adding yep and macs peaks

README.md

@@ -71,6 +71,7 @@ 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
@@ -90,6 +91,7 @@ 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: target_locus_tag
     dtype: string
@@ -242,7 +244,7 @@ configs:
 - config_name: rossi_2021_metadata_replicate
   description: Metadata describing the tagged regulator in each experiment
   dataset_type: metadata
-  applies_to: ["genome_map"]
+  applies_to: ["genome_map", "yep_filtered_peaks"]
   data_files:
   - split: train
     path: rossi_2021_metadata.parquet
@@ -267,7 +269,7 @@ 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"]
+  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"]
   data_files:
   - split: train
     path: rossi_2021_metadata_sample.parquet
@@ -375,6 +377,31 @@ configs:
       dtype: int32
       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."
+  data_files:
+  - split: train
+    path:  macs2_annotated_peaks_combined.parquet
+  dataset_info:
+    features:
+    - name: sample_id
+      dtype: string
+      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: rossi_2021_af_replicates
   description: ChIP-exo annotated features at biological replicate level with binding peaks and statistical significance metrics
   dataset_type: annotated_features

macs2_annotated_peaks_combined.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c1153f0d3b94608f484dabec7577134f89abe11678f42d43ec4dc78b68f5b367
+size 7124660

scripts/parse_rossi_filtered_peaks.R

@@ -2,7 +2,7 @@
 # https://www.datacommons.psu.edu/download/eberly/pughlab/yeast-epigenome-project/
 # and then extract them
 # with
-#!/usr/bin/env bash
+# !/usr/bin/env bash
 # fetch_yep_peaks.sh
 #
 # Extracts *_chexmix_filtered_peaks.bed from each *_YEP.zip in ZIP_DIR.
@@ -68,7 +68,6 @@ library(here)
 #' @param zero_indexed Logical, whether input is 0-indexed (default: TRUE)
 #' @return GRanges object
 bed_to_granges <- function(bed_df, zero_indexed = TRUE) {
-
     if (!all(c("chr", "start", "end") %in% names(bed_df))) {
         stop("bed_df must have columns: chr, start, end")
     }
@@ -110,19 +109,21 @@ bed_to_granges <- function(bed_df, zero_indexed = TRUE) {
 #' @return GRanges of retained gene features with metadata columns
 #'   \code{locus_tag} and \code{symbol}.
 prepare_gene_features <- function(features_df) {
-
     genes <- features_df |>
-        dplyr::filter(type == "gene",
-                      stringr::str_detect(note, stringr::fixed("dubious", ignore_case = TRUE),
-                                          negate = TRUE),
-                      chr != "chrM")
+        dplyr::filter(
+            type == "gene",
+            stringr::str_detect(note, stringr::fixed("dubious", ignore_case = TRUE),
+                negate = TRUE
+            ),
+            chr != "chrM"
+        )
 
     GenomicRanges::GRanges(
         seqnames = genes$chr,
-        ranges   = IRanges::IRanges(start = genes$start, end = genes$end),
-        strand   = genes$strand,
+        ranges = IRanges::IRanges(start = genes$start, end = genes$end),
+        strand = genes$strand,
         locus_tag = genes$locus_tag,
-        symbol    = genes$symbol
+        symbol = genes$symbol
     )
 }
 
@@ -154,64 +155,74 @@ prepare_gene_features <- function(features_df) {
 #'
 #' @seealso \code{\link{bed_to_granges}}, \code{\link{prepare_gene_features}}
 annotate_peaks_to_features <- function(peaks_df, features_gr, nbp = 500) {
-
     peaks_gr <- bed_to_granges(peaks_df)
 
     # round 1: nearest downstream feature (strand-agnostic) and its distance
     ds_idx <- GenomicRanges::precede(peaks_gr, features_gr, ignore.strand = TRUE)
-    keep   <- !is.na(ds_idx)
+    keep <- !is.na(ds_idx)
 
-    peaks_gr  <- peaks_gr[keep]
+    peaks_gr <- peaks_gr[keep]
     feat_hits <- features_gr[ds_idx[keep]]
 
     round1 <- tibble::tibble(
-        score            = peaks_df$score[keep],
+        score = peaks_df$score[keep],
         target_locus_tag = feat_hits$locus_tag,
-        target_symbol    = feat_hits$symbol,
-        distance         = GenomicRanges::distance(peaks_gr, feat_hits,
-                                                   ignore.strand = TRUE)) |>
+        target_symbol = feat_hits$symbol,
+        distance = GenomicRanges::distance(peaks_gr, feat_hits,
+            ignore.strand = TRUE
+        )
+    ) |>
         dplyr::filter(distance <= nbp)
 
     # round 2: aggregate peaks per target gene, median score as the peak score
     round1 |>
         dplyr::group_by(target_locus_tag, target_symbol) |>
-        dplyr::reframe(peak_score = median(score),
-                       n_peaks    = dplyr::n(),
-                       max_distance = max(distance))
+        dplyr::reframe(
+            peak_score = median(score),
+            n_peaks = dplyr::n(),
+            max_distance = max(distance)
+        )
 }
 
 
-rossi_meta = arrow::read_parquet("~/code/hf/rossi_2021/rossi_2021_metadata.parquet")
-chrmap = read_csv("~/code/hf/yeast_genome_resources/chrmap.csv.gz")
+rossi_meta <- arrow::read_parquet("~/projects/huggingface/rossi_2021/rossi_2021_metadata.parquet")
+chrmap <- read_csv("~/projects/huggingface/yeast_genome_resources/chrmap.csv.gz")
 
-brentlab_features = arrow::read_parquet("~/code/hf/yeast_genome_resources/brentlab_features.parquet")
+brentlab_features <- arrow::read_parquet("~/projects/huggingface/yeast_genome_resources/brentlab_features.parquet")
 
 # protein-coding gene features used to annotate peaks (excludes dubious ORFs)
-gene_features_gr = prepare_gene_features(brentlab_features)
+gene_features_gr <- prepare_gene_features(brentlab_features)
 
-filtered_bed = list.files(here("data/yep_filtered_peaks"), full.names = TRUE)
-names(filtered_bed) = str_extract(basename(filtered_bed), "^\\d+")
+filtered_bed <- list.files(here("data/yep_filtered_peaks"), full.names = TRUE)
+names(filtered_bed) <- str_extract(basename(filtered_bed), "^\\d+")
 
-read_in_filtered_bed = function(path){
-    read_tsv(path, col_names = c('chr', 'start', 'end', 'source', 'score')) |>
+read_in_filtered_bed <- function(path) {
+    read_tsv(path, col_names = c("chr", "start", "end", "source", "score")) |>
         left_join(dplyr::select(chrmap, chr, ucsc)) |>
         dplyr::select(-chr) |>
         dplyr::rename(chr = ucsc) |>
         dplyr::relocate(chr)
 }
 
-filtered_bed_df_list = map(filtered_bed, read_in_filtered_bed)
+filtered_bed_df_list <- map(filtered_bed, read_in_filtered_bed)
+
+filtered_bed_df_list_no_null <- filtered_bed_df_list[unlist(map(filtered_bed_df_list, ~ nrow(.) > 0))]
 
-filtered_bed_df_list_no_null = filtered_bed_df_list[unlist(map(filtered_bed_df_list, ~nrow(.) > 0))]
+filtered_bed_df_list_no_null_in_meta <- filtered_bed_df_list_no_null[
+    as.integer(names(filtered_bed_df_list_no_null)) %in% rossi_meta$yeastepigenome_id
+]
 
-filtered_bed_df_list_no_null_in_meta = filtered_bed_df_list_no_null[
-    as.integer(names(filtered_bed_df_list_no_null)) %in% rossi_meta$yeastepigenome_id]
+# bind_rows(filtered_bed_df_list_no_null_in_meta, .id = 'yeastepigenome_id') |>
+#     dplyr::select(yeastepigenome_id, chr, start, end, score) |>
+#     arrow::write_parquet("~/projects/huggingface/rossi_2021/yep_filtered_peaks.parquet")
 
 # annotate each sample's peaks to nearest downstream genes within 500bp
-annotated = map(filtered_bed_df_list_no_null_in_meta,
-                annotate_peaks_to_features, gene_features_gr, nbp = 500)
+annotated <- map(filtered_bed_df_list_no_null_in_meta,
+    annotate_peaks_to_features, gene_features_gr,
+    nbp = 500
+)
 
-annotated_df = bind_rows(annotated, .id = "yeastepigenome_id") |>
+annotated_df <- bind_rows(annotated, .id = "yeastepigenome_id") |>
     mutate(yeastepigenome_id = as.integer(yeastepigenome_id)) |>
     left_join(rossi_meta, by = "yeastepigenome_id") |>
     group_by(sample_id, target_locus_tag, target_symbol) |>
@@ -219,7 +230,8 @@ annotated_df = bind_rows(annotated, .id = "yeastepigenome_id") |>
         peak_score = median(peak_score),
         n_peaks = sum(n_peaks),
         max_distance = max(max_distance),
-        peak_n_replicates = n()) |>
+        peak_n_replicates = n()
+    ) |>
     arrange(desc(peak_score), .by_group = TRUE)
 
 # arrow::write_parquet(

scripts/rossi_peak_analysis.R

@@ -0,0 +1,325 @@
+library(tidyverse)
+library(janitor)
+library(GenomicRanges)
+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"
+
+
+read_in_annotated_peaks <- function(peak_path) {
+    df <- read_tsv(peak_path, show_col_types = FALSE)
+
+    # Skip empty annotations
+    if (nrow(df) == 0) {
+        warning(sprintf("Skipping empty annotation file: %s", basename(peak_path)))
+        return(NULL)
+    }
+
+    tryCatch(
+        {
+            # Convert peaks to GRanges for overlap detection
+            peaks_gr <- GenomicRanges::GRanges(
+                seqnames = df$Chr,
+                ranges = IRanges::IRanges(start = df$Start, end = df$End),
+                strand = df$Strand
+            )
+
+            # Find overlaps with exclude regions
+            overlaps <- GenomicRanges::findOverlaps(peaks_gr, exclude_regions)
+
+            # Label peaks that overlap with exclude regions
+            df <- df |>
+                mutate(
+                    in_exclude_region = seq_len(nrow(df)) %in% S4Vectors::queryHits(overlaps)
+                ) |>
+                janitor::clean_names()
+
+            return(df)
+        },
+        error = function(e) {
+            warning(sprintf("Error processing: %s. Error: %s", basename(peak_path), e$message))
+            return(NULL)
+        }
+    )
+}
+
+score_targets <- function(df, min_dist = 0, max_dist = 700) {
+    df |>
+        filter(!in_exclude_region) |>
+        filter(
+            peak_score > -log10(0.1),
+            str_detect(nearest_promoter_id, "mRNA"),
+            between(distance_to_tss, min_dist, max_dist)
+        ) |>
+        group_by(entrez_id) |>
+        reframe(
+            n_peaks = n(),
+            nearest_score = peak_score[which.min(abs(distance_to_tss))],
+            median_score = median(peak_score),
+            max_score = max(peak_score)
+        )
+}
+
+
+annotated_peaks <- list(
+    files = list.files(here("data/chipexo_macs3/annotated_peaks"),
+        "_annotated_peaks.txt",
+        full.names = TRUE,
+        recursive = TRUE
+    )
+)
+names(annotated_peaks$files) <- str_remove(
+    basename(annotated_peaks$files),
+    "_annotated_peaks.txt"
+)
+
+annotated_peaks$df <- map(annotated_peaks$files, read_in_annotated_peaks)
+
+annotated_peaks$dfcomp
+
+annotated_peaks$target_score <- map(compact(annotated_peaks$df), score_targets)
+
+peaks_df <- bind_rows(annotated_peaks$target_score, .id = "tmp") |>
+    separate_wider_delim(tmp,
+        delim = "_", names = c(
+            "regulator_locus_tag",
+            "regulator_symbol",
+            "treatment",
+            "growth_media"
+        ),
+        too_few = "align_start"
+    ) |>
+    mutate(
+        treatment = ifelse(treatment == "Heat", "Heat Shock", treatment),
+        growth_media = ifelse(is.na(growth_media), "YPD", growth_media)
+    )
+
+
+rossi_sample_meta <- arrow::read_parquet("~/projects/huggingface/rossi_2021/rossi_2021_metadata_sample.parquet")
+
+brentlab_features <- read_csv("~/projects/huggingface/yeast_genome_resources/brentlab_features.csv.gz")
+
+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
+    )
+
+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)

yep_filtered_peaks.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:38ec6d47a44317a391ce3afcfc9c8aa19f1e5997865d63bbb8c4884693853e91
-size 3619341
+oid sha256:5827b4bc79da5efdd878be32eeda371e129d2091613d7cf76fc985b97ae2beec
+size 3618047