adding mindel analysis set

README.md

@@ -110,6 +110,7 @@ features:
   - annotated_feature_combined_meta
   - genome_map_meta
   - 2026_analysis_set
+  - annotated_feature_reprocess_mindel_analysis
   fields:
   - name: regulator_locus_tag
     dtype: string
@@ -126,6 +127,7 @@ features:
   - 2026_analysis_set
   - annotated_feature_reprocess_yiming
   - annotated_feature_reprocess_mindel
+  - annotated_feature_reprocess_mindel_analysis
   fields:
   - name: target_locus_tag
     dtype: string
@@ -142,6 +144,7 @@ features:
   - 2026_analysis_set
   - annotated_feature_reprocess_yiming
   - annotated_feature_reprocess_mindel
+  - annotated_feature_reprocess_mindel_analysis
   fields:
   - name: experiment_hops
     dtype: float64 
@@ -177,6 +180,7 @@ features:
 - applies_to:
   - annotated_feature_reprocess_yiming
   - annotated_feature_reprocess_mindel
+  - annotated_feature_reprocess_mindel_analysis
   fields:
   - name: total_background_hops
     dtype: float64
@@ -479,6 +483,31 @@ configs:
       dtype: int64
       description: >-
         Genome map identifier linking to the genome_map and genome_map_meta dataset
+
+- config_name: annotated_feature_reprocess_mindel_analysis
+  description: >-
+    This is the analysis set for the mindel data. It is generated using the same logic as the 2026_analysis_set, but using the results from the reprocessing against the mindel promoters. A passing replicate has >=3000 hops had a dto empirical pvalue < 0.01 against either kemmeren or hackett. Where a given regulator had multiple passing replicates, those replicates were combined (see annotated_feature_combined). This dataset should be used when you want to use data that can be reproduced directly from the genome_map data included in this repo, using the mindel promoter definitions. See scripts/quantify_regions.R as well as BrentLab/yeast_comparative_analysis/scripts for details of how this was conducted.
+  dataset_type: annotated_features
+  metadata_fields: ["combined_id","regulator_locus_tag","regulator_symbol", "experiment_total_hops", "background_total_hops"]
+  data_files:
+  - split: train
+    path: annotated_feature_reprocess_mindel_analysis.parquet
+  genome_resources:
+    region_sets:
+      Mindel:
+        path: https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/mindel_promoters.csv.gz 
+        join_column: target_locus_tag
+  dataset_info:
+    features:
+    - name: combined_id
+      dtype: string
+      description: >-
+        The genome map identifier of either a single passing, or multiple
+        passing replicates (hyphen-delimited) that were combined for the analysis set. replicates were combined if the DTO empirical
+        pvalue was <= 0.01 in either kemmeren or hackett. After combining,
+        if they have more than 3k hopes (single or combined), then they are
+        included in the analysis set.
+      role: sample_id
 ---
 # Calling Cards
 

annotated_feature_reprocess_mindel_analysis.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a8198424bd09ae489bb569b77e1e34360b5accca195aae78cd9931842b70890e
+size 11159225

scripts/quantify_regions.R

@@ -673,3 +673,84 @@ results_replicates_df = bind_rows(map(results_replicates, as_tibble), .id = "gen
 #         genome_map_id = TRUE
 #     )
 #   )
+
+# creating the combined data for a new set
+
+hackett_dedup = arrow::read_parquet("~/code/hf/hackett_2020/hackett_2020_analysis_set.parquet")
+
+dto_results = arrow::open_dataset("~/code/hf/yeast_comparative_analysis/dto_rossi_cc_mahendrawada_mindel/")
+
+# empirical pval < 0.01 in at least one of {kemmeren, hackett-at-30min}
+passing_cc_reps = dto_results |>
+    filter(perturbation_repo_dataset == "hackett_2020-hackett_2020"
+           | perturbation_repo_dataset == "kemmeren_2014-kemmeren_2014",
+           binding_repo_dataset == "callingcards-annotated_feature_reprocess_mindel",
+           pr_ranking_column == "pvalue") |>
+    collect() |>
+    filter(perturbation_repo_dataset == "kemmeren_2014-kemmeren_2014" | perturbation_id %in%
+               paste0("BrentLab/hackett_2020;hackett_2020;", unique(pull(filter(hackett_dedup, time == 30), sample_id)))) |>
+    group_by(binding_id) |>
+    filter(any(dto_empirical_pvalue <= 0.01)) |>
+    distinct(binding_id) |>
+    pull(binding_id)
+
+passing_cc_mindel_reps = str_remove(passing_cc_reps, "BrentLab/callingcards;annotated_feature_reprocess_mindel;")
+
+genome_map_replicate_meta_mindel_combined = genome_map_replicate_meta |>
+    filter(id %in% passing_cc_mindel_reps) |>
+    group_by(regulator_locus_tag, regulator_symbol, condition) |>
+    mutate(id = as.character(id)) |>
+    reframe(combined_id = if (n() > 1) paste(id, collapse = "-") else id)
+
+# # Run analysis with deduplication (default for calling cards)
+results_combined <- map(genome_map_replicate_meta_mindel_combined$combined_id, ~{
+    # get list of ids (might just be 1)
+    ids <- as.integer(strsplit(.x, "-")[[1]])
+    ids <- ids[!is.na(ids)]
+
+    # for each id, produce a GRanges
+    combined_passing_set <- map(ids, ~{
+        genome_map_replicate_ds |>
+            filter(id == .x) |>
+            collect() |>
+            dplyr::rename(score = depth) |>
+            relocate(chr, start, end, id, score, strand) |>
+            mutate(depth = scales::rescale(score, to = c(1, 1000))) |>
+            bed_to_granges() |>
+            deduplicate_granges()
+    })
+
+    # combine the list into a single gr
+    combined_gr <- do.call(c, combined_passing_set)
+
+    enrichment_analysis(
+        experiment_gr = combined_gr,
+        background_gr = background_gr,
+        regions_gr = regions_gr,
+        deduplicate_experiment = FALSE,
+        pseudocount = 0.1
+    )
+})
+names(results_combined) = genome_map_replicate_meta_mindel_combined$combined_id
+
+results_combined_analysis_df = bind_rows(map(results_combined, as_tibble), .id = "combined_id") |>
+    left_join(genome_map_replicate_meta_mindel_combined) |>
+    select(combined_id,
+           regulator_locus_tag, regulator_symbol, condition,
+           target_locus_tag, target_symbol, experiment_hops, background_hops,
+           total_background_hops, total_experiment_hops,
+           callingcards_enrichment,
+           poisson_pval, log_poisson_pval, poisson_qval,
+           hypergeometric_pval, log_hypergeometric_pval, hypergeometric_qval) |>
+    filter(condition == "standard")
+
+# results_combined_analysis_df |>
+#     ungroup() |>
+#     dplyr::select(-condition) |>
+#     arrange(regulator_locus_tag) |>
+#     write_parquet(
+#         "/home/chase/code/hf/callingcards/annotated_feature_reprocess_mindel_analysis.parquet",
+#         compression = "zstd",
+#         write_statistics = TRUE,
+#         use_dictionary = TRUE
+#     )