Update scripts/parse_barkai_checseq.R

scripts/parse_barkai_checseq.R

@@ -4,154 +4,223 @@
 library(tidyverse)
 library(here)
 library(arrow)
+library(GEOquery)
 
-sacCer3_genome = rtracklayer::import("~/ref/sacCer3/ucsc/sacCer3.fa.gz", format="fasta")
+# genomic feature harmonization table ----
+# see https://huggingface.co/datasets/BrentLab/yeast_genome_resources
+genomicfeatures = arrow::open_dataset(here("data/genome_files/hf/features")) %>%
+  as_tibble()
 
-sacCer3_seqnames = unlist(map(str_split(names(sacCer3_genome), " "), ~.[[1]]))
+# sacCer3_genome = rtracklayer::import("~/ref/sacCer3/ucsc/sacCer3.fa.gz", format="fasta")
+#
+# sacCer3_seqnames = unlist(map(str_split(names(sacCer3_genome), " "), ~.[[1]]))
+#
+# sacCer3_genome_df = tibble(
+#   seqnames = rep(sacCer3_seqnames, Biostrings::width(sacCer3_genome))
+# ) %>%
+#   group_by(seqnames) %>%
+#   mutate(start = row_number()-1,
+#          end = row_number()) %>%
+#   ungroup()
+#
+# retrieve_series_paths = function(series_id){
+#   sra_meta_path = file.path("data/barkai_checseq", series_id, "SraRunTable.csv")
+#   stopifnot(file.exists(sra_meta_path))
+#   df = read_csv(sra_meta_path)
+#
+#   data_files = list.files(here("data/barkai_checseq", series_id), "*.txt.gz", full.names = TRUE)
+#
+#   stopifnot(nrow(df) == length(data_files))
+#
+#   names(data_files) = str_extract(basename(data_files), "GSM\\d+")
+#
+#   list(
+#     meta = sra_meta_path,
+#     files = data_files
+#   )
+# }
+#
+#
+# add_genomic_coordinate = function(checseqpath){
+#
+#   bind_cols(sacCer3_genome_df,
+#             data.table::fread(checseqpath, sep = "\t", col.names='pileup'))
+#
+# }
+#
+# process_checseq_files = function(file){
+#
+#   add_genomic_coordinate(file) %>%
+#     filter(pileup != 0)
+# }
+#
+# series_list = map(set_names(c("GSE179430", "GSE209631", "GSE222268")), retrieve_series_paths)
+#
+# dataset_basepath = here("data/barkai_checseq/hf/genome_map")
+#
+# # Create output directory
+# dir.create(dataset_basepath, recursive = TRUE, showWarnings = FALSE)
+#
+# for (series_id in names(series_list)) {
+#
+#   message(glue::glue("Processing series {series_id}"))
+#
+#   for (accession_id in names(series_list[[series_id]]$files)) {
+#
+#     message(glue::glue("  Processing {accession_id}"))
+#
+#     df <- process_checseq_files(
+#       series_list[[series_id]]$files[[accession_id]]
+#     ) %>%
+#       mutate(accession = accession_id, series = series_id)
+#
+#     df %>%
+#       group_by(seqnames) %>%
+#     write_dataset(
+#       path = dataset_basepath,
+#       format = "parquet",
+#       partitioning = c("series", "accession"),
+#       existing_data_behavior = "overwrite",
+#       compression = "zstd",
+#       write_statistics = TRUE,
+#       use_dictionary = c(
+#         seqnames = TRUE
+#       )
+#     )
+#
+#     gc()
+#   }
+# }
 
-sacCer3_genome_df = tibble(
-  seqnames = rep(sacCer3_seqnames, Biostrings::width(sacCer3_genome))
-) %>%
-  group_by(seqnames) %>%
-  mutate(start = row_number()-1,
-         end = row_number()) %>%
-  ungroup()
+# the following code was used to parse an entire series to DF and then save
+# to a parquet dataset. that was too large and I chose the dataset partitioning
+# instead.
 
-retrieve_series_paths = function(series_id){
-  sra_meta_path = file.path("data/barkai_checseq", series_id, "SraRunTable.csv")
-  stopifnot(file.exists(sra_meta_path))
-  df = read_csv(sra_meta_path)
+split_manipulation <- function(manipulation_str) {
+  parts <- str_split(manipulation_str, "::")[[1]]
+
+  if (length(parts) != 2) {
+    stop("Unexpected format. Expected 'LOCUS::TAGGED_CONSTRUCT'")
+  }
 
-  data_files = list.files(here("data/barkai_checseq", series_id), "*.txt.gz", full.names = TRUE)
+  tagged_locus <- parts[1]
+  rhs <- parts[2]
 
-  stopifnot(nrow(df) == length(data_files))
+  # default
+  dbd_donor_symbol_str <- "none"
+  ortholog <- "none"
 
-  names(data_files) = str_extract(basename(data_files), "GSM\\d+")
+  # Check for paralog DBD
+  if (str_detect(rhs, "-[A-Za-z0-9]+DBD-Mnase$")) {
+    dbd_donor_symbol_str <- toupper(str_remove(str_split(rhs, "-", simplify = TRUE)[[2]], "DBD"))
+  } else if (str_detect(rhs, "^K\\.lactis .*?-Mnase$")) {
+    ortholog <- rhs
+  }
 
   list(
-    meta = sra_meta_path,
-    files = data_files
+    mnase_tagged_symbol = tagged_locus,
+    dbd_donor_symbol = dbd_donor_symbol_str,
+    ortholog_donor = ortholog
   )
 }
 
 
-add_genomic_coordinate = function(checseqpath){
-
-  bind_cols(sacCer3_genome_df,
-            data.table::fread(checseqpath, sep = "\t", col.names='pileup'))
-
-}
-
-process_checseq_files = function(file){
+split_deletion <- function(deletion_str) {
+  parts <- str_split(deletion_str, "::", simplify = TRUE)
 
-  add_genomic_coordinate(file) %>%
-    filter(pileup != 0)
+  list(
+    paralog_deletion_symbol = parts[1],
+    paralog_resistance_cassette = if (ncol(parts) >= 2) parts[2] else "none"
+  )
 }
 
-series_list = map(set_names(c("GSE179430", "GSE209631", "GSE222268")), retrieve_series_paths)
+split_construct_to_tibble = function(split_list){
+  background = list(background=split_list[[1]])
+  manipulation_list = split_manipulation(split_list[[2]])
+  deletion_list = split_deletion(tryCatch(split_list[[3]], error = function(e) "none"))
 
-dataset_basepath = here("data/barkai_checseq/hf/genome_map")
+  bind_cols(map(list(background, manipulation_list, deletion_list), as_tibble))
 
-# Create output directory
-dir.create(dataset_basepath, recursive = TRUE, showWarnings = FALSE)
+}
 
-for (series_id in names(series_list)) {
 
-  message(glue::glue("Processing series {series_id}"))
+split_constructs <- function(s) {
+  s <- str_trim(s)
+  if (s == "" || is.na(s)) return(character(0))
+  # split on spaces ONLY when the next token starts a new locus "XYZ::"
+  split_geno = str_split(s, "\\s+(?=[A-Za-z0-9_.()\\-]+::)")[[1]]
 
-  for (accession_id in names(series_list[[series_id]]$files)) {
+  bind_cols(tibble(genotype = s), split_construct_to_tibble(split_geno))
 
-    message(glue::glue("  Processing {accession_id}"))
 
-    df <- process_checseq_files(
-      series_list[[series_id]]$files[[accession_id]]
-    ) %>%
-      mutate(accession = accession_id, series = series_id)
+}
 
-    df %>%
-      group_by(seqnames) %>%
-    write_dataset(
-      path = dataset_basepath,
-      format = "parquet",
-      partitioning = c("series", "accession"),
-      existing_data_behavior = "overwrite",
-      compression = "zstd",
-      write_statistics = TRUE,
-      use_dictionary = c(
-        seqnames = TRUE
-      )
-    )
+gse178430_meta = read_csv("data/barkai_checseq/GSE179430/SraRunTable.csv") %>%
+    mutate(genotype = str_replace(genotype, "Yap2", "Cad1")) %>%
+    mutate(genotype = str_replace(genotype, "Yap4", "Cin5"))
+
+gse178430_parsed_meta = bind_cols(
+  select(gse178430_meta, `Sample Name`, strainid, Instrument) %>%
+    dplyr::rename(accession = `Sample Name`,
+                  instrument = Instrument),
+  bind_rows(map(gse178430_meta$genotype, split_constructs))) %>%
+  left_join(select(genomicfeatures, locus_tag, symbol) %>%
+              dplyr::rename(mnase_tagged_symbol = symbol)) %>%
+  dplyr::rename(regulator_locus_tag = locus_tag,
+                regulator_symbol = mnase_tagged_symbol) %>%
+  select(accession, regulator_locus_tag, regulator_symbol, strainid,
+         instrument, genotype, dbd_donor_symbol, ortholog_donor,
+         paralog_deletion_symbol, paralog_resistance_cassette)
+
+gse178430_parsed_meta %>%
+  write_parquet(here("/home/chase/code/hf/barkai_compendium/GSE178430_metadata.parquet"),
+                compression = "zstd",
+                write_statistics = TRUE,
+                use_dictionary = c(
+                  accession = TRUE,
+                  regulator_locus_tag = TRUE,
+                  regulator_symbol = TRUE
+                )
+  )
 
-    gc()
-  }
-}
+gse209631_meta = read_csv("data/barkai_checseq/GSE209631/SraRunTable.csv")
+
+gse209631_parsed_meta = gse209631_meta %>%
+  select(`Sample Name`, tagged_tf, Instrument, `variant-type`) %>%
+  janitor::clean_names() %>%
+  dplyr::rename(accession = sample_name) %>%
+  arrange(tagged_tf, variant_type) %>%
+  left_join(select(genomicfeatures, locus_tag, symbol) %>% dplyr::rename(tagged_tf = symbol)) %>%
+  dplyr::rename(regulator_symbol = tagged_tf, regulator_locus_tag = locus_tag) %>%
+  select(accession, regulator_locus_tag, regulator_symbol, variant_type)
+
+gse209631_parsed_meta %>%
+  write_parquet(here("/home/chase/code/hf/barkai_compendium/GSE209631_metadata.parquet"),
+                compression = "zstd",
+                write_statistics = TRUE,
+                use_dictionary = c(
+                  accession = TRUE,
+                  regulator_locus_tag = TRUE,
+                  regulator_symbol = TRUE,
+                  variant_type = TRUE
+                )
+  )
 
-# the following code was used to parse an entire series to DF and then save
-# to a parquet dataset. that was too large and I chose the dataset partitioning
-# instead.
+gse=GEOquery::getGEO(filename=here("data/barkai_checseq/GSE222268_series_matrix.txt"))
+
+gse222268_meta = Biobase::pData(gse@phenoData) %>% as_tibble() %>%
+  select(title, geo_accession, extract_protocol_ch1, description,
+         instrument_model, library_selection) %>%
+  mutate(description = ifelse(description == "", library_selection, description)) %>%
+  dplyr::rename(accession = geo_accession) %>%
+  select(-library_selection)
+
+gse222268_meta %>%
+  write_parquet(here("/home/chase/code/hf/barkai_compendium/GSE222268_metadata.parquet"),
+                compression = "zstd",
+                write_statistics = TRUE,
+                use_dictionary = c(
+                  accession = TRUE
+                )
+  )
 
-# split_manipulation <- function(manipulation_str) {
-#   parts <- str_split(manipulation_str, "::")[[1]]
-#
-#   if (length(parts) != 2) {
-#     stop("Unexpected format. Expected 'LOCUS::TAGGED_CONSTRUCT'")
-#   }
-#
-#   tagged_locus <- parts[1]
-#   rhs <- parts[2]
-#
-#   # default
-#   dbd_donor_symbol_str <- "none"
-#   ortholog <- "none"
-#
-#   # Check for paralog DBD
-#   if (str_detect(rhs, "-[A-Za-z0-9]+DBD-Mnase$")) {
-#     dbd_donor_symbol_str <- toupper(str_remove(str_split(rhs, "-", simplify = TRUE)[[2]], "DBD"))
-#   } else if (str_detect(rhs, "^K\\.lactis .*?-Mnase$")) {
-#     ortholog <- rhs
-#   }
-#
-#   list(
-#     mnase_tagged_symbol = tagged_locus,
-#     dbd_donor_symbol = dbd_donor_symbol_str,
-#     ortholog_donor = ortholog
-#   )
-# }
-#
-#
-# split_deletion <- function(deletion_str) {
-#   parts <- str_split(deletion_str, "::", simplify = TRUE)
-#
-#   list(
-#     paralog_deletion_symbol = parts[1],
-#     paralog_resistance_cassette = if (ncol(parts) >= 2) parts[2] else "none"
-#   )
-# }
-#
-# split_construct_to_tibble = function(split_list){
-#   background = list(background=split_list[[1]])
-#   manipulation_list = split_manipulation(split_list[[2]])
-#   deletion_list = split_deletion(tryCatch(split_list[[3]], error = function(e) "none"))
-#
-#   bind_cols(map(list(background, manipulation_list, deletion_list), as_tibble))
-#
-# }
-#
-#
-# split_constructs <- function(s) {
-#   s <- str_trim(s)
-#   if (s == "" || is.na(s)) return(character(0))
-#   # split on spaces ONLY when the next token starts a new locus "XYZ::"
-#   split_geno = str_split(s, "\\s+(?=[A-Za-z0-9_.()\\-]+::)")[[1]]
-#
-#   bind_cols(tibble(genotype = s), split_construct_to_tibble(split_geno))
-#
-#
-# }
-#
-# gse178430_parsed_meta = bind_cols(
-#   select(gse178430_meta, `GEO_Accession (exp)`, strainid, Instrument) %>%
-#     dplyr::rename(accession = `GEO_Accession (exp)`,
-#                   instrument = Instrument),
-#   bind_rows(map(gse178430_meta$genotype, split_constructs))
-# )