# All standard condition up to run_7390 library(tidyverse) library(here) library(arrow) # TODO: when making processing scripts github, add the background file # and make sure the repo gets registered to zenodo. Put that link to the # background files in the SRA submission # cells grown on solid media at room temperature # put the definition of the "Description" values in the qc_django_data = read_csv("~/projects/parsing_yeast_database_data/data/qc_from_db/rr_20251222.csv") expr_django_data = read_csv("~/projects/parsing_yeast_database_data/data/qc_from_db/expr_20251222.csv") mcisaac_preferred_reps = expr_django_data %>% filter(source_name == "mcisaac_oe") %>% filter(preferred_replicate==TRUE) %>% pull(id) qc_django_data_in_modeling = list( kemmeren = qc_django_data %>% filter(binding_source == "brent_nf_cc", !is.na(single_binding)) %>% filter(expression_source == "kemmeren_tfko"), mcisaac = qc_django_data %>% filter(binding_source == "brent_nf_cc", !is.na(single_binding)) %>% filter(expression_source == "mcisaac_oe" & expression %in% mcisaac_preferred_reps)) analysis_set_meta = read_csv("~/htcf_ref/data/yeast_database_modelling/pull_data_20250805/data/brent_nf_cc_meta_20250805.csv") composite_binding = read_csv("~/htcf_ref/data/yeast_database_modelling/pull_data_20250805/data/bindingconcat_meta_20250805.csv") %>% filter(source_name == "brent_nf_cc") barcode_details_root = "~/htcf_lts/sequence_data/yeast_cc/sequence" barcode_details_list = list.files(barcode_details_root, "*_barcode_details.json", recursive=TRUE) passing_bam_paths = read_csv("~/htcf_local/cc/yeast/passing_bams_lookup.txt", col_names = 'bampath') passing_fastq_from_bam_paths = list.files("~/htcf_local/cc/yeast/passing_fastq_from_bam") fastq_df = tibble(filename = passing_fastq_from_bam_paths) %>% extract( filename, into = c("batch", "regulator_symbol_replicate"), regex = "^(.+?)_([^_]+)_passing_tagged\\.fastq\\.gz$", remove = FALSE) %>% mutate(replicate = str_remove(str_extract(regulator_symbol_replicate, "x\\d"), "x")) %>% mutate(regulator_symbol = str_remove(regulator_symbol_replicate, "x\\d")) %>% replace_na(list(replicate = '1')) %>% mutate(replicate = as.integer(replicate)) %>% mutate(batch = ifelse(batch == "run_5690_correct", "run_5690", batch)) %>% filter(!batch %in% c('dsir4')) %>% filter(regulator_symbol != "undetermined") %>% # this is empty -- no passing hops filter(filename != "run_6739_MOT3_passing_tagged.fastq.gz") barcode_details_df = map(barcode_details_list, ~{ message(sprintf("working on %s", basename(.x))) x = jsonlite::read_json(file.path(barcode_details_root, .)) tibble( seq = names(x$components$tf$map), regulator_symbol = unlist(x$components$tf$map)) %>% mutate(r1_index = substr(seq,1,5), r2_index = substr(seq,6,nchar(seq))) %>% mutate(batch = basename(dirname(.x)))}) %>% bind_rows() composite_binding_unlisted = unlist(lapply(composite_binding$bindings, function(x) { as.numeric(str_extract_all(x, "\\d+")[[1]])})) analysis_binding_ids = c( pull(filter(analysis_set_meta, !is.na(single_binding)), single_binding), composite_binding_unlisted ) binding_django = read_csv(here("data/binding_from_django_db_20260128.csv")) barcode_details_df_with_id = barcode_details_df %>% mutate(replicate = str_remove(str_extract(regulator_symbol, "x\\d"), "x")) %>% mutate(condition = case_when( batch == "run_7380" & replicate == 2 ~ 'del_MET28', regulator_symbol == "CBF1KOmet28" ~ 'del_MET28', batch == "run_7380" & replicate == 2 ~ 'glu_1_gal_2', batch == "run_7388" & replicate == 2 ~ 'glu_1_gal_2', batch == "run_7390" & replicate == 2 ~ 'glu_1_gal_2', batch == "run_7392" & replicate == 2 ~ 'glu_1_gal_2', .default = 'standard')) %>% mutate(replicate = ifelse(condition != "standard", 1, replicate)) %>% mutate(regulator_symbol = ifelse(regulator_symbol == 'CBF1KOmet28', "CBF1", regulator_symbol)) %>% mutate(regulator_symbol = str_remove(regulator_symbol, "x\\d")) %>% replace_na(list(replicate = '1')) %>% mutate(replicate = as.integer(replicate)) %>% filter(batch != "run_5690") %>% mutate(batch = ifelse(batch == 'run_5690_correct', 'run_5690', batch)) %>% left_join( binding_django %>% select(id, regulator_symbol, batch, replicate)) %>% dplyr::rename(binding_id = id) %>% mutate(binding_id = as.character(binding_id)) %>% replace_na(list(binding_id = "NA")) brentlab_dirs = list.dirs("~/htcf_lts/sequence_data/yeast_cc/sequence", recursive=FALSE) mitra_dirs = list.dirs("~/htcf_lts/sequence_data/yeast_cc/sequence/mitra_data", recursive=FALSE) setdiff(basename(c(brentlab_dirs, mitra_dirs)), unique(binding_django$batch)) gm_db = arrow::open_dataset("~/code/hf/callingcards/genome_map") genome_map_meta_raw = arrow::read_parquet("~/code/hf/callingcards/genome_map_meta.parquet") genome_map_meta = genome_map_meta_raw %>% filter(condition == "standard") %>% filter(!batch %in% c('dsir4')) fastq_df_with_id = fastq_df %>% left_join(genome_map_meta %>% mutate(regulator_symbol = ifelse(str_detect(regulator_symbol, "unknown"), regulator_locus_tag, regulator_symbol))) %>% filter(condition == 'standard') %>% filter(batch != "run_7392") fastq_df_lookup = fastq_df_with_id %>% mutate(filename = file.path("passing_fastq_from_bam", filename), newname = paste0(regulator_locus_tag, "_", regulator_symbol, "_", id)) %>% select(filename, newname) bam_lookup = passing_bam_paths %>% mutate(base = str_remove(basename(bampath), ".bam")) %>% # this is empty, also excluded from the fastqs filter(base != "run_6739_MOT3_passing_tagged") %>% left_join(fastq_df_lookup %>% mutate(base = str_remove(basename(filename), ".fastq.gz"))) %>% filter(str_detect(bampath, "undetermined", negate=TRUE)) %>% select(-filename) %>% # this removes the non standard conditions filter(complete.cases(.)) setdiff(bam_lookup$newname, fastq_df_lookup$newname) setdiff(fastq_df_lookup$newname, bam_lookup$newname) # bam_lookup %>% # select(bampath, newname) %>% # mutate(newname = paste0(newname, ".bam")) %>% # write_tsv("~/htcf_local/cc/yeast/passing_bam_rename_lookup.txt", # col_names = FALSE) # fastq_df_lookup %>% # write_tsv("~/htcf_local/cc/yeast/passing_bam_fastq_rename_lookup.txt", # col_names = FALSE) # fastq_df_with_id %>% # filter(binding_id == "NA") %>% # filter(condition == "standard") %>% # filter(regulator_symbol != "OTU1") %>% # mutate(qbed_path = file.path(sprintf("/home/chase/htcf_local/cc/yeast/results/%s/hops/%s_%s.qbed", batch, batch, regulator_symbol_replicate))) %>% # select(qbed_path) %>% # write_tsv("~/tmp/unprocessed_in_db_qbeds_lookup.txt") # ACE2, ARG80, ARG81, LYS14, STB5, SWI5, SWI6 pass in at least one (didn't check which mcisaac cond pass, but all pass in kemmeren pass) # CUP9 and DAL80 pass in mcisaac at 15 minutes # # WTM1, MIG2, DIG1 ACA1 fails due to non-passing in kemmeren and/or mcisaac. NOTE: the only timepoint MIG2 fails is 15 minutes. # UME6 has no 15 minute condition in mcisaac and is not in kemmeren af_django_meta = arrow::read_parquet("~/code/hf/callingcards/annotated_features_meta.parquet") %>% filter(condition == "standard") %>% filter(genome_map_id %in% genome_map_meta$id) %>% replace_na(list(binding_id = "NA")) %>% mutate(data_usable = case_when( batch %in% c('run_7477', 'run_7487') & regulator_symbol %in% c('ACE2', 'ARG80', 'ARG81', 'LYS14', 'STB5', 'SWI5', 'SWI6', 'CUP9', 'DAL80') ~ "pass", batch %in% c('run_7477', 'run_7487') & regulator_symbol %in% c('WTM1', 'MIG2', 'DIG1', 'ACA1') ~ "fail", .default = data_usable)) %>% mutate(in_modeling_analysis = binding_id %in% analysis_binding_ids) %>% mutate(kemmeren_dto = binding_id %in% (qc_django_data_in_modeling$kemmeren %>% filter(dto_empirical_pvalue <= 0.01) %>% pull(single_binding))) %>% mutate(mcisaac_dto = binding_id %in% (qc_django_data_in_modeling$mcisaac %>% filter(dto_empirical_pvalue <= 0.01) %>% pull(single_binding))) %>% left_join(qc_django_data %>% filter(!is.na(single_binding), binding_source == "brent_nf_cc") %>% select(single_binding, genomic_inserts) %>% distinct() %>% mutate(single_binding = as.character(single_binding)) %>% dplyr::rename(binding_id = single_binding)) %>% left_join(select(barcode_details_df_with_id, condition, regulator_symbol, batch, binding_id, r1_index, r2_index)) %>% dplyr::select(id, genome_map_id, batch, r1_index, r2_index, regulator_locus_tag, regulator_symbol, data_usable, kemmeren_dto, mcisaac_dto, genomic_inserts, in_modeling_analysis) %>% mutate(notes = case_when( genome_map_id %in% c(690, 685) ~ "manually exluded from analysis in favor of library 242", genome_map_id %in% c(26, 612, 300, 119) ~ "less than 3k insertions", .default = "none")) %>% arrange(genome_map_id) af_db = arrow::open_dataset("~/code/hf/callingcards/annotated_features") setdiff(basename(c(brentlab_dirs, mitra_dirs)), unique(genome_map_meta$batch)) setdiff(unique(genome_map_meta$batch),basename(c(brentlab_dirs, mitra_dirs))) # db2506 = read_tsv("~/Downloads/2506.qbed.gz") # hf2506 = gm_db %>% # filter(id == 1) %>% # collect() # Specify output directory # output_dir <- here("~/htcf_local/cc/yeast/callingcards_geo_submission/processed") # dir.create(output_dir, showWarnings = FALSE, recursive = TRUE) # Get unique combinations of id and batch id_batch_combos <- af_django_meta %>% select(id, genome_map_id, batch) %>% distinct() # for (i in 1:nrow(id_batch_combos)) { # current_id <- id_batch_combos$id[i] # current_batch <- id_batch_combos$batch[i] # gm_id = id_batch_combos$genome_map_id[i] # # # Filter and format as af file # af_data <- af_db %>% # filter(id == current_id, # batch == current_batch) %>% # collect() %>% # mutate(genome_map_id = gm_id) %>% # select(-id) %>% # dplyr::rename(library_name = genome_map_id) %>% # mutate(target_symbol = ifelse(str_detect(target_symbol, "unknown"), # target_locus_tag, target_symbol)) %>% # dplyr::relocate(library_name, batch) # # # Create filename # filename <- file.path(output_dir, paste0(gm_id, ".csv.gz")) # # # Write gzipped csv file # write_csv(af_data, filename) # # if (i %% 10 == 0) cat("Wrote", i, "of", nrow(id_batch_combos), "files\n") # } # # cat("Done! Wrote", nrow(id_batch_combos), "CSV files to", output_dir, "\n") af_data = arrow::open_dataset("~/code/hf/callingcards/annotated_features") %>% filter(id %in% af_django_meta$id) %>% collect() %>% left_join( af_django_meta %>% mutate(regulator_locus_tag = as.character(regulator_locus_tag), regulator_symbol = as.character(regulator_symbol)) %>% mutate(regulator_symbol = ifelse(str_detect(regulator_symbol, "unknown"), regulator_locus_tag, regulator_symbol)) %>% mutate(filename = paste0(regulator_locus_tag, "_", regulator_symbol, "_", genome_map_id)) %>% select(id, genome_map_id, filename)) promoters = read_tsv("~/code/hf/yeast_genome_resources/yiming_promoters.bed", col_names = c("chr", "start", "end", "target_locus_tag", "score", "strand")) %>% dplyr::select(target_locus_tag, chr, start, end, strand) # af_data %>% # group_by(filename) %>% # group_walk(~ { # dir.create(here("results/processed"), showWarnings = FALSE) # output_name = paste0(.y$filename, ".csv.gz") # .x %>% # mutate(target_symbol = ifelse(str_detect(target_symbol, "unknown"), target_locus_tag, target_symbol)) %>% # dplyr::select(-c(id, hypergeometric_pval, batch)) %>% # dplyr::rename(enrichment = callingcards_enrichment) %>% # left_join(promoters) %>% # dplyr::relocate(genome_map_id, target_locus_tag, target_symbol, chr, start, end, strand) %>% # write_csv(file.path(here("results/processed"), output_name)) # }) submission_df = af_django_meta %>% select(-id) %>% arrange(regulator_locus_tag) %>% mutate(regulator_locus_tag = as.character(regulator_locus_tag), regulator_symbol = as.character(regulator_symbol)) %>% mutate(regulator_symbol = ifelse( str_detect(regulator_symbol, "unknown"), regulator_locus_tag, regulator_symbol)) %>% mutate(`library name` = paste0(regulator_locus_tag, "_", regulator_symbol, "_", genome_map_id)) %>% mutate(title = paste0(regulator_locus_tag, " (", regulator_symbol, ") calling cards; gmid ", genome_map_id), `library strategy` = "OTHER", organism = 'Saccharomyces cerevisiae', `cell line` = "Saccharomyces cerevisiae S288C", molecule = 'genomic DNA', `single or paired-end` = 'single', `instrument model` = 'Illumina MiSeq', description = paste0(regulator_locus_tag, " tagged callingcards experiment. ", "kemmeren_dto: ", kemmeren_dto, "; mcisaac_dto: ", mcisaac_dto, "; in_modeling_analysis: ", in_modeling_analysis, "; notes: ", notes), `processed data file` = paste0(`library name`, ".csv.gz"), `raw file` = paste0(`library name`, ".bam")) %>% dplyr::rename(perturbation_validated = data_usable) %>% dplyr::select( `library name`, title, `library strategy`, organism, `cell line`, molecule, `single or paired-end`, `instrument model`, description, perturbation_validated, `processed data file`, `raw file` ) setdiff(paste0(bam_lookup$newname,".bam"), submission_df$`raw file`) setdiff(submission_df$`raw file`, paste0(bam_lookup$newname,".bam")) write_csv(submission_df, here("data/cc_submission_df.csv")) ################################################################################ ################################################################################ ################################################################################ # library(tidyverse) # library(arrow) # library(here) # # genome_map_meta = arrow::read_parquet("~/code/hf/callingcards/genome_map_meta.parquet") # # genome_map_meta_af = genome_map_meta %>% filter(batch %in% c("run_6778", "run_7380", "run_7388", "run_7390")) # # af = list.files("~/tmp/callingcards_output", full.names = TRUE) # # af_gmid_map = tibble( # af = str_remove(basename(af), "_af.csv"), # batch = str_extract(af, "run_\\d+"), # regulator_orig = str_remove(af, paste0(batch, "_")), # regulator_symbol = str_remove(regulator_orig, "x\\d")) %>% # mutate(regulator_symbol = ifelse(regulator_symbol == 'CBF1KOmet28', "CBF1", regulator_symbol)) %>% # mutate(condition = case_when( # str_detect(regulator_orig, "x\\d$", negate=TRUE) | str_detect(regulator_orig, "x1$") ~ 'standard', # regulator_orig == 'CBF1x2' ~ 'del_MET28', # regulator_orig == 'CBF1KOmet28' ~ 'del_MET28', # regulator_orig == 'GCR1x2' ~ 'glu_1_gal_2', # regulator_orig == 'GCR2x2' ~ 'glu_1_gal_2', # regulator_orig == 'TYE7x2' ~ 'glu_1_gal_2', # regulator_orig == "MIG1x2" ~ 'glu_1_gal_2')) %>% # left_join(dplyr::select(genome_map_meta_af, batch, regulator_symbol, condition, id)) %>% # mutate(id = ifelse(regulator_orig == 'CBF1KOmet28', 746, id)) # # # af_df = map(af, read_csv) # names(af_df) = af_gmid_map$id # # genomicfeatures = arrow::read_parquet("~/code/hf/yeast_genome_resources/brentlab_features.parquet") # # promoters = read_tsv("~/code/hf/yeast_genome_resources/yiming_promoters.bed", # col_names = c("chr", "start", "end", "name", "score", "strand")) %>% # dplyr::select("chr", "name", "start", "end", "strand") %>% # left_join(dplyr::select(genomicfeatures, name = locus_tag, symbol)) # # af_df_all = bind_rows(af_df, .id = "genome_map_id") %>% # mutate(genome_map_id = as.integer(genome_map_id)) %>% # dplyr::select(c( # 'genome_map_id','name','experiment_hops', # 'background_hops','background_total_hops','experiment_total_hops', # 'callingcards_enrichment','poisson_pval','hypergeometric_pval')) %>% # left_join(promoters) %>% # dplyr::rename(target_locus_tag = name, target_symbol = symbol) %>% # dplyr::select( # c('genome_map_id','target_locus_tag','target_symbol','experiment_hops', # 'background_hops','background_total_hops','experiment_total_hops', # 'callingcards_enrichment','poisson_pval','hypergeometric_pval')) %>% # left_join(dplyr::select(af_gmid_map, id, batch), by = c("genome_map_id" = "id")) %>% # mutate(genome_map_id = as.integer(genome_map_id)) # # af_meta = arrow::read_parquet('~/code/hf/callingcards/annotated_features_meta.parquet') # # af_meta_new = af_gmid_map %>% # dplyr::select(id, batch, regulator_symbol, condition) %>% # dplyr::rename(genome_map_id = id) %>% # left_join(dplyr::select(genomicfeatures, # regulator_locus_tag = locus_tag, # regulator_symbol = symbol)) %>% # mutate(data_usable = 'unreviewed', analysis_set = FALSE) %>% # # note that this is 810 before adding these records # mutate(id = max(af_meta$id)+row_number(), # pss_id = "NA", # binding_id = "NA") %>% # dplyr::relocate(id) # # af_meta_augment = af_meta %>% # dplyr::select(-preferred_replicate) %>% # bind_rows(af_meta_new) %>% # replace_na(list(binding_id = "NA")) # # # af_meta_augment |> # # arrow::as_arrow_table() |> # # (\(tbl) { # # dict_cols <- c("data_usable", "batch", "condition", "regulator_locus_tag", "regulator_symbol") # # for (col in dict_cols) { # # tbl[[col]] <- tbl[[col]]$cast(arrow::dictionary()) # # } # # tbl # # })() |> # # arrow::as_arrow_table() |> # # arrow::write_parquet( # # "/home/chase/code/hf/callingcards/annotated_features_meta.parquet", # # compression = "zstd", # # compression_level = 9, # # write_statistics = TRUE # # ) # # # # af_df_all %>% # # left_join(dplyr::select(af_meta_new, id, genome_map_id)) %>% # # dplyr::relocate(id) %>% # # select(-genome_map_id) %>% # # arrow::write_dataset( # # path = "/home/chase/code/hf/callingcards/annotated_features_tmp", # # format = "parquet", # # partitioning = c("batch"), # # existing_data_behavior = "error", # # compression = "zstd", # # compression_level = 9, # # write_statistics = TRUE, # # use_dictionary = TRUE)