major reorganization and reprocessing. scripts added

README.md

@@ -74,159 +74,243 @@ configs:
     - name: pileup
       dtype: int32
       description: "Depth of coverage (number of 5' tags) at this genomic position"
-- config_name: rossi_annotated_features
-  description: ChIP-exo regulator-target binding features with peak statistics
-  dataset_type: annotated_features
-  default: true
-  metadata_fields:
-    - regulator_locus_tag
-    - regulator_symbol
-    - target_locus_tag
+
+- config_name: rossi_2021_metadata
+  description: Replicate-level metadata for ChIP-exo experiments including experimental conditions and sample information
+  dataset_type: metadata
+  applies_to: ["rossi_2021_af_replicates"]
+  data_files:
+  - split: train
+    path: rossi_2021_metadata.parquet
+  dataset_info:
+    features:
+    - name: regulator_locus_tag
+      dtype: string
+      description: Systematic gene identifier for the transcription factor
+      role: regulator_identifier
+    - name: regulator_symbol
+      dtype: string
+      description: Standard gene symbol for the transcription factor
+      role: regulator_identifier
+    - name: run_accession
+      dtype: string
+      description: SRA run accession identifier for this biological replicate
+    - name: yeastepigenome_id
+      dtype: string
+      description: Identifier from the Yeast Epigenome Project
+    - name: treatment
+      dtype: string
+      description: Experimental treatment condition
+      role: experimental_condition
+    - name: growth_media
+      dtype: string
+      description: Growth media composition
+      role: experimental_condition
+    - name: antibody
+      dtype: string
+      description: Antibody used for ChIP-exo immunoprecipitation
+    - name: sample_id
+      dtype: string
+      description: Unique identifier for the biological replicate
+
+- 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"]
   data_files:
-    - split: train
-      path: yeastepigenome_annotatedfeatures.parquet
+  - split: train
+    path: rossi_2021_metadata_sample.parquet
   dataset_info:
     features:
-      - name: sample_id
-        dtype: int32
-        description: >-
-          Unique identifier for each ChIP-exo experimental sample.
-      - name: pss_id
-        dtype: float64
-        description: >-
-          Current brentlab promotersetsig table id. This will eventually be removed.
-      - name: binding_id
-        dtype: float64
-        description: >-
-          Current brentlab binding table id. This will eventually be removed.
-      - name: yeastepigenome_id
-        dtype: float64
-        description: >-
-          Unique identifier in the yeastepigenome database.
-      - name: regulator_locus_tag
-        dtype: string
-        description: >-
-          Systematic ORF name of the regulator.
-        role: regulator_identifier
-      - name: regulator_symbol
-        dtype: string
-        description: >-
-          Common gene name of the regulator.
-        role: regulator_identifier
-      - name: target_locus_tag
-        dtype: string
-        description: >-
-          The systematic ID of the feature to which the effect/pvalue is
-          assigned. See hf/BrentLab/yeast_genome_resources
-        role: target_identifier
-      - name: target_symbol
-        dtype: string
-        description: >-
-          The common name of the feature to which the effect/pvalue is
-          assigned. If there is no common name, the `target_locus_tag` is
-          used.
-        role: target_identifier
-      - name: n_sig_peaks
-        dtype: float64
-        description: >-
-          Number of peaks in the promoter region of the the target gene
-        role: quantitative_measure
-      - name: max_fc
-        dtype: float64
-        description: >-
-          If there are multiple peaks in the promoter region, then the maximum is
-          reported. Otherwise, it is the fold change of the single peak in the
-          promoter.
-        role: quantitative_measure
-      - name: min_pval
-        dtype: float64
-        description: >-
-          The most significant p-value among peaks for this interaction. 
-        role: quantitative_measure
-- config_name: reprocess_annotatedfeatures
-  description: >-
-    Annotated features reprocessed with updated peak
-    calling methodology
+    - name: regulator_locus_tag
+      dtype: string
+      description: Systematic gene identifier for the transcription factor
+      role: regulator_identifier
+    - name: regulator_symbol
+      dtype: string
+      description: Standard gene symbol for the transcription factor
+      role: regulator_identifier
+    - name: treatment
+      dtype: string
+      description: Experimental treatment condition
+      role: experimental_condition
+    - name: growth_media
+      dtype: string
+      description: Growth media composition
+      role: experimental_condition
+    - name: antibody
+      dtype: string
+      description: Antibody used for ChIP-exo immunoprecipitation
+    - name: sample_id
+      dtype: string
+      description: Unique identifier combining regulator and replicates
+
+- 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
   data_files:
-    - split: train
-      path: reprocess_annotatedfeatures.parquet
+  - split: train
+    path: rossi_2021_af_replicates.parquet
   dataset_info:
     features:
-      - name: regulator_locus_tag
-        dtype: string
-        description: Systematic gene name (ORF identifier) of the transcription factor
-      - name: regulator_symbol
-        dtype: string
-        description: Standard gene symbol of the transcription factor
-      - name: target_locus_tag
-        dtype: string
-        description: Systematic gene name (ORF identifier) of the target gene
-      - name: target_symbol
-        dtype: string
-        description: Standard gene symbol of the target gene
-      - name: baseMean
-        dtype: float64
-        description: Average of normalized count values, dividing by size factors, taken over all samples
-      - name: log2FoldChange
-        dtype: float64
-        description: Log2 fold change between comparison and control groups
-      - name: lfcSE
-        dtype: float64
-        description: Standard error estimate for the log2 fold change estimate
-      - name: stat
-        dtype: float64
-        description: Value of the test statistic for the gene
-      - name: pvalue
-        dtype: float64
-        description: P-value of the test for the gene
-      - name: padj
-        dtype: float64
-        description: Adjusted p-value for multiple testing for the gene
-- config_name: reprocess_annotatedfeatures_tagcounts
-  description: Another version of the reprocessed data, quantified similarly to Calling Cards
+    - name: sample_id
+      dtype: string
+      description: Unique identifier for the biological replicate
+      role: sample_id
+    - name: run_accession
+      dtype: string
+      description: SRA run accession identifier for this biological replicate
+    - name: regulator_locus_tag
+      dtype: string
+      description: Systematic gene identifier for the transcription factor
+      role: regulator_identifier
+    - name: regulator_symbol
+      dtype: string
+      description: Standard gene symbol for the transcription factor
+      role: regulator_identifier
+    - name: target_locus_tag
+      dtype: string
+      description: Systematic gene identifier for the target gene
+      role: target_identifier
+    - name: target_symbol
+      dtype: string
+      description: Standard gene symbol for the target gene
+      role: target_identifier
+    - name: seqnames
+      dtype: string
+      description: Chromosome identifier (e.g., chrI, chrII, chrXVI)
+    - name: start
+      dtype: int64
+      description: Promoter region start position (1-based coordinate)
+    - name: end
+      dtype: int64
+      description: Promoter region end position (1-based, inclusive)
+    - name: background_counts
+      dtype: int64
+      description: Read counts in the background/control sample for this peak region
+      role: quantitative_measure
+    - name: experiment_counts
+      dtype: int64
+      description: Read counts in the ChIP-exo experiment sample for this peak region
+      role: quantitative_measure
+    - name: total_background_counts
+      dtype: int64
+      description: Total read counts across the entire genome in the background sample
+      role: quantitative_measure
+    - name: total_experiment_counts
+      dtype: int64
+      description: Total read counts across the entire genome in the experiment sample
+      role: quantitative_measure
+    - name: enrichment
+      dtype: float64
+      description: Enrichment score for the binding peak
+      role: quantitative_measure
+    - name: poisson_pval
+      dtype: float64
+      description: P-value from Poisson distribution test for peak significance
+      role: quantitative_measure
+    - name: log_poisson_pval
+      dtype: float64
+      description: Log-transformed Poisson p-value
+      role: quantitative_measure
+    - name: hypergeometric_pval
+      dtype: float64
+      description: P-value from hypergeometric distribution test for peak significance
+      role: quantitative_measure
+    - name: log_hypergeometric_pval
+      dtype: float64
+      description: Log-transformed hypergeometric p-value
+      role: quantitative_measure
+    - name: poisson_qval
+      dtype: float64
+      description: FDR-adjusted q-value from Poisson test (multiple testing correction)
+      role: quantitative_measure
+    - name: hypergeometric_qval
+      dtype: float64
+      description: FDR-adjusted q-value from hypergeometric test (multiple testing correction)
+      role: quantitative_measure
+
+- config_name: rossi_2021_af_combined
+  description: Combined ChIP-exo annotated features with binding peaks and statistical significance metrics aggregated across biological replicates
   dataset_type: annotated_features
   data_files:
-    - split: train
-      path: reprocess_annotatedfeatures_tagcounts.parquet
+  - split: train
+    path: rossi_2021_af_combined.parquet
   dataset_info:
     features:
-      - name: regulator_locus_tag
-        dtype: string
-        description: Systematic gene name (ORF identifier) of the transcription factor
-        role: regulator_identifier
-      - name: target_locus_tag
-        dtype: string
-        description: Systematic gene name (ORF identifier) of the target gene
-        role: target_identifier
-      - name: rank
-        dtype: int64
-        description: Rank (ties method min rank) of the peak based on pvalue with ties broken by enrichment. Largest rank is most significant.
-      - name: control_count
-        dtype: int64
-        description: Number of tags in the control condition
-      - name: experimental_count
-        dtype: int64
-        description: Number of tags in the experimental condition
-      - name: mu
-        dtype: float64
-        description: Expected count under the null hypothesis (control_count + 1) * (experimental_total_tags / control_total_tags)
-      - name: enrichment
-        dtype: float64
-        description: Enrichment ratio of experimental over control. (experimental_counts / experimental_total) / (control_counts + pseudocount) / control_total
-        role: quantitative_measure
-      - name: log2_enrichment
-        dtype: float64
-        description: Log2-transformed enrichment ratio
-        role: quantitative_measure
-      - name: neg_log10_pvalue
-        dtype: float64
-        description: Negative log10 of the p-value for binding significance
-        role: quantitative_measure
-      - name: neg_log10_qvalue
-        dtype: float64
-        description: Negative log10 of the FDR-adjusted q-value
-        role: quantitative_measure
+    - name: sample_id
+      dtype: string
+      description: Unique identifier combining regulator and replicates
+      role: sample_id
+    - name: regulator_locus_tag
+      dtype: string
+      description: Systematic gene identifier for the transcription factor
+      role: regulator_identifier
+    - name: regulator_symbol
+      dtype: string
+      description: Standard gene symbol for the transcription factor
+      role: regulator_identifier
+    - name: target_locus_tag
+      dtype: string
+      description: Systematic gene identifier for the target gene
+      role: target_identifier
+    - name: target_symbol
+      dtype: string
+      description: Standard gene symbol for the target gene
+      role: target_identifier
+    - name: seqnames
+      dtype: string
+      description: Chromosome identifier (e.g., chrI, chrII, chrXVI)
+    - name: start
+      dtype: int64
+      description: Promoter region start position (1-based coordinate)
+    - name: end
+      dtype: int64
+      description: Promoter region end position (1-based, inclusive)
+    - name: background_counts
+      dtype: int64
+      description: Combined read counts in the background/control sample for this peak region
+      role: quantitative_measure
+    - name: experiment_counts
+      dtype: int64
+      description: Combined read counts in the ChIP-exo experiment sample for this peak region
+      role: quantitative_measure
+    - name: total_background_counts
+      dtype: int64
+      description: Total read counts across the entire genome in the combined background sample
+      role: quantitative_measure
+    - name: total_experiment_counts
+      dtype: int64
+      description: Total read counts across the entire genome in the combined experiment sample
+      role: quantitative_measure
+    - name: enrichment
+      dtype: float64
+      description: Enrichment score for the binding peak calculated from combined replicates
+      role: quantitative_measure
+    - name: poisson_pval
+      dtype: float64
+      description: P-value from Poisson distribution test for peak significance
+      role: quantitative_measure
+    - name: log_poisson_pval
+      dtype: float64
+      description: Log-transformed Poisson p-value
+      role: quantitative_measure
+    - name: hypergeometric_pval
+      dtype: float64
+      description: P-value from hypergeometric distribution test for peak significance
+      role: quantitative_measure
+    - name: log_hypergeometric_pval
+      dtype: float64
+      description: Log-transformed hypergeometric p-value
+      role: quantitative_measure
+    - name: poisson_qval
+      dtype: float64
+      description: FDR-adjusted q-value from Poisson test (multiple testing correction)
+      role: quantitative_measure
+    - name: hypergeometric_qval
+      dtype: float64
+      description: FDR-adjusted q-value from hypergeometric test (multiple testing correction)
+      role: quantitative_measure
 ---
 # Rossi 2021
 
@@ -235,18 +319,6 @@ This work was published in
 
 [Rossi MJ, Kuntala PK, Lai WKM, Yamada N, Badjatia N, Mittal C, Kuzu G, Bocklund K, Farrell NP, Blanda TR, Mairose JD, Basting AV, Mistretta KS, Rocco DJ, Perkinson ES, Kellogg GD, Mahony S, Pugh BF. A high-resolution protein architecture of the budding yeast genome. Nature. 2021 Apr;592(7853):309-314. doi: 10.1038/s41586-021-03314-8. Epub 2021 Mar 10. PMID: 33692541; PMCID: PMC8035251.](https://doi.org/10.1038/s41586-021-03314-8)
 
-This repo provides 4 datasets:
-
-- **rossi_2021_metadata**: Metadata describing the tagged regulator in each
-  experiment.
-- **genome_map**: ChIP-exo 5' tag coverage data partitioned by sample accession.
-- **reprocess_annotatedfeatures**: This data was reprocessed from the fastq files
-  on GEO. See scripts/reprocessing_details.txt for more information.
-- **yeastepigenome_annotatedfeatures**: ChIP-exo regulator-target binding features
-  with peak statistics.
-- **reprocess_annotatedfeatures_tagcounts**: Reprocessed using a similar method to
-  the calling cards quantification
-
 ## Usage
 
 The python package `tfbpapi` provides an interface to this data which eases

yeastepigenome_annotatedfeatures.parquet → deprecated_rossi_2021_metadata.parquet

@@ -1,3 +1,3 @@
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-size 2551155
+oid sha256:402b3f50713da090b7c6b33b4d36f70acefa2f56f2fc88eba9f24d08a80a3395
+size 27770

reprocess_annotatedfeatures.parquet → genome_map_control_meta.parquet

@@ -1,3 +1,3 @@
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-oid sha256:1ec8e7d23fb9c494a15a36232ffc7564017c2f9a4562071b822cc33642b6e295
-size 115758673
+oid sha256:49fd5f7062f1b700756371d4174769f9a6f14d68ad7d60d9b273b0d4bea188ce
+size 1111

reprocess_annotatedfeatures.parquet.md5

@@ -1 +0,0 @@
-a277cae297585ab8db9bef0248485d89  reprocess_annotatedfeatures.parquet

reprocess_annotatedfeatures_tagcounts.parquet.md5

@@ -1 +0,0 @@
-026a02d21abbe2809291c8ced11464ec  reprocess_annotatedfeatures_tagcounts.parquet

reprocess_annotatedfeatures_tagcounts.parquet → rossi_2021_af_combined.parquet

@@ -1,3 +1,3 @@
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-size 173799419
+oid sha256:7bf68b04e72863c5cacc4f3da774e572fa71e646fc29bda8dcab04e4fc545c56
+size 276431235

rossi_2021_af_replicates.parquet

@@ -0,0 +1,3 @@
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+oid sha256:1ef0bfd35bdfead581ee8fb799f041228d428e7ff2bf412c3b0fff031c18d8a0
+size 416707331

rossi_2021_metadata.parquet

@@ -1,3 +1,3 @@
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-oid sha256:8dbda57bbf680e0706bf9d7cb298848dfb09a7dfa744e79d7e9e5b6208a64e09
-size 14696
+oid sha256:ffd0d5ad6fdfcc19dbc0cd6a15d95a1c074e9484562c9613b263e8bf06f2ae4e
+size 33667

rossi_2021_metadata.parquet.md5

@@ -1 +0,0 @@
-b5cfc02f0086313ac1c7c290b189b1e7  rossi_2021_metadata.parquet

rossi_2021_metadata_sample.parquet

@@ -0,0 +1,3 @@
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+oid sha256:a033c8b32656b6b38ecefb22a980a8f4d23737271ed84d8b0fb2262960916688
+size 16751

scripts/genomecov_to_annotated_features.R

@@ -0,0 +1,626 @@
+library(tidyverse)
+library(arrow)
+library(here)
+library(yaml)
+
+#' Convert BED format data frame to GRanges
+#'
+#' Handles coordinate system conversion from 0-indexed half-open BED format
+#' to 1-indexed closed GenomicRanges format
+#'
+#' @param bed_df Data frame with chr, start, end columns in BED format (0-indexed, half-open)
+#' @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")
+    }
+
+    # Convert from 0-indexed half-open [start, end) to 1-indexed closed [start, end]
+    if (zero_indexed) {
+        gr_start <- bed_df$start + 1
+        gr_end <- bed_df$end
+    } else {
+        gr_start <- bed_df$start
+        gr_end <- bed_df$end
+    }
+
+    # Create GRanges object (strand-agnostic for calling cards)
+    gr <- GenomicRanges::GRanges(
+        seqnames = bed_df$chr,
+        ranges = IRanges::IRanges(start = gr_start, end = gr_end),
+        strand = "*"
+    )
+
+    # Add any additional metadata columns
+    extra_cols <- setdiff(names(bed_df), c("chr", "start", "end", "strand"))
+    if (length(extra_cols) > 0) {
+        GenomicRanges::mcols(gr) <- bed_df[, extra_cols, drop = FALSE]
+    }
+
+    return(gr)
+}
+
+#' Convert point-wise coverage to BED format
+#'
+#' @param coverage_df Data frame with chr, pos, pileup columns
+#' @return Data frame in BED format with chr, start, end, score
+coverage_to_bed <- function(coverage_df) {
+    coverage_df %>%
+        dplyr::rename(start = pos, score = pileup) %>%
+        dplyr::mutate(end = start + 1) %>%  # pos is 0-indexed, end is exclusive
+        dplyr::select(chr, start, end, score)
+}
+
+#' Sum scores of overlapping insertions per region
+#'
+#' @param insertions_gr GRanges object with insertions containing a 'score' metadata column
+#' @param regions_gr GRanges object with regions
+#' @return Numeric vector of summed scores per region
+sum_overlap_scores <- function(insertions_gr, regions_gr) {
+    # Find overlaps between regions and insertions
+    overlaps <- GenomicRanges::findOverlaps(regions_gr, insertions_gr)
+
+    # If no overlaps, return zeros
+    if (length(overlaps) == 0) {
+        return(rep(0, length(regions_gr)))
+    }
+
+    # Extract the scores for overlapping insertions
+    scores <- GenomicRanges::mcols(insertions_gr)$score[S4Vectors::subjectHits(overlaps)]
+
+    # Sum scores per region using tapply
+    summed_scores <- tapply(scores, S4Vectors::queryHits(overlaps), sum)
+
+    # Create result vector with zeros for regions without overlaps
+    result <- rep(0, length(regions_gr))
+    result[as.integer(names(summed_scores))] <- summed_scores
+
+    return(result)
+}
+
+#' Combine replicates for a given regulator
+#'
+#' @param sample_set_id sample_id that identifies a set of replicates
+#' @param genomecov_data List containing meta and ds (tagged experiment data)
+#' @param regions_gr GRanges object with regions to quantify
+combine_replicates_af <- function(sample_set_id, genomecov_data, regions_gr) {
+
+    message(sprintf("Working on sample_id: %s", sample_set_id))
+
+    run_accession_list <- genomecov_data$meta %>%
+        filter(sample_id == sample_set_id) %>%
+        pull(run_accession)
+
+    library_totals <- genomecov_data$ds %>%
+        filter(accession %in% run_accession_list) %>%
+        group_by(accession) %>%
+        summarise(n = sum(pileup, na.rm = TRUE)) %>%
+        collect()
+
+    replicate_region_counts <- map(run_accession_list, ~{
+        run_acc <- .x
+
+        coverage_gr <- genomecov_data$ds %>%
+            filter(accession == run_acc) %>%
+            collect() %>%
+            coverage_to_bed() %>%
+            bed_to_granges()
+
+        sum_overlap_scores(coverage_gr, regions_gr)
+    })
+
+    replicates <- map2(replicate_region_counts, run_accession_list, ~{
+        replicate_regions <- regions_gr
+        replicate_regions$score <- .x
+        replicate_regions
+    })
+    names(replicates) <- run_accession_list
+
+    combined <- regions_gr
+    combined$score <- Reduce(`+`, replicate_region_counts)
+
+    list(
+        library_total = library_totals,
+        replicates = replicates,
+        combined = combined
+    )
+}
+
+#' Combine control samples
+#'
+#' @param genomecov_control List containing meta and ds (control data)
+#' @param regions_gr GRanges object with regions to quantify
+combine_control_af <- function(genomecov_control, regions_gr) {
+
+    message("Processing control samples...")
+
+    library_totals <- genomecov_control$ds %>%
+        group_by(accession) %>%
+        summarise(n = sum(pileup, na.rm = TRUE)) %>%
+        collect()
+
+    replicate_region_counts <- map(genomecov_control$meta$accession, ~{
+        run_acc <- .x
+
+        coverage_gr <- genomecov_control$ds %>%
+            filter(accession == run_acc) %>%
+            collect() %>%
+            coverage_to_bed() %>%
+            bed_to_granges()
+
+        sum_overlap_scores(coverage_gr, regions_gr)
+    })
+
+    out <- regions_gr
+    out$score <- Reduce(`+`, replicate_region_counts)
+
+    list(
+        library_totals = library_totals,
+        af = out
+    )
+}
+
+#' Calculate enrichment (calling cards effect)
+#'
+#' @param total_background_counts Total number of counts in background (scalar or vector)
+#' @param total_experiment_counts Total number of counts in experiment (scalar or vector)
+#' @param background_counts Number of counts in background per region (vector)
+#' @param experiment_counts Number of counts in experiment per region (vector)
+#' @param pseudocount Pseudocount to avoid division by zero (default: 0.1)
+#' @return Enrichment values
+calculate_enrichment <- function(total_background_counts,
+                                 total_experiment_counts,
+                                 background_counts,
+                                 experiment_counts,
+                                 pseudocount = 0.1) {
+
+    # Input validation
+    if (!all(is.numeric(c(total_background_counts, total_experiment_counts,
+                          background_counts, experiment_counts)))) {
+        stop("All inputs must be numeric")
+    }
+
+    # Get the length of the region vectors
+    n_regions <- length(background_counts)
+
+    # Ensure experiment_counts is same length as background_counts
+    if (length(experiment_counts) != n_regions) {
+        stop("background_counts and experiment_counts must be the same length")
+    }
+
+    # Recycle scalar totals to match region length if needed
+    if (length(total_background_counts) == 1) {
+        total_background_counts <- rep(total_background_counts, n_regions)
+    }
+    if (length(total_experiment_counts) == 1) {
+        total_experiment_counts <- rep(total_experiment_counts, n_regions)
+    }
+
+    # Now check all are same length
+    if (length(total_background_counts) != n_regions ||
+        length(total_experiment_counts) != n_regions) {
+        stop("All input vectors must be the same length or scalars")
+    }
+
+    # Calculate enrichment
+    numerator <- experiment_counts / total_experiment_counts
+    denominator <- (background_counts + pseudocount) / total_background_counts
+    enrichment <- numerator / denominator
+
+    # Check for invalid values
+    if (any(enrichment < 0, na.rm = TRUE)) {
+        stop("Enrichment values must be non-negative")
+    }
+    if (any(is.na(enrichment))) {
+        stop("Enrichment values must not be NA")
+    }
+    if (any(is.infinite(enrichment))) {
+        stop("Enrichment values must not be infinite")
+    }
+
+    return(enrichment)
+}
+
+#' Calculate Poisson p-values
+#'
+#' @param total_background_counts Total number of counts in background (scalar or vector)
+#' @param total_experiment_counts Total number of counts in experiment (scalar or vector)
+#' @param background_counts Number of counts in background per region (vector)
+#' @param experiment_counts Number of counts in experiment per region (vector)
+#' @param pseudocount Pseudocount for lambda calculation (default: 0.1)
+#' @param ... additional arguments to `ppois`. note that lower tail is set to FALSE already
+#' @return Poisson p-values
+calculate_poisson_pval <- function(total_background_counts,
+                                   total_experiment_counts,
+                                   background_counts,
+                                   experiment_counts,
+                                   pseudocount = 0.1,
+                                   ...) {
+
+    # Input validation
+    if (!all(is.numeric(c(total_background_counts, total_experiment_counts,
+                          background_counts, experiment_counts)))) {
+        stop("All inputs must be numeric")
+    }
+
+    # Get the length of the region vectors
+    n_regions <- length(background_counts)
+
+    # Ensure experiment_counts is same length as background_counts
+    if (length(experiment_counts) != n_regions) {
+        stop("background_counts and experiment_counts must be the same length")
+    }
+
+    # Recycle scalar totals to match region length if needed
+    if (length(total_background_counts) == 1) {
+        total_background_counts <- rep(total_background_counts, n_regions)
+    }
+    if (length(total_experiment_counts) == 1) {
+        total_experiment_counts <- rep(total_experiment_counts, n_regions)
+    }
+
+    # Now check all are same length
+    if (length(total_background_counts) != n_regions ||
+        length(total_experiment_counts) != n_regions) {
+        stop("All input vectors must be the same length or scalars")
+    }
+
+    # Calculate hop ratio
+    hop_ratio <- total_experiment_counts / total_background_counts
+
+    # Calculate expected number of counts (mu/lambda parameter)
+    # Add pseudocount to avoid mu = 0
+    mu <- (background_counts + pseudocount) * hop_ratio
+
+    # Observed counts in experiment
+    x <- experiment_counts
+
+    # Calculate p-value: P(X >= x) = 1 - P(X < x) = 1 - P(X <= x-1)
+    pval <- ppois(x - 1, lambda = mu, lower.tail = FALSE, ...)
+
+    return(pval)
+}
+
+#' Calculate hypergeometric p-values
+#'
+#' @param total_background_counts Total number of counts in background (scalar or vector)
+#' @param total_experiment_counts Total number of counts in experiment (scalar or vector)
+#' @param background_counts Number of counts in background per region (vector)
+#' @param experiment_counts Number of counts in experiment per region (vector)
+#' @param ... additional arguments to phyper. Note that lower tail is set to false already
+#' @return Hypergeometric p-values
+calculate_hypergeom_pval <- function(total_background_counts,
+                                     total_experiment_counts,
+                                     background_counts,
+                                     experiment_counts,
+                                     ...) {
+
+    # Input validation
+    if (!all(is.numeric(c(total_background_counts, total_experiment_counts,
+                          background_counts, experiment_counts)))) {
+        stop("All inputs must be numeric")
+    }
+
+    # Get the length of the region vectors
+    n_regions <- length(background_counts)
+
+    # Ensure experiment_counts is same length as background_counts
+    if (length(experiment_counts) != n_regions) {
+        stop("background_counts and experiment_counts must be the same length")
+    }
+
+    # Recycle scalar totals to match region length if needed
+    if (length(total_background_counts) == 1) {
+        total_background_counts <- rep(total_background_counts, n_regions)
+    }
+    if (length(total_experiment_counts) == 1) {
+        total_experiment_counts <- rep(total_experiment_counts, n_regions)
+    }
+
+    # Now check all are same length
+    if (length(total_background_counts) != n_regions ||
+        length(total_experiment_counts) != n_regions) {
+        stop("All input vectors must be the same length or scalars")
+    }
+
+    # Hypergeometric parameters
+    M <- total_background_counts + total_experiment_counts
+    n <- total_experiment_counts
+    N <- background_counts + experiment_counts
+    x <- experiment_counts - 1
+
+    # Handle edge cases
+    valid <- (M >= 1) & (N >= 1)
+    pval <- rep(1, length(M))
+
+    # Calculate p-value for valid cases
+    if (any(valid)) {
+        pval[valid] <- phyper(x[valid], n[valid], M[valid] - n[valid], N[valid],
+                              lower.tail = FALSE, ...)
+    }
+
+    return(pval)
+}
+
+#' Call peaks/quantify regions using calling cards approach
+#'
+#' @param sample_set_id sample_id that identifies a set of replicates
+#' @param background_counts Vector of background counts per region
+#' @param total_background_counts Total background counts (scalar)
+#' @param annotated_feature_counts List of combined replicate data
+#' @param regions_gr GRanges object with regions
+#' @param pseudocount Pseudocount for calculations (default: 0.1)
+#' @return List with replicates and combined quantifications
+enrichment_analysis <- function(sample_set_id,
+                                background_counts,
+                                total_background_counts,
+                                annotated_feature_counts,
+                                regions_gr,
+                                pseudocount = 0.1) {
+
+    message(sprintf("Working on sample_id for %s", sample_set_id))
+
+    counts_regulator <- annotated_feature_counts[[as.character(sample_set_id)]]
+
+    replicate_quants <- map(names(counts_regulator$replicates), ~{
+        message(sprintf("Working on replicate: %s", .x))
+        gr <- counts_regulator$replicates[[.x]]
+
+        af <- regions_gr
+
+        experiment_counts <- gr$score
+        total_experiment_counts <- counts_regulator$library_total %>%
+            filter(accession == .x) %>%
+            pull(n)
+
+        # Add count columns
+        GenomicRanges::mcols(af)$background_counts <- background_counts
+        GenomicRanges::mcols(af)$experiment_counts <- experiment_counts
+        GenomicRanges::mcols(af)$total_background_counts <- total_background_counts
+        GenomicRanges::mcols(af)$total_experiment_counts <- total_experiment_counts
+
+        # Calculate statistics
+        GenomicRanges::mcols(af)$enrichment <- calculate_enrichment(
+            total_background_counts = total_background_counts,
+            total_experiment_counts = total_experiment_counts,
+            background_counts = background_counts,
+            experiment_counts = experiment_counts,
+            pseudocount = pseudocount
+        )
+
+        GenomicRanges::mcols(af)$poisson_pval <- calculate_poisson_pval(
+            total_background_counts = total_background_counts,
+            total_experiment_counts = total_experiment_counts,
+            background_counts = background_counts,
+            experiment_counts = experiment_counts,
+            pseudocount = pseudocount
+        )
+
+        GenomicRanges::mcols(af)$log_poisson_pval <- calculate_poisson_pval(
+            total_background_counts = total_background_counts,
+            total_experiment_counts = total_experiment_counts,
+            background_counts = background_counts,
+            experiment_counts = experiment_counts,
+            pseudocount = pseudocount,
+            log.p = TRUE
+        )
+
+        GenomicRanges::mcols(af)$hypergeometric_pval <- calculate_hypergeom_pval(
+            total_background_counts = total_background_counts,
+            total_experiment_counts = total_experiment_counts,
+            background_counts = background_counts,
+            experiment_counts = experiment_counts
+        )
+
+        GenomicRanges::mcols(af)$log_hypergeometric_pval <- calculate_hypergeom_pval(
+            total_background_counts = total_background_counts,
+            total_experiment_counts = total_experiment_counts,
+            background_counts = background_counts,
+            experiment_counts = experiment_counts,
+            log.p = TRUE
+        )
+
+        # Calculate adjusted p-values
+        GenomicRanges::mcols(af)$poisson_qval <- p.adjust(
+            GenomicRanges::mcols(af)$poisson_pval, method = "fdr")
+        GenomicRanges::mcols(af)$hypergeometric_qval <- p.adjust(
+            GenomicRanges::mcols(af)$hypergeometric_pval, method = "fdr")
+
+        af
+    })
+
+    names(replicate_quants) <- names(counts_regulator$replicates)
+
+    message(sprintf("Working on the combined for sample_id %s", sample_set_id))
+
+    combined_gr <- regions_gr
+
+    combined_experiment_counts <- counts_regulator$combined$score
+    combined_total_experiment_counts <- sum(counts_regulator$library_total$n)
+
+    # Add count columns
+    GenomicRanges::mcols(combined_gr)$background_counts <- background_counts
+    GenomicRanges::mcols(combined_gr)$experiment_counts <- combined_experiment_counts
+    GenomicRanges::mcols(combined_gr)$total_background_counts <- total_background_counts
+    GenomicRanges::mcols(combined_gr)$total_experiment_counts <- combined_total_experiment_counts
+
+    # Calculate statistics
+    GenomicRanges::mcols(combined_gr)$enrichment <- calculate_enrichment(
+        total_background_counts = total_background_counts,
+        total_experiment_counts = combined_total_experiment_counts,
+        background_counts = background_counts,
+        experiment_counts = combined_experiment_counts,
+        pseudocount = pseudocount
+    )
+
+    message("Calculating Poisson p-values...")
+    GenomicRanges::mcols(combined_gr)$poisson_pval <- calculate_poisson_pval(
+        total_background_counts = total_background_counts,
+        total_experiment_counts = combined_total_experiment_counts,
+        background_counts = background_counts,
+        experiment_counts = combined_experiment_counts,
+        pseudocount = pseudocount
+    )
+
+    GenomicRanges::mcols(combined_gr)$log_poisson_pval <- calculate_poisson_pval(
+        total_background_counts = total_background_counts,
+        total_experiment_counts = combined_total_experiment_counts,
+        background_counts = background_counts,
+        experiment_counts = combined_experiment_counts,
+        pseudocount = pseudocount,
+        log.p = TRUE
+    )
+
+    message("Calculating hypergeometric p-values...")
+    GenomicRanges::mcols(combined_gr)$hypergeometric_pval <- calculate_hypergeom_pval(
+        total_background_counts = total_background_counts,
+        total_experiment_counts = combined_total_experiment_counts,
+        background_counts = background_counts,
+        experiment_counts = combined_experiment_counts
+    )
+
+    GenomicRanges::mcols(combined_gr)$log_hypergeometric_pval <- calculate_hypergeom_pval(
+        total_background_counts = total_background_counts,
+        total_experiment_counts = combined_total_experiment_counts,
+        background_counts = background_counts,
+        experiment_counts = combined_experiment_counts,
+        log.p = TRUE
+    )
+
+    # Calculate adjusted p-values
+    message("Calculating adjusted p-values...")
+    GenomicRanges::mcols(combined_gr)$poisson_qval <- p.adjust(
+        GenomicRanges::mcols(combined_gr)$poisson_pval, method = "fdr")
+    GenomicRanges::mcols(combined_gr)$hypergeometric_qval <- p.adjust(
+        GenomicRanges::mcols(combined_gr)$hypergeometric_pval, method = "fdr")
+
+    message("Analysis complete!")
+
+    list(
+        replicates = replicate_quants,
+        combined = combined_gr
+    )
+}
+
+# ============================================================================
+# Main analysis workflow
+# ============================================================================
+
+# Load data
+genomic_features <- arrow::read_parquet(
+    "~/code/hf/yeast_genome_resources/brentlab_features.parquet")
+
+genomecov <- list(
+    tagged = list(
+        meta = arrow::read_parquet("~/code/hf/rossi_2021/rossi_2021_metadata.parquet"),
+        ds = arrow::open_dataset("~/code/hf/rossi_2021/genome_map")
+    ),
+    control = list(
+        meta = arrow::read_parquet("~/code/hf/rossi_2021/genome_map_control_meta.parquet"),
+        ds = arrow::open_dataset("~/code/hf/rossi_2021/genome_map_control")
+    )
+)
+
+# Get unique regulators
+sample_id_list <- genomecov$tagged$meta %>%
+    pull(sample_id) %>%
+    unique()
+
+# Load regions
+regions_gr <- read_tsv(
+    "~/code/hf/yeast_genome_resources/yiming_promoters.bed",
+    col_names = c('chr', 'start', 'end', 'locus_tag', 'score', 'strand')) %>%
+    bed_to_granges()
+
+# Process control samples
+rossi_2021_control <- combine_control_af(genomecov$control, regions_gr)
+
+# Process all sample_id sets
+annotated_feature_counts <- map(sample_id_list, ~{
+    combine_replicates_af(.x, genomecov$tagged, regions_gr)
+})
+names(annotated_feature_counts) <- sample_id_list
+
+# Perform enrichment analysis
+annotated_feature_quants <- map(sample_id_list, ~{
+    enrichment_analysis(
+        .x,
+        rossi_2021_control$af$score,
+        sum(rossi_2021_control$library_totals$n),
+        annotated_feature_counts,
+        regions_gr
+    )
+})
+names(annotated_feature_quants) <- sample_id_list
+
+# Extract and format replicate-level results
+annotated_features_quants_replicates <- map(annotated_feature_quants, ~{
+    map(.x$replicates, as_tibble) %>%
+        list_rbind(names_to = "run_accession")}) %>%
+    list_rbind(names_to = "sample_id") %>%
+    mutate(sample_id = as.integer(sample_id)) %>%
+    left_join(
+        genomecov$tagged$meta %>%
+            ungroup() %>%
+            select(sample_id, regulator_locus_tag, regulator_symbol, run_accession) %>%
+            distinct(),
+        by = c("sample_id", "run_accession")) %>%
+    left_join(select(genomic_features, locus_tag, symbol)) %>%
+    dplyr::rename(target_locus_tag = locus_tag, target_symbol = symbol) %>%
+    dplyr::relocate(sample_id, run_accession, regulator_locus_tag, regulator_symbol,
+                    target_locus_tag, target_symbol) %>%
+    select(-c(score, width, strand))
+
+# Write replicate-level results
+# annotated_features_quants_replicates %>%
+#     write_parquet(
+#         "~/code/hf/rossi_2021/rossi_2021_af_replicates.parquet",
+#         compression = "zstd",
+#         write_statistics = TRUE,
+#         chunk_size = 6708,
+#         use_dictionary = c(
+#             sample_id = TRUE,
+#             run_accession = TRUE,
+#             regulator_locus_tag = TRUE,
+#             regulator_symbol = TRUE,
+#             seqnames = TRUE,
+#             target_locus_tag = TRUE,
+#             target_symbol = TRUE
+#         )
+#     )
+
+# Extract and format combined results
+annotated_feature_quants_combined <- map(annotated_feature_quants, ~{
+    as_tibble(.x$combined)}) %>%
+    list_rbind(names_to = "sample_id") %>%
+    mutate(sample_id = as.integer(sample_id)) %>%
+    left_join(
+        genomecov$tagged$meta %>%
+            ungroup() %>%
+            select(sample_id, regulator_locus_tag, regulator_symbol) %>%
+            distinct(),
+        by = "sample_id") %>%
+    left_join(select(genomic_features, locus_tag, symbol)) %>%
+    dplyr::rename(target_locus_tag = locus_tag, target_symbol = symbol) %>%
+    dplyr::relocate(sample_id, regulator_locus_tag, regulator_symbol,
+                    target_locus_tag, target_symbol) %>%
+    select(-c(score, width, strand))
+
+# Write combined results
+# annotated_feature_quants_combined %>%
+#     write_parquet(
+#         "~/code/hf/rossi_2021/rossi_2021_af_combined.parquet",
+#         compression = "zstd",
+#         write_statistics = TRUE,
+#         chunk_size = 6708,
+#         use_dictionary = c(
+#             sample_id = TRUE,
+#             regulator_locus_tag = TRUE,
+#             regulator_symbol = TRUE,
+#             seqnames = TRUE,
+#             target_locus_tag = TRUE,
+#             target_symbol = TRUE
+#         )
+#     )

scripts/parse_pugh_genomecov_5p.R

@@ -71,19 +71,22 @@ pugh_genomecov_meta = pugh_genomecov_tmp %>%
   filter(!is.na(locus_tag)) %>%
   bind_rows(
     filled_missing_locus_tag) %>%
-  mutate(regulator_symbol = symbol, regulator_locus_tag = locus_tag)
-
-pugh_genomecov_meta %>%
-  select(regulator_locus_tag, regulator_symbol,
-         run_accession, yeastepigenome_id) %>%
-write_parquet("~/code/hf/rossi_2021/rossi_2021_metadata.parquet",
-              compression = "zstd",
-              write_statistics = TRUE,
-              use_dictionary = c(
-                regulator_locus_tag = TRUE,
-                regulator_symbol = TRUE
-              )
-)
+  mutate(regulator_symbol = symbol, regulator_locus_tag = locus_tag) %>%
+    arrange(regulator_locus_tag) %>%
+    group_by(regulator_locus_tag) %>%
+    mutate(sample_id = cur_group_id())
+
+# pugh_genomecov_meta %>%
+#   select(regulator_locus_tag, regulator_symbol,
+#          run_accession, yeastepigenome_id) %>%
+# write_parquet("~/code/hf/rossi_2021/rossi_2021_metadata.parquet",
+#               compression = "zstd",
+#               write_statistics = TRUE,
+#               use_dictionary = c(
+#                 regulator_locus_tag = TRUE,
+#                 regulator_symbol = TRUE
+#               )
+# )
 
 process_chipexo_genomecov_file = function(covpath, accession_str){
 

scripts/parse_yeastepigenome_sample_data.R

@@ -0,0 +1,127 @@
+library(httr)
+library(jsonlite)
+library(tidyverse)
+
+## TODO: this uses the rossi metadata that already existed. That will eventually
+## be removed. This needs to be created from the yeastepigenome pull below,
+## and the data from the getGEO directly
+
+# Fetch all samples from the API
+response <- GET("https://odin.cac.cornell.edu/yep_api/reviewSamples")
+
+# Parse the JSON response
+samples_data <- content(response, "text", encoding = "UTF-8") %>%
+    fromJSON(flatten = TRUE)
+
+# Convert to tibble
+# The data comes as a named list with numeric indices as names
+yeastepigenome_sample_df <- samples_data %>%
+    map_df(~as_tibble(.), .id = "index") %>%
+    select(sampleId, assayType, treatments, growthMedia, antibody) %>%
+    dplyr::rename(yeastepigenome_id = sampleId,
+                  assay_type = assayType,
+                  treatment = treatments,
+                  growth_media = growthMedia)
+
+rossi_meta = arrow::read_parquet("~/code/hf/rossi_2021/deprecated_rossi_2021_metadata.parquet")
+
+rossi_meta_with_addtl = rossi_meta %>%
+    left_join(yeastepigenome_sample_df) %>%
+    filter(!run_accession %in% c('SRR11466887', 'SRR11466891')) %>%
+    bind_rows(
+        tibble(
+            regulator_locus_tag = c("YNL076W", "YGL244W"),
+            regulator_symbol = c("MKS1", "RTF1"),
+            run_accession = c("SRR11466887", "SRR11466891"),
+            yeastepigenome_id = c(14846, 12031),
+            assay_type = "ChIP-exo",
+            treatment = "Normal",
+            growth_media = "YPD",
+            antibody = c("HA-tag: Santa Cruz sc-7392", "TAP-tag: Sigma i5006"))) %>%
+    arrange(regulator_locus_tag) %>%
+    select(-assay_type) %>%
+    group_by(regulator_locus_tag, treatment, growth_media) %>%
+    mutate(sample_id = cur_group_id()) %>%
+    ungroup()
+
+# arrow::write_parquet(
+#     rossi_meta_with_addtl,
+#     "~/code/hf/rossi_2021/rossi_2021_metadata.parquet",
+#     compression = "zstd",
+#     write_statistics = TRUE,
+#     use_dictionary = c(
+#         sample_id = TRUE,
+#         regulator_locus_tag=TRUE,
+#         regulator_symbol = TRUE,
+#         treatment = TRUE,
+#         growth_media = TRUE))
+
+
+# NOTE: the following works, but is currently unused
+#
+# library(GEOquery)
+# library(tidyverse)
+#
+# # Get the GEO series data
+# gse <- getGEO("GSE147927", GSEMatrix = FALSE)
+# sample_list <- GSMList(gse)
+#
+# # Helper function for NULL coalescing
+# `%||%` <- function(x, y) if (is.null(x)) y else x
+#
+# # Extract sample metadata
+# extract_sample_metadata_robust <- function(gsm) {
+#     meta <- Meta(gsm)
+#
+#     # Start with basic info
+#     result <- tibble(
+#         gsm_id = meta$geo_accession %||% NA,
+#         title = meta$title %||% NA,
+#         source_name = meta$source_name_ch1 %||% NA,
+#         organism = meta$organism_ch1 %||% NA
+#     )
+#
+#     # Extract characteristics from the 'characteristics_ch1' field
+#     if (!is.null(meta$characteristics_ch1)) {
+#         for (char in meta$characteristics_ch1) {
+#             # Split on first colon
+#             parts <- str_split(char, ":\\s*", n = 2)[[1]]
+#             if (length(parts) == 2) {
+#                 char_name <- parts[1]
+#                 char_value <- parts[2]
+#                 result[[char_name]] <- char_value
+#             }
+#         }
+#     }
+#
+#     # Add protocols
+#     result$treatment_protocol <- paste(meta$treatment_protocol_ch1, collapse = " ") %||% NA
+#     result$growth_protocol <- paste(meta$growth_protocol_ch1, collapse = " ") %||% NA
+#     result$extract_protocol <- paste(meta$extract_protocol_ch1, collapse = " ") %||% NA
+#
+#     # Add library info
+#     result$library_strategy <- meta$library_strategy %||% NA
+#     result$library_source <- meta$library_source %||% NA
+#     result$library_selection <- meta$library_selection %||% NA
+#     result$instrument_model <- meta$instrument_model %||% NA
+#
+#     # Add data processing
+#     result$data_processing <- paste(meta$data_processing, collapse = " | ") %||% NA
+#
+#     # Extract SRA accession
+#     relations <- meta$relation
+#     sra_relation <- relations[grepl("SRA", relations)]
+#     if (length(sra_relation) > 0) {
+#         result$sra_accession <- str_extract(sra_relation, "SR[XR]\\d+")
+#     } else {
+#         result$sra_accession <- NA
+#     }
+#
+#     return(result)
+# }
+#
+# # Apply to all samples
+# all_samples_metadata <- map_df(sample_list, extract_sample_metadata_robust)
+#
+# # View results
+# glimpse(all_samples_metadata)

yeastepigenome_annotatedfeatures.parquet.md5

@@ -1 +0,0 @@
-b905c289a894fe8f92804fc87a79e5d2  yeastepigenome_annotatedfeatures.parquet