## NOTE: The data is currently on /lts/mblab/downloaded_data/barkai_checseq
## and the parquet dataset is on the brentlab-strides aws at s3://yeast-binding-perturbation-data/barkai_checkseq

library(tidyverse)
library(here)
library(arrow)
library(GEOquery)

# 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_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()
#   }
# }

# 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.

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_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
                )
  )

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
                )
  )

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, -instrument_model, -extract_protocol_ch1)

gse222268_meta %>%
  write_parquet(here("/home/chase/code/hf/barkai_compendium/GSE222268_metadata.parquet"),
                compression = "zstd",
                write_statistics = TRUE,
                use_dictionary = c(
                  accession = TRUE
                )
  )