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

write_out_pr_dto_lists = function(pr_dataset_name,
                                  binding_pr_set_name,
                                  all_pr_dtos_list,
                                  base_outdir=here("results/dto")) {

    output_path = file.path(base_outdir, 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,
                               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"),
                  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") {
    # 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)

    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(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)

    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) {
        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
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]])

        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), ~{
    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(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')

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
#     )
# )