adding macs2 peaks for standard conditions

README.md

@@ -57,6 +57,7 @@ features:
   - chec_genome_map_meta
   - chec_mahendrawada_m2025_af_combined_meta
   - mahendrawada_chec_seq
+  - macs2_standard_peaks
   - rna_seq
   - rnaseq_reprocessed
   - degron_counts_meta
@@ -77,6 +78,7 @@ features:
   - chec_mahendrawada_m2025_af_replicates_mindel
   - chec_mahendrawada_m2025_af_combined_mindel
   - mahendrawada_chec_seq
+  - macs2_standard_peaks
   - rna_seq
   - rnaseq_reprocessed
   - degron_counts
@@ -811,6 +813,40 @@ configs:
       description: SRA (Sequence Read Archive) accession identifier for this biological replicate
       role: sample_id
 
+- config_name: macs2_standard_peaks
+  description: >-
+    MACS2 peak calls for the Mahendrawada 2025 ChEC-seq data. This data is processed through a workflow which is described in scripts/mahendrawada_peak_analysis.R
+    and standardized with the Rossi data. It follows the same convention that a promoter must have a peak in at least 2 replicates to be counted as peak for that 
+    promoter. The max, median and nearest score across peaks in a given promoter are provided when there is more than one peak either within a single replicate, or 
+    across replicates where the same promoter is included in at least 2 replicates. Annotations of peaks to targets is performed by homer. Only peaks within
+    700bp of the start codon and with `q-value < 0.1` are retained.
+  dataset_type: annotated_features
+  data_files:
+  - split: train
+    path: macs2_standard_peaks.parquet
+  dataset_info:
+    features:
+    - name: sample_id
+      dtype: int64
+      description: Unique identifier for a sample.
+      role: sample_id
+    - name: n_peaks
+      dtype: int64
+      description: Number of peaks called for this promoter across replicates
+      role: quantitative_measure
+    - name: nearest_score
+      dtype: float64
+      description: Score of the peak nearest to the promoter center. -log10(q-value) from MACS2 peak calling.
+      role: quantitative_measure
+    - name: median_score
+      dtype: float64
+      description: Median score of all peaks called for this promoter across replicates. -log10(q-value) from MACS2 peak calling.
+      role: quantitative_measure
+    - name: max_score
+      dtype: float64
+      description: Maximum score of all peaks called for this promoter across replicates. -log10(q-value) from MACS2 peak calling.
+      role: quantitative_measure
+
 - config_name: chec_mahendrawada_m2025_af_replicates_mindel
   description: >-
     Promoter significance scores using the Mindel promoters, calculated using the mahendrawada_annotated_features.R. This is a reprocessing of the original authors'
@@ -881,7 +917,7 @@ configs:
 - config_name: chec_mahendrawada_m2025_af_combined_meta
   description: Sample-level metadata for combined ChEC-seq experiments with regulator information and experimental conditions
   dataset_type: metadata
-  applies_to: ["chec_mahendrawada_m2025_af_combined","chec_mahendrawada_m2025_af_combined_mindel", "chec_mahendrawada_m2025_af_combined_start_codon_500bp", "chec_mahendrawada_m2025_af_combined_intergenic"]
+  applies_to: ["chec_mahendrawada_m2025_af_combined","chec_mahendrawada_m2025_af_combined_mindel", "chec_mahendrawada_m2025_af_combined_start_codon_500bp", "chec_mahendrawada_m2025_af_combined_intergenic", "macs2_standard_peaks"]
   data_files:
   - split: train
     path: chec_mahendrawada_m2025_af_combined_meta.parquet

macs2_standard_peaks.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1819800bb53adb25f25c8a8d0079802cc68077df878c648179c1bc937d46f50d
+size 333212

scripts/mahendrawada_peak_analysis.R

@@ -0,0 +1,224 @@
+library(tidyverse)
+library(janitor)
+library(GenomicRanges)
+library(here)
+
+read_in_annotated_peaks <- function(peak_path) {
+    df <- read_tsv(peak_path, show_col_types = FALSE) |>
+        janitor::clean_names()
+
+    # Skip empty annotations
+    if (nrow(df) == 0) {
+        warning(sprintf("Skipping empty annotation file: %s", basename(peak_path)))
+        return(NULL)
+    }
+
+    df
+}
+
+score_targets <- function(regulator, min_dist = 0, max_dist = 700) {
+    bind_rows(annotated_peaks$df[which(str_detect(names(annotated_peaks$df), regulator))],
+        .id = "tmp"
+    ) |>
+        separate_wider_delim(tmp,
+            delim = "_", names = c(
+                "regulator_symbol",
+                "replicate"
+            ),
+            too_few = "align_start"
+        ) |>
+        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_distinct(replicate),
+            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/mahendrawada_macs"),
+        "_annotated_peaks.txt",
+        full.names = TRUE,
+        recursive = TRUE
+    )
+)
+names(annotated_peaks$files) <- str_remove(
+    basename(annotated_peaks$files),
+    "_annotated_peaks.txt"
+)
+
+annotated_peaks$df <- compact(map(annotated_peaks$files, read_in_annotated_peaks))
+
+
+annotated_peaks$target_score <- map(unique(str_remove(names(annotated_peaks$df), "_(A|B|C)")), score_targets)
+names(annotated_peaks$target_score) <- unique(str_remove(names(annotated_peaks$df), "_(A|B|C)"))
+
+meta <- arrow::read_parquet("~/projects/huggingface/mahendrawada_2025/chec_genome_map_meta.parquet")
+
+brentlab_features <- read_csv("~/projects/huggingface/yeast_genome_resources/brentlab_features.csv.gz")
+
+peaks_df_to_hf <- bind_rows(annotated_peaks$target_score, .id = "regulator_symbol") |>
+    left_join(
+        filter(meta, condition == "standard") |>
+            dplyr::select(sample_id, regulator_locus_tag, regulator_symbol) |>
+            distinct()
+    ) |>
+    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/mahendrawada_2025/macs2_standard_peaks.parquet")
+
+# NOTE: do not use the sample_id
+authors_orig_peaks <- arrow::read_parquet("~/projects/huggingface/mahendrawada_2025/chec_mahendrawada_2025.parquet")
+
+peaks_df_to_hf_with_athors_orig <- peaks_df_to_hf |>
+    # NOTE: the authors orig peaks sample_id column is deprecated. see datacard
+    dplyr::select(-sample_id)
+left_join(
+    authors_orig_peaks |>
+        dplyr::rename(authors_score = peak_score)
+)
+
+# 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)