scripts/mahendrawada_annotated_features.R

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)
}


#' 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)
}

#' @param sampleid regulator_locus_tag used to get a set of replicates
combine_replicates_af = function(sampleid){

    message(sprintf("working on sample_id: %s", sampleid))

    sra_accession_list = chec_genomemap_meta %>%
        filter(sample_id == sampleid) %>%
        pull(sra_accession)

    library_totals = mahendrawada_genome_map_ds %>%
        filter(sra_accession %in% sra_accession_list) %>%
        group_by(sra_accession) %>%
        tally() %>%
        collect()

    replicate_region_counts = map(sra_accession_list, ~{
        sra = .
        insertions_gr = mahendrawada_genome_map_ds %>%
            filter(sra_accession == sra) %>%
            collect() %>%
            dplyr::select(-sra_accession) %>%
            bed_to_granges()

        sum_overlap_scores(insertions_gr, regions_gr)
    })

    replicates = map(replicate_region_counts, ~{
        replicate_regions = regions_gr
        replicate_regions$score = .
        replicate_regions
    })
    names(replicates) = sra_accession_list

    combined = regions_gr
    combined$score = Reduce(`+`, replicate_region_counts)

    list(
        library_total = library_totals,
        replicates = replicates,
        combined = combined
    )

}

combine_control_af = function(){

    library_totals = mahendrawada_control_ds %>%
        group_by(sra_accession) %>%
        tally() %>%
        collect()

    replicate_region_counts = map(freemnase_meta$sra_accession, ~{
        sra = .
        insertions_gr = mahendrawada_control_ds %>%
            filter(sra_accession == sra) %>%
            collect() %>%
            dplyr::select(-sra_accession) %>%
            bed_to_granges()

        sum_overlap_scores(insertions_gr, regions_gr)
    })

    out = regions_gr

    # combine replicate counts
    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)
    # This is equivalent to: 1 - CDF(x) + PMF(x)
    # Using the upper tail directly with lower.tail = FALSE
    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 number of balls (total counts)
    M <- total_background_counts + total_experiment_counts
    # n: number of white balls (experiment counts)
    n <- total_experiment_counts
    # N: number of draws (counts in region)
    N <- background_counts + experiment_counts
    # x: number of white balls drawn (experiment counts in region) - 1 for upper tail
    x <- experiment_counts - 1

    # Handle edge cases
    valid <- (M >= 1) & (N >= 1)
    pval <- rep(1, length(M))

    # Calculate p-value for valid cases: P(X >= x) = 1 - P(X <= x-1)
    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 sampleid regulator_locus_tag used to extract from annotated_features_counts
#' @param pseudocount Pseudocount for calculations (default: 0.1)
#' @return GRanges object with regions and statistics as metadata columns
enrichment_analysis <- function(sampleid,
                                background_counts,
                                total_background_counts,
                                pseudocount = 0.1) {

    message(sprintf("Working on replicates for %s", sampleid))

    counts_sampleid = annotated_feature_counts[[sampleid]]

    replicate_quants = map(names(counts_sampleid$replicates), ~{
        message(sprintf("Working on replicate: %s", .x))
        gr = counts_sampleid$replicates[[.x]]

        af = regions_gr

        experiment_counts = gr$score
        total_experiment_counts = counts_sampleid$library_total %>%
            filter(sra_accession == .x) %>%
            pull(n)

        # 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_sampleid$replicates)

    message(sprintf("Working on the combined for sample_id %s", sampleid))

    combined_gr = regions_gr

    combined_experiment_counts =  counts_sampleid$combined$score
    combined_total_experiment_counts = sum(counts_sampleid$library_total$n)

    # 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
    )
}

genomic_features = arrow::read_parquet("~/code/hf/yeast_genome_resources/brentlab_features.parquet")

chec_genomemap_meta = arrow::read_parquet(
    "~/code/hf/mahendrawada_2025/chec_genome_map_meta.parquet")

freemnase_meta = arrow::read_parquet(
    "~/code/hf/mahendrawada_2025/chec_genome_map_control_meta.parquet")

mahendrawada_genome_map_ds = arrow::open_dataset(
    "~/code/hf/mahendrawada_2025/chec_genome_map")

mahendrawada_control_ds = arrow::open_dataset(
    "~/code/hf/mahendrawada_2025/chec_genome_map_control")

samplid_list = chec_genomemap_meta %>%
    pull(sample_id) %>%
    unique()

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()

m2025_control = combine_control_af()

annotated_feature_counts = map(samplid_list, combine_replicates_af)
names(annotated_feature_counts) = samplid_list

annotated_feature_quants = map(
    samplid_list, ~{
        enrichment_analysis(
            .x,
            m2025_control$af$score,
            sum(m2025_control$library_totals$n)
        )
    }
)

names(annotated_feature_quants) = samplid_list

sra_accession_for_quants = map(annotated_feature_quants, ~names(.x$replicates))

annotated_features_quants_replicates =
    map(annotated_feature_quants, ~{
        map(.x$replicates, as_tibble) %>%
                list_rbind(names_to = "sra_accession")}) %>%
    list_rbind(names_to = "sample_id") %>%
    mutate(sample_id = as.integer(sample_id)) %>%
    arrange(sample_id) %>%
    select(-sample_id) %>%
    left_join(select(genomic_features, locus_tag, symbol)) %>%
    dplyr::rename(target_locus_tag = locus_tag, target_symbol = symbol) %>%
    dplyr::relocate(sra_accession, target_locus_tag, target_symbol) %>%
    select(-score)


annotated_features_quants_replicates %>%
  write_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_replicates.parquet",
                compression = "zstd",
                write_statistics = TRUE,
                chunk_size = 6708,
                use_dictionary = c(
                  sra_accession = TRUE,
                  seqnames = TRUE,
                  target_locus_tag = TRUE,
                  target_symbol = TRUE
                )
  )

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)) %>%
    arrange(sample_id) %>%
    left_join(select(genomic_features, locus_tag, symbol)) %>%
    dplyr::rename(target_locus_tag = locus_tag, target_symbol = symbol) %>%
    dplyr::relocate(sample_id, target_locus_tag, target_symbol) %>%
    select(-score)


annotated_feature_quants_combined %>%
  write_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined.parquet",
                compression = "zstd",
                write_statistics = TRUE,
                chunk_size = 6708,
                use_dictionary = c(
                  sample_id = TRUE,
                  seqnames = TRUE,
                  target_locus_tag = TRUE,
                  target_symbol = TRUE
                )
  )