Refine df filtering in calculate_interaction_zscores.R

workflow/apps/r/calculate_interaction_zscores.R

@@ -113,33 +113,37 @@ load_and_preprocess_data <- function(easy_results_file, genes) {
     stop("Error reading file: ", easy_results_file, "\n", e$message)
   })
 
+  # Convert specific columns to numeric
   numeric_columns <- c("Col", "Row", "l", "K", "r", "Scan", "AUC96", "LstBackgrd", "X1stBackgrd")
   df[numeric_columns[numeric_columns %in% colnames(df)]] <- 
     suppressWarnings(lapply(df[numeric_columns[numeric_columns %in% colnames(df)]], as.numeric))
 
+  # Apply initial filters and mutating steps as in the original script
   df <- df %>%
-    filter(!.[[1]] %in% c("", "Scan")) %>%
+    mutate(L = if ("l" %in% colnames(.)) l else {warning("Missing column: l"); NA}) %>%
-    filter(Gene != "BLANK" & Gene != "Blank" & ORF != "Blank" & Gene != "blank") %>%
+    mutate(AUC = if ("AUC96" %in% colnames(.)) AUC96 else {warning("Missing column: AUC96"); NA}) %>%
-    filter(Drug != "BMH21") %>%
+    filter(!.[[1]] %in% c("", "Scan")) %>%            # Filter out empty or Scan rows
-    mutate(
+    # {if ("Conc" %in% colnames(.)) filter(., Conc != "0ug/mL") else .} %>%
-      L = if ("l" %in% colnames(.)) l else {warning("Missing column: l"); NA},
+    filter(Gene != "BLANK" & Gene != "Blank" & ORF != "Blank" & Gene != "blank") %>%  # Remove blank genes
-      AUC = if ("AUC96" %in% colnames(.)) AUC96 else {warning("Missing column: AUC96"); NA},
+    filter(Drug != "BMH21") %>%                       # Filter out specific drugs if necessary
-      conc_num = if ("Conc" %in% colnames(.)) as.numeric(gsub("[^0-9\\.]", "", Conc)) else NA,
+    filter(!is.na(ORF) & ORF != "") %>%               # Ensure ORF is not NA or empty
-      delta_bg = if (all(c("X1stBackgrd", "LstBackgrd") %in% colnames(.)))
+    mutate(OrfRep = ifelse(ORF == "YDL227C", "YDL227C", OrfRep)) %>%
-        LstBackgrd - X1stBackgrd else {warning("Missing columns for delta_bg calculation"); NA},
+    mutate(conc_num = as.numeric(gsub("[^0-9\\.]", "", Conc))) %>%  # Extract numeric drug concentrations
-      GeneName = vapply(ORF, function(orf) {
+    filter(!is.na(LstBackgrd) & !is.na(X1stBackgrd)) %>%  # Ensure finite background values
+    mutate(delta_bg = LstBackgrd - X1stBackgrd) %>%  # Calculate delta background
+    filter(!is.na(L) & !is.na(K) & !is.na(r) & !is.na(AUC96)) %>%  # Ensure finite measurements
+    mutate(GeneName = vapply(ORF, function(orf) {
       gene_name <- genes %>% filter(ORF == orf) %>% pull(GeneName)
       ifelse(is.null(gene_name) || gene_name == "" || length(gene_name) == 0, orf, gene_name)
-      }, character(1)),
+    }, character(1)))
-      OrfRep = ifelse(ORF == "YDL227C", "YDL227C", OrfRep)
-    ) # %>%
-    # mutate(across(everything(), ~na_if(., "")))
 
   if (nrow(df) == 0) warning("Dataframe is empty after filtering")
 
   return(df)
 }
 
+
+
 create_and_publish_plot <- function(df, var, plot_type, out_dir_qc, suffix = "") {
   if (!("Scan" %in% colnames(df))) {
     warning("'Scan' column is not present in the data. Skipping this plot.")
@@ -182,7 +186,6 @@ publish_summary_stats <- function(df, variables, out_dir) {
   fwrite(summary_stats_df, file.path(out_dir, "summary_stats_all_strains.csv"), row.names = FALSE)
 }
 
-# Interaction scores publication
 publish_interaction_scores <- function(df, out_dir) {
   interaction_scores <- df %>%
     dplyr::group_by(OrfRep) %>%
@@ -239,6 +242,8 @@ deletion_suppressors_K <- interaction_scores[interaction_scores$mean_K >= 2, ]
   return(interaction_scores)  # Return the updated data frame with rank columns
 }
 
+
+
 publish_zscores <- function(df, out_dir) {
   zscores <- df %>%
     dplyr::mutate(
@@ -282,6 +287,8 @@ generate_and_publish_qc <- function(df, delta_bg_tolerance, out_dir_qc) {
   })
 }
 
+
+
 # Create rank plots
 create_rank_plots <- function(interaction_scores, out_dir) {
   rank_vars <- c("l_rank", "k_rank", "r_rank", "auc_rank")
@@ -305,7 +312,6 @@ create_rank_plots <- function(interaction_scores, out_dir) {
   })
 }
 
-
 create_correlation_plot <- function(interaction_scores, out_dir) {
   # Check for non-finite values and remove them from the dataset
   interaction_scores <- interaction_scores %>%
@@ -348,6 +354,7 @@ process_experiment <- function(exp_name, exp_dir, genes, output_dir) {
   dir.create(out_dir, showWarnings = FALSE, recursive = TRUE)
   dir.create(out_dir_qc, showWarnings = FALSE)
 
+  # Load and preprocess the data
   data <- load_and_preprocess_data(args$easy_results_file, genes)
 
   # Calculate delta background tolerance