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)