      assign(paste0("SEROPOSITIVE_",as.character(round(as.numeric(SE_Other)*100,digits = 0)), "SE_",as.character(round(as.numeric(SP_Other)*100,digits = 0)),"SP"),ifelse(SVM_MODEL_VOTES <= CUTOFF_4, "Positive", "Negative"))), 
-                                                stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            assign(paste0("SEROPOSITIVE_",as.character(round(as.numeric(SE_Other)*100,digits = 0)), "SE_",as.character(round(as.numeric(SP_Other)*100,digits = 0)),"SP"),ifelse(SVM_MODEL_VOTES <= CUTOFF_4, "Positive", "Negative"))), 
+                                      stringsAsFactors = F)
         }
       }
     }
@@ -1657,27 +1742,27 @@ getSeropositiveResults_SVM = function(PATHWAY_1,
       # Create binary seropositivity value
       if(PATHWAY_1){
         SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:4],
-                                                    exp(RAU_Dilution_Subset), 
-                                                    SVM_MODEL_VOTES, 
-                                                    assign(paste0("SEROPOSITIVE_",as.character(round(as.numeric(SE_Other)*100,digits = 0)), "SE_",as.character(round(as.numeric(SP_Other)*100,digits = 0)),"SP"),ifelse(SVM_MODEL_VOTES <= CUTOFF_4, "Positive", "Negative"))), 
-                                              stringsAsFactors = F)
+                                          exp(RAU_Dilution_Subset), 
+                                          SVM_MODEL_VOTES, 
+                                          assign(paste0("SEROPOSITIVE_",as.character(round(as.numeric(SE_Other)*100,digits = 0)), "SE_",as.character(round(as.numeric(SP_Other)*100,digits = 0)),"SP"),ifelse(SVM_MODEL_VOTES <= CUTOFF_4, "Positive", "Negative"))), 
+                                    stringsAsFactors = F)
         
       }else{
         if(RAU_user_id_columns == 1){
           col_name = colnames(RAU_RESULTS)[1]
           SVM_SEROPOS = as.data.frame(cbind(as.character(RAU_RESULTS[,colnames(RAU_RESULTS)[1]]),
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      assign(paste0("SEROPOSITIVE_",as.character(round(as.numeric(SE_Other)*100,digits = 0)), "SE_",as.character(round(as.numeric(SP_Other)*100,digits = 0)),"SP"),ifelse(SVM_MODEL_VOTES <= CUTOFF_4, "Positive", "Negative"))), 
-                                                stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            assign(paste0("SEROPOSITIVE_",as.character(round(as.numeric(SE_Other)*100,digits = 0)), "SE_",as.character(round(as.numeric(SP_Other)*100,digits = 0)),"SP"),ifelse(SVM_MODEL_VOTES <= CUTOFF_4, "Positive", "Negative"))), 
+                                      stringsAsFactors = F)
           colnames(SVM_SEROPOS) = c(col_name, colnames(SVM_SEROPOS[2:ncol(SVM_SEROPOS)]))
         }
         if(RAU_user_id_columns > 1){
           SVM_SEROPOS = as.data.frame(cbind(RAU_RESULTS[,1:as.integer(RAU_user_id_columns)],
-                                                      exp(RAU_Dilution_Subset), 
-                                                      SVM_MODEL_VOTES, 
-                                                      assign(paste0("SEROPOSITIVE_",as.character(round(as.numeric(SE_Other)*100,digits = 0)), "SE_",as.character(round(as.numeric(SP_Other)*100,digits = 0)),"SP"),ifelse(SVM_MODEL_VOTES <= CUTOFF_4, "Positive", "Negative"))), 
-                                                stringsAsFactors = F)
+                                            exp(RAU_Dilution_Subset), 
+                                            SVM_MODEL_VOTES, 
+                                            assign(paste0("SEROPOSITIVE_",as.character(round(as.numeric(SE_Other)*100,digits = 0)), "SE_",as.character(round(as.numeric(SP_Other)*100,digits = 0)),"SP"),ifelse(SVM_MODEL_VOTES <= CUTOFF_4, "Positive", "Negative"))), 
+                                      stringsAsFactors = F)
         }
       }
     }
diff --git a/SHINY_APP.R b/SHINY_APP.R
index 0a3a99c..53f5381 100644
--- a/SHINY_APP.R
+++ b/SHINY_APP.R
@@ -56,7 +56,7 @@ ui <- fluidPage(
              p("The Pv SeroTAT Tool has been developed by Ivo Mueller's research groups at the Institut Pasteur and Walter and Eliza Hall Institut of Medical Research. The tool is a patented diagnostic test based on validated serological markers of recent",tags$em("Plasmodium vivax")," infections and hypnozoite carriage. The tool uses machine learning classification algorithms (Random Forest (RF) and Support Vector Machine (SVM)) to predict sero-positivity with varying performance."),
              p("Validation of this tool using Random Forest can be found in the", a("Nature Medicine Publication", href="https://www.nature.com/articles/s41591-020-0841-4")),
              p("Please contact on how to cite this tool in publications."),
-             p(em("Follow steps 1 - 6 to process Luminex MFI or Relative Antibody Unit (RAU) data and run the diagnostic test.")),
+             p(em("Follow steps 1 - 6 to process Luminex/BioPlex MFI or Relative Antibody Unit (RAU) data and run the diagnostic test.")),
              hr()),
     tabPanel("Biomarkers of exposure",
              br(),
@@ -216,7 +216,7 @@ ui <- fluidPage(
                               fileInput("MFI_file",
                                         h5("4.1 Load raw MFI file (required)"),
                                         accept = c(".csv", ".xlsx")),
-                              helpText("Note: a (.csv) or (.xlsx) file of raw outputs of the Luminex-MagPix machine.
+                              helpText("Note: a (.csv) or (.xlsx) file of raw outputs of the Luminex-MagPix or BioPlex machine.
                                        If you encounter errors when loading a (.csv) file, convert it to (.xlsx) in Excel and load the (.xlsx) file instead.")),
              conditionalPanel("input.radio_data == 1",
                               textInput("MFI_num_antigens",
-- 
GitLab