adding peaks vs promoter sets

README.md

@@ -71,7 +71,6 @@ features:
   - rossi_2021_af_combined_start_codon_500bp
   - rossi_2021_af_replicates_intergenic_replicates
   - rossi_2021_af_combined_intergenic
-  - macs2_annotated_peaks_combined
   fields:
   - name: regulator_locus_tag
     dtype: string
@@ -80,6 +79,24 @@ features:
     dtype: string
     description: Standard gene symbol of the transcription factor
 
+- applies_to:
+  - macs_bp500
+  - macs_intergenic
+  - macs_kang
+  - macs_mindel
+  fields:
+    - name: n_peaks
+      dtype: int32
+      description: number of peaks that are annotated to within 700 bp of the target. Note that a peak may be annotated to multiple targets
+    - name: nearest_score
+      dtype: float64
+      description: -log10(qvalue) of the peak nearest to the target
+    - name: median_score
+      dtype: float64
+      description: median -log10(qvalue) of the peaks annotated to the target
+    - name: max_score
+      dtype: float64
+      description: max -log10(qvalue) of the peaks annotated to the target
 
 - applies_to:
   - rossi_2021_af_replicates
@@ -91,7 +108,10 @@ features:
   - rossi_2021_af_combined_start_codon_500bp
   - rossi_2021_af_replicates_intergenic_replicates
   - rossi_2021_af_combined_intergenic
-  - macs2_annotated_peaks_combined
+  - macs_bp500
+  - macs_intergenic
+  - macs_kang
+  - macs_mindel
   fields:
   - name: target_locus_tag
     dtype: string
@@ -133,11 +153,11 @@ features:
   - rossi_2021_af_combined_intergenic
   fields:
   - name: background_counts
-    dtype: int64
+    dtype: float64
     description: Read counts in the background/control sample for this peak region
     role: quantitative_measure
   - name: experiment_counts
-    dtype: int64
+    dtype: float64
     description: Read counts in the ChIP-exo experiment sample for this peak region
     role: quantitative_measure
   - name: total_background_counts
@@ -227,7 +247,7 @@ features:
       ORF, we take the median peak score. Replicates are then combined. When 
       combining replicates, the median score across all replicates is taken.
   - name: n_peaks
-    dtype: float64
+    dtype: int32
     description: >-
       The number of peaks annotated to the same target. This is both within replicates and 
       across (ie, there might be 3 peaks in 2 replicates for a total of 6 in this data)
@@ -236,7 +256,7 @@ features:
     description: >-
       The maximum distance a peak in the set (see n_peaks) is from the ORF
   - name: peak_n_replicates
-    dtype: float64
+    dtype: int32
     description: >-
       The number of replicates which have peaks for this target
 
@@ -269,7 +289,7 @@ configs:
 - config_name: rossi_2021_metadata_sample
   description: Sample-level metadata for combined ChIP-exo experiments including experimental conditions
   dataset_type: metadata
-  applies_to: ["rossi_2021_af_combined", "rossi_2021_af_combined_mindel", "yep_filtered_peaks_combined", "rossi_2021_af_combined_start_codon_500bp", "rossi_2021_af_combined_intergenic", "macs2_annotated_peaks_combined"]
+  applies_to: ["rossi_2021_af_combined", "rossi_2021_af_combined_mindel", "yep_filtered_peaks_combined", "rossi_2021_af_combined_start_codon_500bp", "rossi_2021_af_combined_intergenic", "macs_bp500", "macs_intergenic", "macs_kang", "macs_mindel"]
   data_files:
   - split: train
     path: rossi_2021_metadata_sample.parquet
@@ -304,7 +324,8 @@ configs:
       description: Sample identifier used by yeastepigenome.org
 
 - config_name: genome_map
-  description: "ChIP-exo 5' tag coverage data partitioned by sample accession"
+  description: >-
+    ChIP-exo 5' tag coverage data partitioned by sample accession. See https://github.com/BrentLab/checseq_promoter_enrichment_slurm_pipeline/tree/main/promoter_enrichment for how these are created from alignments.
   dataset_type: genome_map
   data_files:
   - split: train
@@ -368,40 +389,90 @@ configs:
       dtype: string
       description: Chromosome name (e.g., chrI, chrII, etc.)
     - name: start
-      dtype: int32
+      dtype: float64
       description: 0-based start position of the peak region
     - name: end
-      dtype: int32
+      dtype: float64
       description: 0-based, half open end position of a single base resolution peak
     - name: score
-      dtype: int32
+      dtype: float64
       description: "Score assigned by ChExMix as reported by yeastepigenome.org"
 
-- config_name: macs2_annotated_peaks_combined
-  description: "peaks called with macs2. see scripts/rossi_peak_analysis.R for details. peaks filtered to -log10(q) > 0.1, and to only those within 700 bp of the 5' end of the most 5' exon. max, median and nearest scores are assigned to the feature."
+- config_name: macs_bp500
+  description: >-
+    peaks called with macs. see scripts/rossi_peak_analysis.R for details. then intersected with the promoter set defined
+    as 500 bp upstream of the start codon.
   dataset_type: annotated_features
   data_files:
   - split: train
-    path:  macs2_annotated_peaks_combined.parquet
+    path:  macs_bp500.parquet
+  genome_resources:
+    region_sets:
+      start_codon_500bp:
+        path: https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/start_codon_500bp_upstream_promoters.bed
+        join_column: target_locus_tag
   dataset_info:
     features:
     - name: sample_id
-      dtype: string
+      dtype: int64
       description: sample identifier. use with rossi_2021_metadata_sample
-    - name: n_peaks
-      dtype: int32
-      description: number of peaks that are annotated to within 700 bp of the target. Note that a peak may be annotated to multiple targets
-    - name: nearest_score
-      dtype: float64
-      description: -log10(qvalue) of the peak nearest to the target
-    - name: median_score
-      dtype: float64
-      description: median -log10(qvalue) of the peaks annotated to the target
-    - name: max_score
-      dtype: float64
-      description: max -log10(qvalue) of the peaks annotated to the target
 
+- config_name: macs_intergenic
+  description: >-
+    peaks called with macs. see scripts/rossi_peak_analysis.R for details. then intersected with the promoter set defined
+    as the intergenic regions from SGD 5-1.
+  dataset_type: annotated_features
+  data_files:
+  - split: train
+    path:  macs_intergenic.parquet
+  genome_resources:
+    region_sets:
+      intergenic:
+        path: https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/intergenic_regions_metadata_5_1.csv
+        join_column: target_locus_tag
+  dataset_info:
+    features:
+    - name: sample_id
+      dtype: int64
+      description: sample identifier. use with rossi_2021_metadata_sample
 
+- config_name: macs_kang
+  description: >-
+    peaks called with macs. see scripts/rossi_peak_analysis.R for details. then intersected with the promoter set defined 
+    as 700 bp upstream, truncated by upstream features.
+  dataset_type: annotated_features
+  data_files:
+  - split: train
+    path:  macs_kang.parquet
+  genome_resources:
+    region_sets:
+      Kang:
+        path: https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/yiming_promoters.bed
+        join_column: target_locus_tag
+  dataset_info:
+    features:
+    - name: sample_id
+      dtype: int64
+      description: sample identifier. use with rossi_2021_metadata_sample
+
+- config_name: macs_mindel
+  description: >-
+    peaks called with macs. see scripts/rossi_peak_analysis.R for details. then intersected with the promoter set defined 
+    by Mindel. See huggingface/BrentLab/yeast_genome_resources.
+  dataset_type: annotated_features
+  data_files:
+  - split: train
+    path:  macs_mindel.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: sample_id
+      dtype: int64
+      description: sample identifier. use with rossi_2021_metadata_sample
 
 - config_name: rossi_2021_af_replicates
   description: ChIP-exo annotated features at biological replicate level with binding peaks and statistical significance metrics

macs_bp500.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:704ef9d019695cf30d4bbdb89159f6006300b61c7de662e320d086022dc03032
+size 7021500

macs_intergenic.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd2eb9b04fb6077047106431a515973e5e5ae778312400eb65a2f026791d20c7
+size 9323332

macs_kang.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e65e9bc8f3630b990dcc86937832407dc4f2e36439d2d0ff1c139c64a79120c
+size 6998849

macs_mindel.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b3ba76d80089378f8cfb121238d98300788a0111283310b00b505a612269dbf
+size 8626913

scripts/rossi_peak_analysis.R

@@ -5,7 +5,20 @@ library(here)
 
 exclude_regions <- rtracklayer::import(here("data/ChExMix_Peak_Filter_List_190612.bed"))
 seqlevels(exclude_regions)[which(seqlevels(exclude_regions) == "chr2-micron")] <- "2-micron"
+brentlab_features <- read_csv("~/projects/huggingface/yeast_genome_resources/brentlab_features.csv.gz")
+
+
+promoters <- list(
+    bp500 = rtracklayer::import("~/projects/huggingface/yeast_genome_resources/start_codon_500bp_upstream_promoters.bed"),
+    mindel = GenomicRanges::GRanges(read_csv("~/projects/huggingface/yeast_genome_resources/mindel_promoters.csv.gz")),
+    kang = rtracklayer::import("~/projects/huggingface/yeast_genome_resources/yiming_promoters.bed"),
+    intergenic = rtracklayer::import("~/projects/huggingface/yeast_genome_resources/intergenic_regions_5_1.bed")
+)
 
+GenomicRanges::mcols(promoters$mindel) <- GenomicRanges::mcols(promoters$mindel) |>
+    as.data.frame() |>
+    dplyr::transmute(name = target_locus_tag) |>
+    S4Vectors::DataFrame()
 
 read_in_annotated_peaks <- function(peak_path) {
     df <- read_tsv(peak_path, show_col_types = FALSE)
@@ -96,230 +109,374 @@ peaks_df <- bind_rows(annotated_peaks$target_score, .id = "tmp") |>
     )
 
 
-rossi_sample_meta <- arrow::read_parquet("~/projects/huggingface/rossi_2021/rossi_2021_metadata_sample.parquet")
+annotate_peaks_to_promoters <- function(peaks_df, promoters_gr) {
+    peaks_gr <- GenomicRanges::GRanges(
+        seqnames = peaks_df$chr,
+        ranges = IRanges::IRanges(start = peaks_df$start, end = peaks_df$end)
+    )
 
-brentlab_features <- read_csv("~/projects/huggingface/yeast_genome_resources/brentlab_features.csv.gz")
+    hits <- GenomicRanges::findOverlaps(peaks_gr, promoters_gr, ignore.strand = TRUE)
 
-peaks_df_to_hf <- peaks_df |>
-    left_join(rossi_sample_meta) |>
-    dplyr::select(sample_id, regulator_locus_tag,
-        regulator_symbol,
-        target_locus_tag = entrez_id,
-        n_peaks, nearest_score, median_score, max_score
-    ) |>
-    left_join(dplyr::select(brentlab_features,
-        target_locus_tag = locus_tag,
-        target_symbol = symbol
-    )) |>
-    dplyr::relocate(
-        sample_id, regulator_locus_tag, regulator_symbol,
-        target_locus_tag, target_symbol
-    )
+    if (length(hits) == 0) {
+        return(peaks_df |> dplyr::slice(0) |> dplyr::mutate(promoter_id = character(), distance_to_tss = numeric()))
+    }
 
-peaks_df_to_hf |>
-    arrow::write_parquet("~/projects/huggingface/rossi_2021/macs2_annotated_peaks.parquet")
+    promoter_strand <- as.character(GenomicRanges::strand(promoters_gr))
+    promoter_start <- GenomicRanges::start(promoters_gr)
+    promoter_end <- GenomicRanges::end(promoters_gr)
+    promoter_name <- promoters_gr$name
+
+    peaks_df[S4Vectors::queryHits(hits), ] |>
+        dplyr::mutate(
+            promoter_id = promoter_name[S4Vectors::subjectHits(hits)],
+            .promoter_strand = promoter_strand[S4Vectors::subjectHits(hits)],
+            .promoter_start = promoter_start[S4Vectors::subjectHits(hits)],
+            .promoter_end = promoter_end[S4Vectors::subjectHits(hits)],
+            .peak_mid = (start + end) / 2,
+            # TSS-proximal edge of the promoter interval
+            .tss_pos = dplyr::if_else(.promoter_strand == "+", .promoter_end, .promoter_start),
+            distance_to_tss = abs(.peak_mid - .tss_pos)
+        ) |>
+        dplyr::select(-dplyr::starts_with("."), distance_to_tss, promoter_id)
+}
 
-mcisaac_responsive <- arrow::read_parquet("~/projects/huggingface/hackett_2020/hackett_2020_analysis_set.parquet")
+score_targets_promoters <- function(df, promoters_gr, peak_score_thresh = -log10(0.1)) {
+    df |>
+        dplyr::filter(!in_exclude_region) |>
+        annotate_peaks_to_promoters(promoters_gr) |>
+        dplyr::filter(peak_score > peak_score_thresh) |>
+        dplyr::group_by(promoter_id) |>
+        dplyr::reframe(
+            n_peaks = n(),
+            nearest_score = peak_score[which.min(distance_to_tss)],
+            median_score = median(peak_score),
+            max_score = max(peak_score)
+        )
+}
 
-peaks_with_mcisaac <- peaks_df |>
-    dplyr::rename(target_locus_tag = entrez_id) |>
-    filter(
-        treatment == "Normal",
-        growth_media == "YPD",
-        median_score >= -log10(0.1)
-    ) |>
-    left_join(dplyr::select(
-        mcisaac_responsive,
-        regulator_locus_tag,
-        target_locus_tag,
-        time,
-        responsive
-    )) |>
-    filter(!is.na(responsive))
+chrmap <- read_csv("~/projects/huggingface/yeast_genome_resources/chrmap.csv.gz")
 
-peaks_with_mcisaac |>
-    filter(time == 30) |>
-    group_by(regulator_locus_tag) |>
-    nest() |>
-    mutate(
-        rr_nearest = map_dbl(data, ~ {
-            .x |>
-                arrange(desc(nearest_score)) |>
-                slice_head(n = 25) |>
-                summarise(sum(responsive) / n()) |>
-                pull()
-        }),
-        rr_max = map_dbl(data, ~ {
-            .x |>
-                arrange(desc(max_score)) |>
-                slice_head(n = 25) |>
-                summarise(sum(responsive) / n()) |>
-                pull()
-        }),
-        rr_median = map_dbl(data, ~ {
-            .x |>
-                arrange(desc(median_score)) |>
-                slice_head(n = 25) |>
-                summarise(sum(responsive) / n()) |>
-                pull()
-        })
-    ) |>
-    dplyr::select(-data) |>
-    pivot_longer(
-        cols = starts_with("rr_"),
-        names_to = "score_type",
-        values_to = "rr"
-    ) |>
-    ggplot(aes(x = score_type, y = rr)) +
-    geom_boxplot()
-
-library(patchwork)
-library(gridExtra)
-
-score_summary <- peaks_with_mcisaac |>
-    filter(time == 30) |>
-    group_by(regulator_locus_tag) |>
-    reframe(
-        score_type = c("nearest", "max", "median"),
-        n_targets = c(
-            n_distinct(target_locus_tag),
-            n_distinct(target_locus_tag),
-            n_distinct(target_locus_tag)
-        ),
-        n_peaks = c(n(), n(), n()),
-        min = c(min(nearest_score), min(max_score), min(median_score)),
-        max = c(max(nearest_score), max(max_score), max(median_score)),
-        median = c(median(nearest_score), median(max_score), median(median_score)),
-        mean = c(mean(nearest_score), mean(max_score), mean(median_score))
-    )
+reduce_peak_cols <- function(df) {
+    df |>
+        dplyr::rename(peak_id = 1) |>
+        dplyr::select(peak_id, chr, start, end, strand, peak_score, in_exclude_region) |>
+        left_join(dplyr::select(chrmap, chr, ucsc)) |>
+        mutate(chr = ucsc) |>
+        dplyr::select(-ucsc)
+}
 
 
-p1 <- score_summary |>
-    ggplot(aes(x = score_type, y = mean, fill = score_type)) +
-    geom_boxplot(alpha = 0.7) +
-    labs(title = "Mean Score Distribution", y = "Mean Score", x = "") +
-    theme_minimal() +
-    theme(legend.position = "none")
-
-p3 <- score_summary |>
-    ggplot(aes(x = n_targets, y = mean, color = score_type)) +
-    geom_point(alpha = 0.6) +
-    facet_wrap(~score_type) +
-    scale_x_log10() +
-    labs(title = "Number of Targets vs Mean Score", x = "N Targets (log10)", y = "Mean") +
-    theme_minimal() +
-    theme(legend.position = "none")
-
-# Calculate stats for table
-n_targets_summary <- score_summary |>
-    dplyr::select(n_targets) |>
-    distinct() |>
-    pull(n_targets) %>%
-    {
-        tibble(
-            Min = round(quantile(., probs = 0), 2),
-            Q25 = round(quantile(., probs = 0.25), 2),
-            Median = round(quantile(., probs = 0.5), 2),
-            Q75 = round(quantile(., probs = 0.75), 2),
-            Max = round(quantile(., probs = 1), 2)
-        )
-    }
+annotated_peaks$df_reduced <- map(
+    compact(annotated_peaks$df),
+    reduce_peak_cols
+)
+
+annotated_peaks_by_promoter <- map(promoters, ~ {
+    map(
+        annotated_peaks$df_reduced,
+        score_targets_promoters,
+        promoters_gr = .
+    )
+})
+
+intergenic_meta <- read_csv("~/projects/huggingface/yeast_genome_resources/intergenic_regions_metadata_5_1.csv")
 
-# Vertical boxplot
-p4_plot <- score_summary |>
-    dplyr::select(regulator_locus_tag, n_targets) |>
-    distinct() |>
-    ggplot(aes(x = "", y = n_targets)) +
-    geom_boxplot(width = 0.3) +
-    scale_y_log10() +
-    labs(title = "Distribution of Targets per Regulator", y = "N Targets (log10)", x = "") +
-    theme_minimal() +
-    theme(legend.position = "none")
-
-# Summary table
-p4_table <- gridExtra::tableGrob(n_targets_summary,
-    rows = NULL,
-    theme = ttheme_minimal(base_size = 10)
+annotated_peaks_by_promoter_df <- map(annotated_peaks_by_promoter,
+    bind_rows,
+    .id = "tmp"
 )
 
-(p1 + p3) / (p4_plot + p4_table)
-
-authors_orig_peaks <- arrow::read_parquet("~/projects/huggingface/rossi_2021/yep_filtered_peaks.parquet") |>
-    mutate(yeastepigenome_id = as.integer(yeastepigenome_id))
-authors_orig_peaks_meta <- arrow::read_parquet("~/projects/huggingface/rossi_2021/rossi_2021_metadata.parquet")
-
-authors_orig_peaks_normal_conds <- authors_orig_peaks |>
-    left_join(authors_orig_peaks_meta) |>
-    filter(treatment == "Normal", growth_media == "YPD")
-
-find_nearest_peaks <- function(macs_peaks, yep_chexmix_peaks, chrmap) {
-    library(GenomicRanges)
-    # Prepare MACS peaks (convert chr names)
-    macs_gr <- macs_peaks |>
-        left_join(chrmap |> dplyr::select(ucsc, chr)) |>
-        dplyr::select(-chr) |>
-        dplyr::rename(seqnames = ucsc) |>
-        filter(!is.na(seqnames)) |>
-        dplyr::select(seqnames, start, end, macs_score = peak_score) |>
-        GRanges()
-
-    # Prepare YEP ChExMix peaks (convert chr names)
-    yep_gr <- yep_chexmix_peaks |>
-        dplyr::select(seqnames = chr, start, end, yeastepigenome_id, yep_score = score) |>
-        GRanges()
-
-    # Find nearest neighbors
-    hits <- distanceToNearest(yep_gr, macs_gr)
-
-    # Add results back to YEP peaks
-    yep_with_nearest <- yep_chexmix_peaks |>
-        mutate(
-            query_idx = 1:n(),
-            subject_idx = subjectHits(hits),
-            distance = mcols(hits)$distance
+annotated_peaks_by_promoter_df$intergenic <- annotated_peaks_by_promoter_df$intergenic |>
+    left_join(intergenic_meta |>
+        dplyr::select(
+            promoter_id = ir_name,
+            feature_left,
+            feature_right
         ) |>
-        left_join(
-            macs_peaks |>
-                mutate(subject_idx = 1:n()) |>
-                dplyr::select(subject_idx, macs_score = peak_score, nearest_promoter_id),
-            by = "subject_idx"
+        pivot_longer(-promoter_id, values_to = "target_locus_tag") |>
+        dplyr::select(-name), relationship = "many-to-many") |>
+    dplyr::select(-promoter_id) |>
+    mutate(promoter_id = target_locus_tag)
+
+chec_meta <- arrow::read_parquet("~/projects/huggingface/rossi_2021/rossi_2021_metadata_sample.parquet")
+
+reformat_tmp <- function(df) {
+    df |>
+        separate_wider_delim(tmp,
+            delim = "_", names = c(
+                "regulator_locus_tag",
+                "regulator_symbol",
+                "treatment",
+                "growth_media"
+            ),
+            too_few = "align_start"
         ) |>
         mutate(
-            macs_score_percentile = percent_rank(macs_score)
-        )
-
-    return(yep_with_nearest)
+            treatment = ifelse(treatment == "Heat", "Heat Shock", treatment),
+            growth_media = ifelse(is.na(growth_media), "YPD", growth_media)
+        ) |>
+        mutate(target_locus_tag = promoter_id) |>
+        dplyr::select(-promoter_id) |>
+        left_join(dplyr::select(brentlab_features,
+            target_locus_tag = locus_tag,
+            target_symbol = symbol
+        )) |>
+        dplyr::relocate(regulator_locus_tag, regulator_symbol, treatment, growth_media, target_locus_tag, target_symbol) |>
+        group_by(regulator_locus_tag, treatment, growth_media) |>
+        arrange(desc(max_score)) |>
+        ungroup() |>
+        filter(
+            !is.na(target_locus_tag),
+            !is.na(target_symbol)
+        ) |>
+        left_join(dplyr::select(
+            chec_meta,
+            sample_id, regulator_locus_tag,
+            treatment, growth_media
+        )) |>
+        dplyr::relocate(sample_id)
 }
 
-chrmap <- read_csv("~/projects/huggingface/yeast_genome_resources/chrmap.csv.gz")
+annotated_peaks_by_promoter_df_out <- map(annotated_peaks_by_promoter_df, reformat_tmp)
 
-# Usage:
-authors_with_nearest <- find_nearest_peaks(
-    annotated_peaks$df$YJR060W_CBF1_Normal_YPD,
-    authors_orig_peaks_normal_conds |> filter(regulator_symbol == "CBF1"),
-    chrmap
-)
+write_out_promoter_intersect_peaks <- function(name, df) {
+    output_path <- file.path(
+        "~/projects/huggingface/rossi_2021",
+        paste0("macs_", name, ".parquet")
+    )
+    df |>
+        dplyr::select(-c(regulator_locus_tag, regulator_symbol, treatment, growth_media)) |>
+        arrow::write_parquet(output_path)
+}
 
-norm_cond_reg_syms <- intersect(
-    str_extract(names(compact(annotated_peaks$df))[str_detect(names(compact(annotated_peaks$df)), "Normal")], "(?<=_)[^_]+(?=_)"),
-    unique(authors_orig_peaks_normal_conds$regulator_symbol)
-)
+# map2(names(annotated_peaks_by_promoter_df_out),
+#      annotated_peaks_by_promoter_df_out,
+#      write_out_promoter_intersect_peaks)
 
+rossi_sample_meta <- arrow::read_parquet("~/projects/huggingface/rossi_2021/rossi_2021_metadata_sample.parquet")
 
-results <- map(norm_cond_reg_syms, ~ {
-    yep_df <- filter(authors_orig_peaks_normal_conds, regulator_symbol == .x)
-    rlt <- unique(yep_df$regulator_locus_tag)
-    macs_df <- annotated_peaks$df[[paste(rlt, .x, "Normal_YPD", sep = "_")]] |>
-        filter(peak_score > -log10(0.05))
+brentlab_features <- read_csv("~/projects/huggingface/yeast_genome_resources/brentlab_features.csv.gz")
 
-    find_nearest_peaks(
-        macs_df,
-        yep_df,
-        chrmap
+peaks_df_to_hf <- peaks_df |>
+    left_join(rossi_sample_meta) |>
+    dplyr::select(sample_id, regulator_locus_tag,
+        regulator_symbol,
+        target_locus_tag = entrez_id,
+        n_peaks, nearest_score, median_score, max_score
+    ) |>
+    left_join(dplyr::select(brentlab_features,
+        target_locus_tag = locus_tag,
+        target_symbol = symbol
+    )) |>
+    dplyr::relocate(
+        sample_id, regulator_locus_tag, regulator_symbol,
+        target_locus_tag, target_symbol
     )
-})
-
-names(results) <- norm_cond_reg_syms
-results_df <- bind_rows(results)
 
-summary(results_df$macs_score_percentile)
+# peaks_df_to_hf |>
+#     arrow::write_parquet("~/projects/huggingface/rossi_2021/macs2_annotated_peaks.parquet")
+
+# mcisaac_responsive <- arrow::read_parquet("~/projects/huggingface/hackett_2020/hackett_2020_analysis_set.parquet")
+#
+# peaks_with_mcisaac <- peaks_df |>
+#     dplyr::rename(target_locus_tag = entrez_id) |>
+#     filter(
+#         treatment == "Normal",
+#         growth_media == "YPD",
+#         median_score >= -log10(0.1)
+#     ) |>
+#     left_join(dplyr::select(
+#         mcisaac_responsive,
+#         regulator_locus_tag,
+#         target_locus_tag,
+#         time,
+#         responsive
+#     )) |>
+#     filter(!is.na(responsive))
+#
+# peaks_with_mcisaac |>
+#     filter(time == 30) |>
+#     group_by(regulator_locus_tag) |>
+#     nest() |>
+#     mutate(
+#         rr_nearest = map_dbl(data, ~ {
+#             .x |>
+#                 arrange(desc(nearest_score)) |>
+#                 slice_head(n = 25) |>
+#                 summarise(sum(responsive) / n()) |>
+#                 pull()
+#         }),
+#         rr_max = map_dbl(data, ~ {
+#             .x |>
+#                 arrange(desc(max_score)) |>
+#                 slice_head(n = 25) |>
+#                 summarise(sum(responsive) / n()) |>
+#                 pull()
+#         }),
+#         rr_median = map_dbl(data, ~ {
+#             .x |>
+#                 arrange(desc(median_score)) |>
+#                 slice_head(n = 25) |>
+#                 summarise(sum(responsive) / n()) |>
+#                 pull()
+#         })
+#     ) |>
+#     dplyr::select(-data) |>
+#     pivot_longer(
+#         cols = starts_with("rr_"),
+#         names_to = "score_type",
+#         values_to = "rr"
+#     ) |>
+#     ggplot(aes(x = score_type, y = rr)) +
+#     geom_boxplot()
+#
+# library(patchwork)
+# library(gridExtra)
+#
+# score_summary <- peaks_with_mcisaac |>
+#     filter(time == 30) |>
+#     group_by(regulator_locus_tag) |>
+#     reframe(
+#         score_type = c("nearest", "max", "median"),
+#         n_targets = c(
+#             n_distinct(target_locus_tag),
+#             n_distinct(target_locus_tag),
+#             n_distinct(target_locus_tag)
+#         ),
+#         n_peaks = c(n(), n(), n()),
+#         min = c(min(nearest_score), min(max_score), min(median_score)),
+#         max = c(max(nearest_score), max(max_score), max(median_score)),
+#         median = c(median(nearest_score), median(max_score), median(median_score)),
+#         mean = c(mean(nearest_score), mean(max_score), mean(median_score))
+#     )
+#
+#
+# p1 <- score_summary |>
+#     ggplot(aes(x = score_type, y = mean, fill = score_type)) +
+#     geom_boxplot(alpha = 0.7) +
+#     labs(title = "Mean Score Distribution", y = "Mean Score", x = "") +
+#     theme_minimal() +
+#     theme(legend.position = "none")
+#
+# p3 <- score_summary |>
+#     ggplot(aes(x = n_targets, y = mean, color = score_type)) +
+#     geom_point(alpha = 0.6) +
+#     facet_wrap(~score_type) +
+#     scale_x_log10() +
+#     labs(title = "Number of Targets vs Mean Score", x = "N Targets (log10)", y = "Mean") +
+#     theme_minimal() +
+#     theme(legend.position = "none")
+#
+# # Calculate stats for table
+# n_targets_summary <- score_summary |>
+#     dplyr::select(n_targets) |>
+#     distinct() |>
+#     pull(n_targets) %>%
+#     {
+#         tibble(
+#             Min = round(quantile(., probs = 0), 2),
+#             Q25 = round(quantile(., probs = 0.25), 2),
+#             Median = round(quantile(., probs = 0.5), 2),
+#             Q75 = round(quantile(., probs = 0.75), 2),
+#             Max = round(quantile(., probs = 1), 2)
+#         )
+#     }
+#
+# # Vertical boxplot
+# p4_plot <- score_summary |>
+#     dplyr::select(regulator_locus_tag, n_targets) |>
+#     distinct() |>
+#     ggplot(aes(x = "", y = n_targets)) +
+#     geom_boxplot(width = 0.3) +
+#     scale_y_log10() +
+#     labs(title = "Distribution of Targets per Regulator", y = "N Targets (log10)", x = "") +
+#     theme_minimal() +
+#     theme(legend.position = "none")
+#
+# # Summary table
+# p4_table <- gridExtra::tableGrob(n_targets_summary,
+#     rows = NULL,
+#     theme = ttheme_minimal(base_size = 10)
+# )
+#
+# (p1 + p3) / (p4_plot + p4_table)
+#
+# authors_orig_peaks <- arrow::read_parquet("~/projects/huggingface/rossi_2021/yep_filtered_peaks.parquet") |>
+#     mutate(yeastepigenome_id = as.integer(yeastepigenome_id))
+# authors_orig_peaks_meta <- arrow::read_parquet("~/projects/huggingface/rossi_2021/rossi_2021_metadata.parquet")
+#
+# authors_orig_peaks_normal_conds <- authors_orig_peaks |>
+#     left_join(authors_orig_peaks_meta) |>
+#     filter(treatment == "Normal", growth_media == "YPD")
+#
+# find_nearest_peaks <- function(macs_peaks, yep_chexmix_peaks, chrmap) {
+#     library(GenomicRanges)
+#     # Prepare MACS peaks (convert chr names)
+#     macs_gr <- macs_peaks |>
+#         left_join(chrmap |> dplyr::select(ucsc, chr)) |>
+#         dplyr::select(-chr) |>
+#         dplyr::rename(seqnames = ucsc) |>
+#         filter(!is.na(seqnames)) |>
+#         dplyr::select(seqnames, start, end, macs_score = peak_score) |>
+#         GRanges()
+#
+#     # Prepare YEP ChExMix peaks (convert chr names)
+#     yep_gr <- yep_chexmix_peaks |>
+#         dplyr::select(seqnames = chr, start, end, yeastepigenome_id, yep_score = score) |>
+#         GRanges()
+#
+#     # Find nearest neighbors
+#     hits <- distanceToNearest(yep_gr, macs_gr)
+#
+#     # Add results back to YEP peaks
+#     yep_with_nearest <- yep_chexmix_peaks |>
+#         mutate(
+#             query_idx = 1:n(),
+#             subject_idx = subjectHits(hits),
+#             distance = mcols(hits)$distance
+#         ) |>
+#         left_join(
+#             macs_peaks |>
+#                 mutate(subject_idx = 1:n()) |>
+#                 dplyr::select(subject_idx, macs_score = peak_score, nearest_promoter_id),
+#             by = "subject_idx"
+#         ) |>
+#         mutate(
+#             macs_score_percentile = percent_rank(macs_score)
+#         )
+#
+#     return(yep_with_nearest)
+# }
+#
+# chrmap <- read_csv("~/projects/huggingface/yeast_genome_resources/chrmap.csv.gz")
+#
+# # Usage:
+# authors_with_nearest <- find_nearest_peaks(
+#     annotated_peaks$df$YJR060W_CBF1_Normal_YPD,
+#     authors_orig_peaks_normal_conds |> filter(regulator_symbol == "CBF1"),
+#     chrmap
+# )
+#
+# norm_cond_reg_syms <- intersect(
+#     str_extract(names(compact(annotated_peaks$df))[str_detect(names(compact(annotated_peaks$df)), "Normal")], "(?<=_)[^_]+(?=_)"),
+#     unique(authors_orig_peaks_normal_conds$regulator_symbol)
+# )
+#
+#
+# results <- map(norm_cond_reg_syms, ~ {
+#     yep_df <- filter(authors_orig_peaks_normal_conds, regulator_symbol == .x)
+#     rlt <- unique(yep_df$regulator_locus_tag)
+#     macs_df <- annotated_peaks$df[[paste(rlt, .x, "Normal_YPD", sep = "_")]] |>
+#         filter(peak_score > -log10(0.05))
+#
+#     find_nearest_peaks(
+#         macs_df,
+#         yep_df,
+#         chrmap
+#     )
+# })
+#
+# names(results) <- norm_cond_reg_syms
+# results_df <- bind_rows(results)
+#
+# summary(results_df$macs_score_percentile)