adding cc, rossi, mahendrawada mindel promoter dto

README.md

@@ -11,6 +11,63 @@ pretty_name: "Yeast Direct Target Overlap Analysis"
 size_categories:
   - 1K<n<10K
 
+features:
+- applies_to:
+    - dto
+    - dto_rossi_cc_mahendrawada_mindel
+    fields:
+    - name: pr_ranking_column
+      dtype:
+        class_label: ["log2fc", "pvalue"]
+      description: >-
+        Describes which column, effect (log2fc) or pvalue was used
+        in ranking the perturbation response data. see scripts/dto_preparation.R
+        for more info on each dataset
+    - name: binding_rank_threshold
+      dtype: float64
+      description: >-
+        Rank threshold used for the binding dataset in the DTO analysis. This
+        represents the rank cutoff that maximizes overlap significance between
+        binding and perturbation datasets.
+      role: quantitative_measure
+    - name: perturbation_rank_threshold
+      dtype: float64
+      description: >-
+        Rank threshold used for the perturbation dataset in the DTO analysis.
+        This represents the rank cutoff that maximizes overlap significance
+        between binding and perturbation datasets.
+      role: quantitative_measure
+    - name: binding_set_size
+      dtype: int64
+      description: >-
+        Number of targets in the binding dataset at the optimal rank threshold.
+        This is the size of the set used to calculate overlap with the
+        perturbation dataset.
+      role: quantitative_measure
+    - name: perturbation_set_size
+      dtype: int64
+      description: >-
+        Number of targets in the perturbation dataset at the optimal rank
+        threshold. This is the size of the set used to calculate overlap with
+        the binding dataset.
+      role: quantitative_measure
+    - name: dto_fdr
+      dtype: float64
+      description: >-
+        False discovery rate (FDR) for the direct target overlap test. Lower
+        values indicate more significant overlap between binding and
+        perturbation target sets. Missing values (NA) indicate insufficient
+        data for DTO analysis.
+      role: quantitative_measure
+    - name: dto_empirical_pvalue
+      dtype: float64
+      description: >-
+        Empirical p-value from permutation testing for the direct target
+        overlap. This represents the probability of observing the observed
+        overlap by chance. Missing values (NA) indicate insufficient data for
+        DTO analysis.
+      role: quantitative_measure
+
 configs:
   - config_name: dto
     description: >-
@@ -38,61 +95,38 @@ configs:
             'repo_id;config_name;sample_id' (e.g.,
             'BrentLab/Hackett_2020;hackett_2020;200')
           role: source_sample
-        - name: pr_ranking_column
-          dtype:
-            class_label: ["log2fc", "pvalue"]
-          description: >-
-            Describes which column, effect (log2fc) or pvalue was used
-            in ranking the perturbation response data. see scripts/dto_preparation.R
-            for more info on each dataset
-        - name: binding_rank_threshold
-          dtype: float64
-          description: >-
-            Rank threshold used for the binding dataset in the DTO analysis. This
-            represents the rank cutoff that maximizes overlap significance between
-            binding and perturbation datasets.
-          role: quantitative_measure
-        - name: perturbation_rank_threshold
-          dtype: float64
-          description: >-
-            Rank threshold used for the perturbation dataset in the DTO analysis.
-            This represents the rank cutoff that maximizes overlap significance
-            between binding and perturbation datasets.
-          role: quantitative_measure
-        - name: binding_set_size
-          dtype: int64
-          description: >-
-            Number of targets in the binding dataset at the optimal rank threshold.
-            This is the size of the set used to calculate overlap with the
-            perturbation dataset.
-          role: quantitative_measure
-        - name: perturbation_set_size
-          dtype: int64
-          description: >-
-            Number of targets in the perturbation dataset at the optimal rank
-            threshold. This is the size of the set used to calculate overlap with
-            the binding dataset.
-          role: quantitative_measure
-        - name: dto_fdr
-          dtype: float64
+      partitioning:
+        enabled: true
+        partition_by: ["binding_repo_dataset", "perturbation_repo_dataset"]
+        path_template: "dto/binding_repo_dataset={binding_repo_dataset}/perturbation_repo_dataset={perturbation_repo_dataset}/*.parquet"
+  - config_name: dto_rossi_cc_mahendrawada_mindel
+    description: >-
+      Only the rossi, mahendrawada and cc datasets, which are called over the Barkai
+      Mindel promoter regions.
+    dataset_type: comparative
+    data_files:
+      - split: train
+        path: dto_rossi_cc_mahendrawada_mindel/*/*/*.parquet
+    dataset_info:
+      features:
+        - name: binding_id
+          dtype: string
           description: >-
-            False discovery rate (FDR) for the direct target overlap test. Lower
-            values indicate more significant overlap between binding and
-            perturbation target sets. Missing values (NA) indicate insufficient
-            data for DTO analysis.
-          role: quantitative_measure
-        - name: dto_empirical_pvalue
-          dtype: float64
+            Composite sample identifier for the binding experiment in format
+            'repo_id;config_name;sample_id' (e.g.,
+            'BrentLab/callingcards;annotated_features;1')
+          role: source_sample
+        - name: perturbation_id
+          dtype: string
           description: >-
-            Empirical p-value from permutation testing for the direct target
-            overlap. This represents the probability of observing the observed
-            overlap by chance. Missing values (NA) indicate insufficient data for
-            DTO analysis.
-          role: quantitative_measure
+            Composite sample identifier for the perturbation experiment in format
+            'repo_id;config_name;sample_id' (e.g.,
+            'BrentLab/Hackett_2020;hackett_2020;200')
+          role: source_sample
       partitioning:
         enabled: true
         partition_by: ["binding_repo_dataset", "perturbation_repo_dataset"]
-        path_template: "dto/binding_repo_dataset={binding_repo_dataset}/perturbation_repo_dataset={perturbation_repo_dataset}/*.parquet"
+        path_template: "dto_rossi_cc_mahendrawada_mindel/binding_repo_dataset={binding_repo_dataset}/perturbation_repo_dataset={perturbation_repo_dataset}/*.parquet"
 ---
 
 # Yeast Comparative Analysis

dto_rossi_cc_mahendrawada_mindel/binding_repo_dataset=callingcards-annotated_features/perturbation_repo_dataset=hackett_2020-hackett_2020/part-0.parquet

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dto_rossi_cc_mahendrawada_mindel/binding_repo_dataset=callingcards-annotated_features/perturbation_repo_dataset=hu_2007_reimand_2010-hu_2007_reimand_2010/part-0.parquet

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dto_rossi_cc_mahendrawada_mindel/binding_repo_dataset=callingcards-annotated_features/perturbation_repo_dataset=hughes_2006-knockout/part-0.parquet

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dto_rossi_cc_mahendrawada_mindel/binding_repo_dataset=callingcards-annotated_features/perturbation_repo_dataset=hughes_2006-overexpression/part-0.parquet

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dto_rossi_cc_mahendrawada_mindel/binding_repo_dataset=callingcards-annotated_features/perturbation_repo_dataset=kemmeren_2014-kemmeren_2014/part-0.parquet

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dto_rossi_cc_mahendrawada_mindel/binding_repo_dataset=callingcards-annotated_features/perturbation_repo_dataset=mahendrawada_2025-rnaseq_reprocessed/part-0.parquet

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dto_rossi_cc_mahendrawada_mindel/binding_repo_dataset=mahendrawada_2025-chec_mahendrawada_m2025_af_combined/perturbation_repo_dataset=hackett_2020-hackett_2020/part-0.parquet

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dto_rossi_cc_mahendrawada_mindel/binding_repo_dataset=rossi_2021-rossi_2021_af_combined/perturbation_repo_dataset=hackett_2020-hackett_2020/part-0.parquet

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dto_rossi_cc_mahendrawada_mindel/binding_repo_dataset=rossi_2021-rossi_2021_af_combined/perturbation_repo_dataset=hu_2007_reimand_2010-hu_2007_reimand_2010/part-0.parquet

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dto_rossi_cc_mahendrawada_mindel/binding_repo_dataset=rossi_2021-rossi_2021_af_combined/perturbation_repo_dataset=mahendrawada_2025-rnaseq_reprocessed/part-0.parquet

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scripts/dto_preparation.R

@@ -74,38 +74,58 @@ perturbation_response_data = list(
         mutate(pvalue = 0)
     )
 
-composite_cc = arrow::open_dataset("~/code/hf/callingcards/annotated_features_combined") %>%
-    collect() %>%
-    left_join(arrow::read_parquet("~/code/hf/callingcards/annotated_features_combined_meta.parquet")) %>%
-    dplyr::rename(id = genome_map_id_set)
+# composite_cc = arrow::open_dataset("~/code/hf/callingcards/annotated_features_combined") %>%
+#     collect() %>%
+#     left_join(arrow::read_parquet("~/code/hf/callingcards/annotated_features_combined_meta.parquet")) %>%
+#     dplyr::rename(id = genome_map_id_set)
+
+# single_cc_meta = arrow::read_parquet("~/code/hf/callingcards/annotated_features_meta.parquet") %>%
+#     filter(batch != "composite")
+#
+# single_cc = arrow::open_dataset("~/code/hf/callingcards/annotated_features") %>%
+#     filter(id %in% single_cc_meta$id) %>%
+#     collect() %>%
+#     left_join(single_cc_meta) %>%
+#     mutate(id = as.character(id))
 
-single_cc_meta = arrow::read_parquet("~/code/hf/callingcards/annotated_features_meta.parquet") %>%
-    filter(batch != "composite")
 
-single_cc = arrow::open_dataset("~/code/hf/callingcards/annotated_features") %>%
-    filter(id %in% single_cc_meta$id) %>%
+single_cc_meta = arrow::read_parquet("~/code/hf/callingcards/genome_map_meta.parquet")
+
+single_cc = arrow::open_dataset("~/code/hf/callingcards/annotated_feature_mindel") %>%
     collect() %>%
-    left_join(single_cc_meta) %>%
-    mutate(id = as.character(id))
+    left_join(single_cc_meta,
+              by = c("genome_map_id" = "id", "batch" = "batch")) |>
+    dplyr::rename(id = genome_map_id)
 
 # note: filter these for the mahendrawada features, too. Restricts analysis
 # to only non dubious genomic loci
+# binding_data = list(
+#     cc = single_cc %>%
+#         select(intersect(colnames(.), colnames(composite_cc))) %>%
+#         bind_rows(composite_cc %>%
+#                       select(intersect(colnames(.), colnames(single_cc)))) %>%
+#         filter(target_locus_tag %in% mahendrawada_features$locus_tag),
+#     harbison = arrow::read_parquet("~/code/hf/harbison_2004/harbison_2004.parquet") %>%
+#         replace_na(list(effect = 0, pvalue = 1)) %>%
+#         group_by(sample_id, target_locus_tag) %>%
+#         slice_max(abs(effect), n = 1, with_ties = FALSE) %>%
+#         ungroup() %>%
+#         filter(target_locus_tag %in% mahendrawada_features$locus_tag),
+#     chipexo = arrow::read_parquet("~/code/hf/rossi_2021/rossi_2021_af_combined.parquet") %>%
+#         left_join(arrow::read_parquet("~/code/hf/rossi_2021/rossi_2021_metadata_sample.parquet")) %>%
+#         filter(target_locus_tag %in% mahendrawada_features$locus_tag),
+#     mahendrawada_chec = arrow::read_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined.parquet") %>%
+#         left_join(arrow::read_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined_meta.parquet")) %>%
+#         filter(target_locus_tag %in% mahendrawada_features$locus_tag)
+# )
+
 binding_data = list(
     cc = single_cc %>%
-        select(intersect(colnames(.), colnames(composite_cc))) %>%
-        bind_rows(composite_cc %>%
-                      select(intersect(colnames(.), colnames(single_cc)))) %>%
         filter(target_locus_tag %in% mahendrawada_features$locus_tag),
-    harbison = arrow::read_parquet("~/code/hf/harbison_2004/harbison_2004.parquet") %>%
-        replace_na(list(effect = 0, pvalue = 1)) %>%
-        group_by(sample_id, target_locus_tag) %>%
-        slice_max(abs(effect), n = 1, with_ties = FALSE) %>%
-        ungroup() %>%
-        filter(target_locus_tag %in% mahendrawada_features$locus_tag),
-    chipexo = arrow::read_parquet("~/code/hf/rossi_2021/rossi_2021_af_combined.parquet") %>%
+    chipexo = arrow::read_parquet("~/code/hf/rossi_2021/rossi_2021_af_combined_mindel.parquet") %>%
         left_join(arrow::read_parquet("~/code/hf/rossi_2021/rossi_2021_metadata_sample.parquet")) %>%
         filter(target_locus_tag %in% mahendrawada_features$locus_tag),
-    mahendrawada_chec = arrow::read_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined.parquet") %>%
+    mahendrawada_chec = arrow::read_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined_mindel.parquet") %>%
         left_join(arrow::read_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined_meta.parquet")) %>%
         filter(target_locus_tag %in% mahendrawada_features$locus_tag)
 )
@@ -165,29 +185,29 @@ create_pr_dto = function(pr_data, pr_effect_col, pr_pval_col, binding_data_list)
                 pull(target_locus_tag) %>%
                 unique()),
 
-        harbison = list(
-            binding = binding_data_list$harbison %>%
-                filter(regulator_locus_tag != target_locus_tag) %>%
-                filter(pvalue <= 0.1) %>%
-                filter(regulator_locus_tag %in% unique(pr_standardized$regulator_locus_tag),
-                       target_locus_tag %in% unique(pr_standardized$target_locus_tag)) %>%
-                group_by(sample_id) %>%
-                arrange(desc(effect)) %>%
-                mutate(pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
-                group_by(sample_id),
-            pr = pr_standardized %>%
-                filter(pvalue <= 0.1) %>%
-                filter(regulator_locus_tag %in% unique(binding_data_list$harbison$regulator_locus_tag),
-                       target_locus_tag %in% unique(binding_data_list$harbison$target_locus_tag)) %>%
-                group_by(sample_id) %>%
-                mutate(abs_effect_rank = rank(-abs(effect), ties.method = 'min'),
-                       pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
-                group_by(sample_id),
-            background = pr_standardized %>%
-                filter(regulator_locus_tag %in% unique(binding_data_list$harbison$regulator_locus_tag),
-                       target_locus_tag %in% unique(binding_data_list$harbison$target_locus_tag)) %>%
-                pull(target_locus_tag) %>%
-                unique()),
+        # harbison = list(
+        #     binding = binding_data_list$harbison %>%
+        #         filter(regulator_locus_tag != target_locus_tag) %>%
+        #         filter(pvalue <= 0.1) %>%
+        #         filter(regulator_locus_tag %in% unique(pr_standardized$regulator_locus_tag),
+        #                target_locus_tag %in% unique(pr_standardized$target_locus_tag)) %>%
+        #         group_by(sample_id) %>%
+        #         arrange(desc(effect)) %>%
+        #         mutate(pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
+        #         group_by(sample_id),
+        #     pr = pr_standardized %>%
+        #         filter(pvalue <= 0.1) %>%
+        #         filter(regulator_locus_tag %in% unique(binding_data_list$harbison$regulator_locus_tag),
+        #                target_locus_tag %in% unique(binding_data_list$harbison$target_locus_tag)) %>%
+        #         group_by(sample_id) %>%
+        #         mutate(abs_effect_rank = rank(-abs(effect), ties.method = 'min'),
+        #                pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
+        #         group_by(sample_id),
+        #     background = pr_standardized %>%
+        #         filter(regulator_locus_tag %in% unique(binding_data_list$harbison$regulator_locus_tag),
+        #                target_locus_tag %in% unique(binding_data_list$harbison$target_locus_tag)) %>%
+        #         pull(target_locus_tag) %>%
+        #         unique()),
 
         chipexo = list(
             binding = binding_data_list$chipexo %>%
@@ -404,30 +424,30 @@ create_pr_lookups = function(pr_dataset_name, binding_pr_set_name,
 }
 
 # # 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)
-#
-#         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$incomplete_after_filtering %>%
-#                 write_csv(file.path(dto_input_outdir, pr_name, binding_name, "incomplete.csv"))
-#         }
-#     }
-# }
+lookup_results = list()
+
+dto_input_outdir = here("results/dto")
+for (pr_name in names(all_pr_dtos)) {
+    lookup_results[[pr_name]] = list()
+
+    for (binding_name in names(all_pr_dtos[[pr_name]])) {
+        write_out_pr_dto_lists(pr_name, binding_name, all_pr_dtos)
+
+        lookup_result = create_pr_lookups(pr_name, binding_name, all_pr_dtos)
+        lookup_results[[pr_name]][[binding_name]] = lookup_result
+
+        # Write complete lookups only
+        lookup_result$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"))
+        }
+    }
+}
 
 # Summary of incomplete cases across all datasets
 # incomplete_summary = map_dfr(names(lookup_results), ~{
@@ -436,5 +456,5 @@ create_pr_lookups = function(pr_dataset_name, binding_pr_set_name,
 #             mutate(pr_dataset = .x, binding_dataset = binding_name)
 #     })
 # })
-
+#
 # print(incomplete_summary %>% count(pr_dataset, binding_dataset, missing_type))

scripts/parse_dto_results.R

@@ -4,16 +4,17 @@ library(here)
 
 dto_results_lookup = expand_grid(
     pr_source = c("hackett", "hughes_oe", "hughes_ko", "hu_reimand", "kemmeren", "mahendrawada_rnaseq"),
-    binding_source = c("cc", "chipexo", "harbison", "mahendrawada_chec"),
-    pr_ranking_column = c("log2fc", "pvalue")) %>%
+    # binding_source = c("cc", "chipexo", "harbison", "mahendrawada_chec"),
+    binding_source = c("cc", "chipexo", "mahendrawada_chec"),
+    pr_ranking_column = c("effect", "pvalue")) %>%
     mutate(
-        result_file = map(file.path(here("results/dto"),
+        result_file = map(file.path(here("results/processed"),
                                     pr_source,
                                     binding_source, "results",
                                     pr_ranking_column),
                           ~list.files(.x, full.names = TRUE))) %>%
     unnest(result_file) %>%
-    mutate(tmp = str_remove(basename(result_file), ".json")) %>%
+    mutate(tmp = str_remove(basename(result_file), "_results.json")) %>%
     separate_wider_delim(tmp,
                          names = c('binding_id', 'perturbation_id'),
                          delim = "-_-")
@@ -68,63 +69,130 @@ dto_results_list = future_imap(dto_results_lookup$result_file, ~{
 
 dto_results_df = bind_cols(dto_results_lookup, bind_rows(dto_results_list))
 
-hf_results_df = dto_results_df %>%
-    mutate(
-        binding_id = case_when(
-            binding_source == "cc" & str_detect(binding_id, "-")
-            ~ paste0("BrentLab/callingcards;annotated_features_combined;",
-                     binding_id),
-            binding_source == "cc" & str_detect(binding_id, "-", negate=TRUE)
-            ~ paste0("BrentLab/callingcards;annotated_features;",
-                     binding_id),
-            binding_source == "chipexo"
-            ~ paste0("BrentLab/rossi_2021;rossi_2021_af_combined;",
-                     binding_id),
-            binding_source == "harbison"
-            ~ paste0("BrentLab/harbison_2004;harbison_2004;",
-                     binding_id),
-            binding_source == "mahendrawada_chec"
-            ~ paste0("BrentLab/mahendrawada_2025;chec_mahendrawada_m2025_af_combined;",
-                     binding_id)),
-        perturbation_id = case_when(
-            pr_source == "hackett" ~ paste0("BrentLab/hackett_2020;hackett_2020;", perturbation_id),
-            pr_source == "hughes_oe" ~ paste0("BrentLab/hughes_2006;overexpression;", perturbation_id),
-            pr_source == "hughes_ko" ~ paste0("BrentLab/hughes_2006;knockout;", perturbation_id),
-            pr_source == "hu_reimand" ~ paste0("BrentLab/hu_2007_reimand_2010;hu_2007_reimand_2010;", perturbation_id),
-            pr_source == "kemmeren" ~ paste0("BrentLab/kemmeren_2014;kemmeren_2014;", perturbation_id),
-            pr_source == "mahendrawada_rnaseq" ~ paste0("BrentLab/mahendrawada_2025;rnaseq_reprocessed;", perturbation_id),
-            .default = "ERROR"),
-        binding_repo_dataset = case_when(
-            binding_source == "cc" & str_detect(binding_id, "-")
-            ~ "callingcards-annotated_features_combined",
-            binding_source == "cc" & str_detect(binding_id, "-", negate=TRUE)
-            ~ "callingcards-annotated_features",
-            binding_source == "chipexo"
-            ~ "rossi_2021-rossi_2021_af_combined",
-            binding_source == "harbison"
-            ~ paste0("harbison_2004-harbison_2004"),
-            binding_source == "mahendrawada_chec"
-            ~ "mahendrawada_2025-chec_mahendrawada_m2025_af_combined"),
-        perturbation_repo_dataset = case_when(
-            pr_source == "hackett" ~ paste0("hackett_2020-hackett_2020"),
-            pr_source == "hughes_oe" ~ paste0("hughes_2006-overexpression"),
-            pr_source == "hughes_ko" ~ paste0("hughes_2006-knockout"),
-            pr_source == "hu_reimand" ~ paste0("hu_2007_reimand_2010-hu_2007_reimand_2010"),
-            pr_source == "kemmeren" ~ paste0("kemmeren_2014-kemmeren_2014"),
-            pr_source == "mahendrawada_rnaseq" ~ paste0("mahendrawada_2025-rnaseq_reprocessed"),
-            .default = "ERROR")) %>%
-    dplyr::rename(binding_rank_threshold = rank1,
-                  perturbation_rank_threshold = rank2,
-                  binding_set_size = set1_len,
-                  perturbation_set_size = set2_len,
-                  dto_fdr = fdr,
-                  dto_empirical_pvalue = empirical_pvalue) %>%
-    select(binding_id, perturbation_id,
-           binding_rank_threshold, perturbation_rank_threshold,
-           binding_set_size, perturbation_set_size,
-           dto_fdr, dto_empirical_pvalue,
-           pr_ranking_column,
-           binding_repo_dataset, perturbation_repo_dataset)
+# hf_results_df = dto_results_df %>%
+#     mutate(
+#         binding_id = case_when(
+#             binding_source == "cc"
+#             ~ paste0("BrentLab/callingcards;annotated_feature_reprocess_mindel;",
+#                      binding_id),
+#             binding_source == "chipexo"
+#             ~ paste0("BrentLab/rossi_2021;rossi_2021_af_combined_mindel;",
+#                      binding_id),
+#             binding_source == "mahendrawada_chec"
+#             ~ paste0("BrentLab/mahendrawada_2025;chec_mahendrawada_m2025_af_combined_mindel;",
+#                      binding_id)),
+#         perturbation_id = case_when(
+#             pr_source == "hackett" ~ paste0("BrentLab/hackett_2020;hackett_2020;", perturbation_id),
+#             pr_source == "hughes_oe" ~ paste0("BrentLab/hughes_2006;overexpression;", perturbation_id),
+#             pr_source == "hughes_ko" ~ paste0("BrentLab/hughes_2006;knockout;", perturbation_id),
+#             pr_source == "hu_reimand" ~ paste0("BrentLab/hu_2007_reimand_2010;hu_2007_reimand_2010;", perturbation_id),
+#             pr_source == "kemmeren" ~ paste0("BrentLab/kemmeren_2014;kemmeren_2014;", perturbation_id),
+#             pr_source == "mahendrawada_rnaseq" ~ paste0("BrentLab/mahendrawada_2025;rnaseq_reprocessed;", perturbation_id),
+#             .default = "ERROR"),
+#         binding_repo_dataset = case_when(
+#             binding_source == "cc" & str_detect(binding_id, "-")
+#             ~ "callingcards-annotated_features_combined",
+#             binding_source == "cc" & str_detect(binding_id, "-", negate=TRUE)
+#             ~ "callingcards-annotated_features",
+#             binding_source == "chipexo"
+#             ~ "rossi_2021-rossi_2021_af_combined",
+#             binding_source == "harbison"
+#             ~ paste0("harbison_2004-harbison_2004"),
+#             binding_source == "mahendrawada_chec"
+#             ~ "mahendrawada_2025-chec_mahendrawada_m2025_af_combined"),
+#         perturbation_repo_dataset = case_when(
+#             pr_source == "hackett" ~ paste0("hackett_2020-hackett_2020"),
+#             pr_source == "hughes_oe" ~ paste0("hughes_2006-overexpression"),
+#             pr_source == "hughes_ko" ~ paste0("hughes_2006-knockout"),
+#             pr_source == "hu_reimand" ~ paste0("hu_2007_reimand_2010-hu_2007_reimand_2010"),
+#             pr_source == "kemmeren" ~ paste0("kemmeren_2014-kemmeren_2014"),
+#             pr_source == "mahendrawada_rnaseq" ~ paste0("mahendrawada_2025-rnaseq_reprocessed"),
+#             .default = "ERROR")) %>%
+#     dplyr::rename(binding_rank_threshold = rank1,
+#                   perturbation_rank_threshold = rank2,
+#                   binding_set_size = set1_len,
+#                   perturbation_set_size = set2_len,
+#                   dto_fdr = fdr,
+#                   dto_empirical_pvalue = empirical_pvalue) %>%
+#     select(binding_id, perturbation_id,
+#            binding_rank_threshold, perturbation_rank_threshold,
+#            binding_set_size, perturbation_set_size,
+#            dto_fdr, dto_empirical_pvalue,
+#            pr_ranking_column,
+#            binding_repo_dataset, perturbation_repo_dataset)
+
+# arrow::write_dataset(
+#     hf_results_df,
+#     path = "/home/chase/code/hf/yeast_comparative_analysis/dto_rossi_cc_mahendrawada_mindel",
+#     format = "parquet",
+#     partitioning = c("binding_repo_dataset", "perturbation_repo_dataset"),
+#     existing_data_behavior = "overwrite",
+#     compression = "zstd",
+#     write_statistics = TRUE,
+#     use_dictionary = c(
+#         binding_id = TRUE,
+#         perturbation_id = TRUE
+#     )
+# )
+
+
+# hf_results_df = dto_results_df %>%
+#     mutate(
+#         binding_id = case_when(
+#             binding_source == "cc" & str_detect(binding_id, "-")
+#             ~ paste0("BrentLab/callingcards;annotated_features_combined;",
+#                      binding_id),
+#             binding_source == "cc" & str_detect(binding_id, "-", negate=TRUE)
+#             ~ paste0("BrentLab/callingcards;annotated_features;",
+#                      binding_id),
+#             binding_source == "chipexo"
+#             ~ paste0("BrentLab/rossi_2021;rossi_2021_af_combined;",
+#                      binding_id),
+#             binding_source == "harbison"
+#             ~ paste0("BrentLab/harbison_2004;harbison_2004;",
+#                      binding_id),
+#             binding_source == "mahendrawada_chec"
+#             ~ paste0("BrentLab/mahendrawada_2025;chec_mahendrawada_m2025_af_combined;",
+#                      binding_id)),
+#         perturbation_id = case_when(
+#             pr_source == "hackett" ~ paste0("BrentLab/hackett_2020;hackett_2020;", perturbation_id),
+#             pr_source == "hughes_oe" ~ paste0("BrentLab/hughes_2006;overexpression;", perturbation_id),
+#             pr_source == "hughes_ko" ~ paste0("BrentLab/hughes_2006;knockout;", perturbation_id),
+#             pr_source == "hu_reimand" ~ paste0("BrentLab/hu_2007_reimand_2010;hu_2007_reimand_2010;", perturbation_id),
+#             pr_source == "kemmeren" ~ paste0("BrentLab/kemmeren_2014;kemmeren_2014;", perturbation_id),
+#             pr_source == "mahendrawada_rnaseq" ~ paste0("BrentLab/mahendrawada_2025;rnaseq_reprocessed;", perturbation_id),
+#             .default = "ERROR"),
+#         binding_repo_dataset = case_when(
+#             binding_source == "cc" & str_detect(binding_id, "-")
+#             ~ "callingcards-annotated_features_combined",
+#             binding_source == "cc" & str_detect(binding_id, "-", negate=TRUE)
+#             ~ "callingcards-annotated_features",
+#             binding_source == "chipexo"
+#             ~ "rossi_2021-rossi_2021_af_combined",
+#             binding_source == "harbison"
+#             ~ paste0("harbison_2004-harbison_2004"),
+#             binding_source == "mahendrawada_chec"
+#             ~ "mahendrawada_2025-chec_mahendrawada_m2025_af_combined"),
+#         perturbation_repo_dataset = case_when(
+#             pr_source == "hackett" ~ paste0("hackett_2020-hackett_2020"),
+#             pr_source == "hughes_oe" ~ paste0("hughes_2006-overexpression"),
+#             pr_source == "hughes_ko" ~ paste0("hughes_2006-knockout"),
+#             pr_source == "hu_reimand" ~ paste0("hu_2007_reimand_2010-hu_2007_reimand_2010"),
+#             pr_source == "kemmeren" ~ paste0("kemmeren_2014-kemmeren_2014"),
+#             pr_source == "mahendrawada_rnaseq" ~ paste0("mahendrawada_2025-rnaseq_reprocessed"),
+#             .default = "ERROR")) %>%
+#     dplyr::rename(binding_rank_threshold = rank1,
+#                   perturbation_rank_threshold = rank2,
+#                   binding_set_size = set1_len,
+#                   perturbation_set_size = set2_len,
+#                   dto_fdr = fdr,
+#                   dto_empirical_pvalue = empirical_pvalue) %>%
+#     select(binding_id, perturbation_id,
+#            binding_rank_threshold, perturbation_rank_threshold,
+#            binding_set_size, perturbation_set_size,
+#            dto_fdr, dto_empirical_pvalue,
+#            pr_ranking_column,
+#            binding_repo_dataset, perturbation_repo_dataset)
 
 
 # note that the deprecated directory is ignored in yeast_comparative_analysis.