updating script

scripts/dto_preparation.R

@@ -342,11 +342,12 @@ all_pr_dtos = list(
 
 # Write out DTO ranked lists
 
-results_basedir = "~/htcf_local/dto"
+write_out_pr_dto_lists = function(pr_dataset_name,
+                                  binding_pr_set_name,
+                                  all_pr_dtos_list,
+                                  base_outdir=here("results/dto")) {
 
-write_out_pr_dto_lists = function(pr_dataset_name, binding_pr_set_name, all_pr_dtos_list) {
-
-    output_path = file.path(results_basedir, pr_dataset_name)
+    output_path = file.path(base_outdir, pr_dataset_name)
 
     binding_pr_set = all_pr_dtos_list[[pr_dataset_name]][[binding_pr_set_name]]
 
@@ -395,8 +396,11 @@ write_out_pr_dto_lists = function(pr_dataset_name, binding_pr_set_name, all_pr_d
 }
 
 # Generalized function to write background
-write_pr_background = function(pr_dataset_name, binding_pr_set_name, background_list) {
-    output_path = file.path(results_basedir, pr_dataset_name)
+write_pr_background = function(pr_dataset_name,
+                               binding_pr_set_name,
+                               background_list,
+                               base_outdir = here("results/dto")) {
+    output_path = file.path(base_outdir, pr_dataset_name)
 
     background_list[[binding_pr_set_name]] %>%
         write_csv(file.path(output_path, binding_pr_set_name, "background.csv"),
@@ -404,32 +408,62 @@ write_pr_background = function(pr_dataset_name, binding_pr_set_name, background_
 }
 
 # Generalized function to create lookups
-create_pr_lookups = function(pr_dataset_name, binding_pr_set_name, all_pr_dtos_list, scratch_path = "/scratch/mblab/chasem/dto") {
+create_pr_lookups = function(pr_dataset_name, binding_pr_set_name,
+                             all_pr_dtos_list,
+                             scratch_path = "/scratch/mblab/chasem/dto") {
+    # Get binding and PR sample IDs
+    binding_samples = all_pr_dtos_list[[pr_dataset_name]][[binding_pr_set_name]]$binding %>%
+        ungroup() %>%
+        dplyr::select(sample_id, regulator_locus_tag) %>%
+        distinct() %>%
+        dplyr::rename(binding_id = sample_id)
 
-    lookup_df = all_pr_dtos_list[[pr_dataset_name]][[binding_pr_set_name]]$binding %>%
+    pr_samples = all_pr_dtos_list[[pr_dataset_name]][[binding_pr_set_name]]$pr %>%
         ungroup() %>%
         dplyr::select(sample_id, regulator_locus_tag) %>%
         distinct() %>%
-        dplyr::rename(binding_id = sample_id) %>%
-        left_join(all_pr_dtos_list[[pr_dataset_name]][[binding_pr_set_name]]$pr %>%
-                      ungroup() %>%
-                      dplyr::select(sample_id, regulator_locus_tag) %>%
-                      distinct() %>%
-                      dplyr::rename(pr_id = sample_id)) %>%
-        mutate(binding = file.path(scratch_path, pr_dataset_name,
-                                   binding_pr_set_name, "binding",
-                                   paste0(binding_id, ".csv")),
-               pr_effect = file.path(scratch_path, pr_dataset_name,
-                                     binding_pr_set_name, "pr", "effect",
-                                     paste0(pr_id, ".csv")),
-               pr_pvalue = file.path(scratch_path, pr_dataset_name,
-                                     binding_pr_set_name, "pr", "pvalue",
-                                     paste0(pr_id, ".csv"))) %>%
+        dplyr::rename(pr_id = sample_id)
+
+    # Full join to identify incomplete cases - use relationship = "many-to-many"
+    lookup_df = binding_samples %>%
+        full_join(pr_samples, by = "regulator_locus_tag", relationship = "many-to-many") %>%
+        mutate(binding = if_else(!is.na(binding_id),
+                                 file.path(scratch_path, pr_dataset_name,
+                                           binding_pr_set_name, "binding",
+                                           paste0(binding_id, ".csv")),
+                                 NA_character_),
+               pr_effect = if_else(!is.na(pr_id),
+                                   file.path(scratch_path, pr_dataset_name,
+                                             binding_pr_set_name, "pr", "effect",
+                                             paste0(pr_id, ".csv")),
+                                   NA_character_),
+               pr_pvalue = if_else(!is.na(pr_id),
+                                   file.path(scratch_path, pr_dataset_name,
+                                             binding_pr_set_name, "pr", "pvalue",
+                                             paste0(pr_id, ".csv")),
+                                   NA_character_))
+
+    # Separate complete and incomplete cases
+    complete_lookup = lookup_df %>%
+        filter(!is.na(binding_id) & !is.na(pr_id)) %>%
         select(binding, pr_effect, pr_pvalue)
 
-    return(lookup_df)
+    incomplete_after_filtering = lookup_df %>%
+        filter(is.na(binding_id) | is.na(pr_id)) %>%
+        mutate(missing_type = case_when(
+            is.na(binding_id) & is.na(pr_id) ~ "both",
+            is.na(binding_id) ~ "binding",
+            is.na(pr_id) ~ "pr",
+            TRUE ~ "unknown"
+        )) %>%
+        select(regulator_locus_tag, binding_id, pr_id, missing_type) %>%
+        distinct()  # Add distinct here too to avoid duplicate incomplete rows
+
+    return(list(
+        lookup = complete_lookup,
+        incomplete_after_filtering = incomplete_after_filtering
+    ))
 }
-
 # Create background lists for all PR datasets (if not already created)
 all_pr_backgrounds = map(names(all_pr_dtos), ~{
     map(binding_data, function(bd) {
@@ -443,25 +477,50 @@ all_pr_backgrounds = map(names(all_pr_dtos), ~{
 names(all_pr_backgrounds) = names(all_pr_dtos)
 
 # Write out all DTOs for all PR datasets
+lookup_results = list()
+
+dto_input_outdir = here("results/dto")
 for (pr_name in names(all_pr_dtos)) {
+    lookup_results[[pr_name]] = list()
+
     for (binding_name in names(all_pr_dtos[[pr_name]])) {
         write_out_pr_dto_lists(pr_name, binding_name, all_pr_dtos)
         write_pr_background(pr_name, binding_name, all_pr_backgrounds[[pr_name]])
 
-        create_pr_lookups(pr_name, binding_name, all_pr_dtos) %>%
-            write_tsv(file.path(results_basedir, pr_name, binding_name, "lookup.txt"),
+        lookup_result = create_pr_lookups(pr_name, binding_name, all_pr_dtos)
+        lookup_results[[pr_name]][[binding_name]] = lookup_result
+
+        # Write complete lookups only
+        lookup_result$lookup %>%
+            write_tsv(file.path(dto_input_outdir, pr_name, binding_name, "lookup.txt"),
                       col_names = FALSE)
+
+        # Write incomplete cases for reference
+        if (nrow(lookup_result$incomplete_after_filtering) > 0) {
+            lookup_result$incomplete_after_filtering %>%
+                write_csv(file.path(dto_input_outdir, pr_name, binding_name, "incomplete.csv"))
+        }
     }
 }
 
-dto_results_path_list = list.files(results_basedir,
+# Summary of incomplete cases across all datasets
+incomplete_summary = map_dfr(names(lookup_results), ~{
+    map_dfr(names(lookup_results[[.x]]), function(binding_name) {
+        lookup_results[[.x]][[binding_name]]$incomplete_after_filtering %>%
+            mutate(pr_dataset = .x, binding_dataset = binding_name)
+    })
+})
+
+print(incomplete_summary %>% count(pr_dataset, binding_dataset, missing_type))
+
+dto_results_path_list = list.files(base_outdir,
                               "*.json",
                               recursive = TRUE)
 
 ## Parse the results -- note that this needs to be adjusted for the
 ## expanded set of perturbation response
 
-dto_results_frames_list = map(file.path(results_basedir, dto_results_path_list),
+dto_results_frames_list = map(file.path(base_outdir, dto_results_path_list),
                               ~as_tibble(jsonlite::read_json(.x)))
 names(dto_results_frames_list) = dto_results_path_list
 dto_results_frame = bind_rows(dto_results_frames_list, .id = 'path')