adding scripts

scripts/dto_preparation.R

@@ -0,0 +1,536 @@
+library(tidyverse)
+library(arrow)
+library(here)
+
+# these are the protein coding non dubious loci
+mahendrawada_features = arrow::read_parquet("~/code/hf/mahendrawada_2025/features_mahendrawada_2025.parquet")
+
+
+# read in and prepare the perturbation response data
+perturbation_response_data = list(
+    mahendrawada_rnaseq = arrow::read_parquet("~/code/hf/mahendrawada_2025/rnaseq_reprocessed.parquet") %>%
+        filter(target_locus_tag %in% mahendrawada_features$locus_tag) %>%
+        replace_na(list(log2FoldChange = 0, pvalue = 1)) %>%
+        mutate(abs_log2fc = abs(log2FoldChange)),
+    # kemmeren requires deduplicating instances where there are multiple probes
+    # to the same locus_tag. Take the max
+    kemmeren = arrow::open_dataset("~/code/hf/kemmeren_2014/kemmeren_2014.parquet") %>%
+        filter(target_locus_tag %in% mahendrawada_features$locus_tag,
+               str_detect(regulator_locus_tag, "WT-", negate=TRUE)) %>%
+        select(sample_id, regulator_locus_tag, target_locus_tag, Madj, pval) %>%
+        arrow::to_duckdb() %>%
+        group_by(sample_id, target_locus_tag) %>%
+        mutate(rn = row_number(desc(abs(Madj)))) %>%
+        filter(rn == 1) %>%
+        select(-rn) %>%
+        ungroup() %>%
+        collect(),
+    hackett = arrow::read_parquet("~/code/hf/hackett_2020/hackett_2020.parquet") %>%
+        filter(target_locus_tag %in% mahendrawada_features$locus_tag,
+               str_detect(regulator_locus_tag, "WT-", negate=TRUE)) %>%
+        select(sample_id, regulator_locus_tag, target_locus_tag, log2_shrunken_timecourses) %>%
+        arrow::to_duckdb() %>%
+        group_by(sample_id, target_locus_tag) %>%
+        mutate(rn = row_number(desc(abs(log2_shrunken_timecourses)))) %>%
+        filter(rn == 1) %>%
+        select(-rn) %>%
+        ungroup() %>%
+        collect() %>%
+        # add this for consistency with the other datasets
+        mutate(pvalue = 0),
+    hu_reimand = arrow::read_parquet("~/code/hf/hu_2007_reimand_2010/hu_2007_reimand_2010.parquet") %>%
+        filter(target_locus_tag %in% mahendrawada_features$locus_tag) %>%
+        select(sample_id, regulator_locus_tag, target_locus_tag, effect, pval) %>%
+        arrow::to_duckdb() %>%
+        group_by(sample_id, target_locus_tag) %>%
+        mutate(rn = row_number(desc(abs(effect)))) %>%
+        filter(rn == 1) %>%
+        select(-rn) %>%
+        ungroup() %>%
+        collect(),
+    hughes_ko = arrow::read_parquet("~/code/hf/hughes_2006/knockout.parquet") %>%
+        filter(target_locus_tag %in% mahendrawada_features$locus_tag) %>%
+        select(sample_id, regulator_locus_tag, target_locus_tag, mean_norm_log2fc) %>%
+        arrow::to_duckdb() %>%
+        group_by(sample_id, target_locus_tag) %>%
+        mutate(rn = row_number(desc(abs(mean_norm_log2fc)))) %>%
+        filter(rn == 1) %>%
+        select(-rn) %>%
+        ungroup() %>%
+        collect() %>%
+        # add this for consistency with the other datasets
+        mutate(pvalue = 0),
+    hughes_oe = arrow::read_parquet("~/code/hf/hughes_2006/overexpression.parquet") %>%
+        filter(target_locus_tag %in% mahendrawada_features$locus_tag) %>%
+        select(sample_id, regulator_locus_tag, target_locus_tag, mean_norm_log2fc) %>%
+        arrow::to_duckdb() %>%
+        group_by(sample_id, target_locus_tag) %>%
+        mutate(rn = row_number(desc(abs(mean_norm_log2fc)))) %>%
+        filter(rn == 1) %>%
+        select(-rn) %>%
+        ungroup() %>%
+        collect() %>%
+        # add this for consistency with the other datasets
+        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)
+
+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$id) %>%
+    collect() %>%
+    left_join(single_cc_meta) %>%
+    mutate(id = as.character(id))
+
+binding_data = list(
+    cc = single_cc %>%
+        select(intersect(colnames(.), colnames(composite_cc))) %>%
+        bind_rows(composite_cc %>%
+                      select(intersect(colnames(.), colnames(single_cc)))),
+    harbison = arrow::read_parquet("~/code/hf/harbison_2004/harbison_2004.parquet") %>%
+        replace_na(list(effect = 0, pvalue = 1)) %>%
+        group_by(sample_id, target_locus_tag) %>%
+        slice_max(abs(effect), n = 1, with_ties = FALSE) %>%
+        ungroup(),
+    chipexo = arrow::read_parquet("~/code/hf/rossi_2021/rossi_2021_af_combined.parquet") %>%
+        left_join(arrow::read_parquet("~/code/hf/rossi_2021/rossi_2021_metadata_sample.parquet")),
+    mahendrawada_chec = arrow::read_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined.parquet") %>%
+        left_join(arrow::read_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined_meta.parquet"))
+)
+
+mahendrawada_rnaseq_dto_background = map(binding_data, ~{
+    .x %>%
+        ungroup() %>%
+        select(target_locus_tag) %>%
+        distinct() %>%
+        filter(target_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$target_locus_tag))
+    })
+
+mahendrawada_rnaseq_dto = list(
+    cc = list(
+        binding = binding_data$cc %>%
+            filter(poisson_pval <= 0.1) %>%
+            filter(regulator_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$regulator_locus_tag),
+                   target_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$target_locus_tag)) %>%
+            group_by(id) %>%
+            arrange(desc(callingcards_enrichment)) %>%
+            mutate(pvalue_rank = rank(poisson_pval, ties.method = 'min')) %>%
+            dplyr::rename(sample_id = id) %>%
+            group_by(sample_id),
+        pr = perturbation_response_data$mahendrawada_rnaseq %>%
+            filter(pvalue <= 0.1) %>%
+            filter(regulator_locus_tag %in% unique(binding_data$cc$regulator_locus_tag),
+                   target_locus_tag %in% unique(binding_data$cc$target_locus_tag)) %>%
+            group_by(sample_id) %>%
+            mutate(abs_log2fc_rank = rank(-abs_log2fc, ties.method = 'min'),
+                   pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
+            group_by(sample_id)),
+    harbison = list(
+        binding = binding_data$harbison %>%
+            filter(pvalue <= 0.1) %>%
+            filter(regulator_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$regulator_locus_tag),
+                   target_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$target_locus_tag)) %>%
+            group_by(sample_id) %>%
+            arrange(desc(effect)) %>%
+            mutate(pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
+            group_by(sample_id),
+        pr = perturbation_response_data$mahendrawada_rnaseq %>%
+            filter(pvalue <= 0.1) %>%
+            filter(regulator_locus_tag %in% unique(binding_data$harbison$regulator_locus_tag),
+                   target_locus_tag %in% unique(binding_data$harbison$target_locus_tag)) %>%
+            group_by(sample_id) %>%
+            mutate(abs_log2fc_rank = rank(-abs_log2fc, ties.method = 'min'),
+                   pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
+            group_by(sample_id)),
+    chipexo = list(
+        binding = binding_data$chipexo %>%
+            filter(log_poisson_pval <= log(0.1)) %>%
+            filter(regulator_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$regulator_locus_tag),
+                   target_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$target_locus_tag)) %>%
+            group_by(sample_id) %>%
+            arrange(desc(enrichment)) %>%
+            mutate(pvalue_rank = rank(log_poisson_pval, ties.method = 'min')) %>%
+            group_by(sample_id),
+        pr = perturbation_response_data$mahendrawada_rnaseq %>%
+            filter(pvalue <= 0.1) %>%
+            filter(regulator_locus_tag %in% unique(binding_data$chipexo$regulator_locus_tag),
+                   target_locus_tag %in% unique(binding_data$chipexo$target_locus_tag)) %>%
+            group_by(sample_id) %>%
+            mutate(abs_log2fc_rank = rank(-abs_log2fc, ties.method = 'min'),
+                   pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
+            group_by(sample_id)),
+    mahendrawada_chec = list(
+        binding = binding_data$mahendrawada_chec %>%
+            filter(log_poisson_pval <= log(0.1)) %>%
+            filter(regulator_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$regulator_locus_tag),
+                   target_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$target_locus_tag)) %>%
+            group_by(sample_id) %>%
+            arrange(desc(enrichment)) %>%
+            mutate(pvalue_rank = rank(log_poisson_pval, ties.method = 'min')) %>%
+            group_by(sample_id),
+        pr = perturbation_response_data$mahendrawada_rnaseq %>%
+            filter(pvalue <= 0.1) %>%
+            filter(regulator_locus_tag %in% unique(binding_data$mahendrawada_chec$regulator_locus_tag),
+                   target_locus_tag %in% unique(binding_data$mahendrawada_chec$target_locus_tag)) %>%
+            group_by(sample_id) %>%
+            mutate(abs_log2fc_rank = rank(-abs_log2fc, ties.method = 'min'),
+                   pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
+            group_by(sample_id))
+)
+
+# Function to create DTO for a given PR dataset
+create_pr_dto = function(pr_data, pr_effect_col, pr_pval_col, binding_data_list) {
+
+    # Standardize column names for the PR data
+    pr_standardized = pr_data %>%
+        ungroup() %>%
+        # remove the target observation for the perturbed locus
+        # NOTE: this is also done for the binding data, though i don't
+        # remove it from the background
+        filter(regulator_locus_tag != target_locus_tag)
+
+    # Handle effect column renaming
+    if (pr_effect_col != "effect") {
+        pr_standardized = pr_standardized %>%
+            rename(effect = !!sym(pr_effect_col))
+    }
+
+    # Handle pvalue column renaming
+    if (pr_pval_col != "pvalue") {
+        # If renaming a different column to pvalue, drop existing pvalue column first
+        if ("pvalue" %in% colnames(pr_standardized)) {
+            pr_standardized = pr_standardized %>%
+                select(-pvalue)
+        }
+        pr_standardized = pr_standardized %>%
+            rename(pvalue = !!sym(pr_pval_col))
+    }
+
+    # Create DTOs for each binding dataset
+    dto_list = list(
+        cc = list(
+            binding = binding_data_list$cc %>%
+                filter(regulator_locus_tag != target_locus_tag) %>%
+                filter(poisson_pval <= 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(id) %>%
+                arrange(desc(callingcards_enrichment)) %>%
+                mutate(pvalue_rank = rank(poisson_pval, ties.method = 'min')) %>%
+                dplyr::rename(sample_id = id) %>%
+                group_by(sample_id),
+            pr = pr_standardized %>%
+                filter(pvalue <= 0.1) %>%
+                filter(regulator_locus_tag %in% unique(binding_data_list$cc$regulator_locus_tag),
+                       target_locus_tag %in% unique(binding_data_list$cc$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)),
+
+        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$target_locus_tag)) %>%
+                group_by(sample_id) %>%
+                mutate(abs_effect_rank = rank(-abs(effect), ties.method = 'min'),
+                       pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
+                group_by(sample_id)),
+
+        chipexo = list(
+            binding = binding_data_list$chipexo %>%
+                filter(regulator_locus_tag != target_locus_tag) %>%
+                filter(log_poisson_pval <= log(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(enrichment)) %>%
+                mutate(pvalue_rank = rank(log_poisson_pval, ties.method = 'min')) %>%
+                group_by(sample_id),
+            pr = pr_standardized %>%
+                filter(pvalue <= 0.1) %>%
+                filter(regulator_locus_tag %in% unique(binding_data_list$chipexo$regulator_locus_tag),
+                       target_locus_tag %in% unique(binding_data_list$chipexo$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)),
+
+        mahendrawada_chec = list(
+            binding = binding_data_list$mahendrawada_chec %>%
+                filter(regulator_locus_tag != target_locus_tag) %>%
+                filter(log_poisson_pval <= log(0.1)) %>%
+                filter(regulator_locus_tag %in% unique(pr_standardized$regulator_locus_tag),
+                       target_locus_tag %in% unique(pr_standardized$target_locus_tag)) %>%
+                group_by(sample_id) %>%
+                arrange(desc(enrichment)) %>%
+                mutate(pvalue_rank = rank(log_poisson_pval, ties.method = 'min')) %>%
+                group_by(sample_id),
+            pr = pr_standardized %>%
+                filter(pvalue <= 0.1) %>%
+                filter(regulator_locus_tag %in% unique(binding_data_list$mahendrawada_chec$regulator_locus_tag),
+                       target_locus_tag %in% unique(binding_data_list$mahendrawada_chec$target_locus_tag)) %>%
+                group_by(sample_id) %>%
+                mutate(abs_effect_rank = rank(-abs(effect), ties.method = 'min'),
+                       pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
+                group_by(sample_id))
+    )
+
+    return(dto_list)
+}
+
+# Create all DTOs
+all_pr_dtos = list(
+    mahendrawada_rnaseq = create_pr_dto(
+        perturbation_response_data$mahendrawada_rnaseq,
+        pr_effect_col = "log2FoldChange",
+        pr_pval_col = "padj",
+        binding_data_list = binding_data
+    ),
+
+    kemmeren = create_pr_dto(
+        perturbation_response_data$kemmeren,
+        pr_effect_col = "Madj",
+        pr_pval_col = "pval",
+        binding_data_list = binding_data
+    ),
+
+    hackett = create_pr_dto(
+        perturbation_response_data$hackett,
+        pr_effect_col = "log2_shrunken_timecourses",
+        pr_pval_col = "pvalue",
+        binding_data_list = binding_data
+    ),
+
+    hu_reimand = create_pr_dto(
+        perturbation_response_data$hu_reimand,
+        pr_effect_col = "effect",
+        pr_pval_col = "pval",
+        binding_data_list = binding_data
+    ),
+
+    hughes_ko = create_pr_dto(
+        perturbation_response_data$hughes_ko,
+        pr_effect_col = "mean_norm_log2fc",
+        pr_pval_col = "pvalue",
+        binding_data_list = binding_data
+    ),
+
+    hughes_oe = create_pr_dto(
+        perturbation_response_data$hughes_oe,
+        pr_effect_col = "mean_norm_log2fc",
+        pr_pval_col = "pvalue",
+        binding_data_list = binding_data
+    )
+)
+
+# Write out DTO ranked lists
+
+results_basedir = "~/htcf_local/dto"
+
+write_out_pr_dto_lists = function(pr_dataset_name, binding_pr_set_name, all_pr_dtos_list) {
+
+    output_path = file.path(results_basedir, pr_dataset_name)
+
+    binding_pr_set = all_pr_dtos_list[[pr_dataset_name]][[binding_pr_set_name]]
+
+    binding_split = binding_pr_set$binding %>%
+        group_split()
+    names(binding_split) = pull(group_keys(binding_pr_set$binding), sample_id)
+
+    pr_split = binding_pr_set$pr %>%
+        group_split()
+    names(pr_split) = pull(group_keys(binding_pr_set$pr), sample_id)
+
+    curr_output_path = list(
+        binding = file.path(output_path, binding_pr_set_name, "binding"),
+        pr_effect = file.path(output_path, binding_pr_set_name, "pr", "effect"),
+        pr_pvalue = file.path(output_path, binding_pr_set_name, "pr", "pvalue")
+    )
+
+    map(curr_output_path, dir.create, recursive = TRUE, showWarnings = FALSE)
+
+    # Write out binding lists
+    map(names(binding_split), ~{
+        binding_split[[.x]] %>%
+            select(target_locus_tag, pvalue_rank) %>%
+            arrange(pvalue_rank) %>%
+            write_csv(file.path(curr_output_path$binding, paste0(.x, ".csv")),
+                      col_names = FALSE)
+    })
+
+    # Write out effect-ranked pr lists
+    map(names(pr_split), ~{
+        pr_split[[.x]] %>%
+            select(target_locus_tag, abs_effect_rank) %>%
+            arrange(abs_effect_rank) %>%
+            write_csv(file.path(curr_output_path$pr_effect, paste0(.x, ".csv")),
+                      col_names = FALSE)
+    })
+
+    # Write out pvalue pr lists
+    map(names(pr_split), ~{
+        pr_split[[.x]] %>%
+            select(target_locus_tag, pvalue_rank) %>%
+            arrange(pvalue_rank) %>%
+            write_csv(file.path(curr_output_path$pr_pvalue, paste0(.x, ".csv")),
+                      col_names = FALSE)
+    })
+}
+
+# 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)
+
+    background_list[[binding_pr_set_name]] %>%
+        write_csv(file.path(output_path, binding_pr_set_name, "background.csv"),
+                  col_names = FALSE)
+}
+
+# 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") {
+
+    lookup_df = all_pr_dtos_list[[pr_dataset_name]][[binding_pr_set_name]]$binding %>%
+        ungroup() %>%
+        dplyr::select(sample_id, regulator_locus_tag) %>%
+        distinct() %>%
+        dplyr::rename(binding_id = sample_id) %>%
+        left_join(all_pr_dtos_list[[pr_dataset_name]][[binding_pr_set_name]]$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"))) %>%
+        select(binding, pr_effect, pr_pvalue)
+
+    return(lookup_df)
+}
+
+# Create background lists for all PR datasets (if not already created)
+all_pr_backgrounds = map(names(all_pr_dtos), ~{
+    map(binding_data, function(bd) {
+        bd %>%
+            ungroup() %>%
+            select(target_locus_tag) %>%
+            distinct() %>%
+            filter(target_locus_tag %in% unique(all_pr_dtos[[.x]][[1]]$pr$target_locus_tag))
+    })
+})
+names(all_pr_backgrounds) = names(all_pr_dtos)
+
+# Write out all DTOs for all PR datasets
+for (pr_name in names(all_pr_dtos)) {
+    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"),
+                      col_names = FALSE)
+    }
+}
+
+dto_results_path_list = list.files(results_basedir,
+                              "*.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),
+                              ~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')
+
+curr_dto_res = arrow::open_dataset("~/code/hf/yeast_comparative_analysis/dto") %>%
+    collect()
+
+results_df = tibble(
+    combined_id = str_remove(basename(dto_results_list), ".json"),
+    binding_source = dirname(dirname(dirname(dirname(dto_results_list)))),
+    pr_scoring = basename(dirname(dto_results_list)),
+    path = dto_results_path_list) %>%
+    separate_wider_delim(combined_id,
+                         names = c('binding_sampleid', 'pr_sampleid'),
+                         delim = '-_-') %>%
+    left_join(dto_results_frame) %>%
+    select(-path) %>%
+    mutate(
+        binding_id = case_when(
+            binding_source == "cc" & str_detect(binding_sampleid, "-")
+            ~ paste0("BrentLab/callingcards;annotated_features_combined",
+                     binding_sampleid),
+            binding_source == "cc" & str_detect(binding_sampleid, "-", negate=TRUE)
+            ~ paste0("BrentLab/callingcards;annotated_features",
+                     binding_sampleid),
+            binding_source == "chipexo"
+            ~ paste0("BrentLab/rossi_2021;rossi_2021_af_combined",
+                     binding_sampleid),
+            binding_source == "harbison"
+            ~ paste0("BrentLab/harbison_2004;harbison_2004",
+                     binding_sampleid),
+            binding_source == "mahendrawada_chec"
+            ~ paste0("BrentLab/mahendrawada_2025;chec_mahendrawada_m2025_af_combined",
+                     binding_sampleid)),
+        perturbation_id = paste0("BrentLab/mahendrawada_2025/rnaseq_reprocessed", pr_sampleid),
+        binding_repo_dataset = case_when(
+            binding_source == "cc" & str_detect(binding_sampleid, "-")
+            ~ "callingcards-annotated_features_combined",
+            binding_source == "cc" & str_detect(binding_sampleid, "-", 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 = "mahendrawada_2025-rnaseq_reprocessed") %>%
+    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,
+           binding_repo_dataset, perturbation_repo_dataset)
+
+# arrow::write_dataset(
+#     results_df,
+#     path = "/home/chase/code/hf/yeast_comparative_analysis/dto",
+#     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
+#     )
+# )