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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)
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