library(tidyverse) library(httr) library(here) library(arrow) library(readxl) ## NOTES: ## ## Hu had a heat shock and tetracycline condition. Reimand says they ## use: ## Further analysis was performed separately for the following ## three cases: A) same-strain replicates (BY4741); ## B) replicates of different strains (BY4741 and S288C); ## c) replicates of the induced promoter strain (R1158+TET) ## ## which i interpreted to mean (given some inspection from the original Hu ## metadata), that it should be !heat_shock and !tetracycline except ## for the regulators which did not have those conditions, in which ## case I kept the tetracyclie condition. # genomic feature harmonization table ---- # see https://huggingface.co/datasets/BrentLab/yeast_genome_resources gene_table = arrow::open_dataset(here("data/genome_files/hf/features")) %>% as_tibble() hu_data = list( de = here("data/hu/gkq232_SuppTable1C_KOTargetGenes_Matrix_LogFoldChange.dat.xz"), pval = here("data/hu/gkq232_SuppTable1B_KOTargetGenes_Matrix_PValue.dat.xz") ) hu_de_target_genes = as.character(read_delim(hu_data$de, delim=' ', col_names = FALSE, n_max = 1)[1,]) hu_de_target_genes = str_replace(hu_de_target_genes, "\\.", "-") hu_de_colnames = c('hu_regulator', hu_de_target_genes) hu_pval_target_genes = as.character(read_delim(hu_data$pval, delim=' ', col_names = FALSE, n_max = 1)[1,]) hu_pval_target_genes = str_replace(hu_pval_target_genes, "\\.", "-") hu_pval_colnames = c('hu_regulator', hu_pval_target_genes) stopifnot(setequal(hu_pval_colnames, hu_de_colnames)) # Note: The warning is expected. this is being parsed # correctly despite the warning de_df = read.delim( hu_data$de, sep=' ', col.names = hu_de_colnames, check.names=FALSE) %>% as_tibble() # Note: The warning is expected. this is being parsed # correctly despite the warning pval_df = read.delim( hu_data$pval, sep=' ', col.names = hu_pval_colnames, check.names=FALSE) %>% as_tibble() hu_df = de_df %>% pivot_longer(-hu_regulator, names_to='hu_target', values_to='effect') %>% left_join(pval_df %>% pivot_longer(-hu_regulator, names_to='hu_target', values_to='pval')) %>% mutate(hu_regulator = toupper(hu_regulator)) %>% # remove deleted orfs filter(!hu_target %in% c('YER187W-A', 'YCR103C', 'YCL006C', "YIL080W")) tf_map = list() tf_map$init = hu_df %>% distinct(hu_regulator) %>% mutate(regulator_symbol = hu_regulator) %>% # these are labeled with aliases in the hu data mutate(regulator_symbol = case_when( regulator_symbol == "CAF17" ~ "IBA57", regulator_symbol == "RCS1" ~ "AFT1", regulator_symbol == "RLR1" ~ "THO2", regulator_symbol == "ZMS1" ~ "RSF2", regulator_symbol == "RIS1" ~ "ULS1", .default = regulator_symbol )) %>% left_join(gene_table %>% select(symbol,locus_tag) %>% dplyr::rename(regulator_locus_tag = locus_tag, regulator_symbol = symbol)) %>% mutate(regulator_symbol = ifelse(is.na(regulator_locus_tag), NA, regulator_symbol)) tf_map$locus_tags = tf_map$init %>% filter(!complete.cases(.)) %>% select(hu_regulator) %>% mutate(regulator_locus_tag = hu_regulator) %>% left_join(gene_table %>% select(symbol,locus_tag) %>% dplyr::rename(regulator_locus_tag = locus_tag, regulator_symbol = symbol)) tf_map_df = bind_rows( tf_map$init %>% filter(complete.cases(.)), tf_map$locus_tags ) stopifnot(setequal(tf_map$init$hu_regulator, tf_map_df$hu_regulator)) target_map = list() target_map$init = hu_df %>% distinct(hu_target) %>% mutate(target_locus_tag = hu_target) %>% left_join(select(gene_table, locus_tag, symbol) %>% dplyr::rename(target_locus_tag = locus_tag, target_symbol = symbol)) target_map$alias_df = gene_table %>% mutate(hu_target = map_chr(alias, ~ { alias_i <- .x match <- keep(target_map$init %>% filter(!complete.cases(.)) %>% pull(hu_target), ~ str_detect(alias_i, .x)) if (length(match) > 0) match[[1]] else NA_character_ })) %>% filter(!is.na(hu_target)) %>% select(hu_target, locus_tag, symbol) %>% dplyr::rename(target_locus_tag = locus_tag, target_symbol = symbol) target_map_df = bind_rows(target_map$init %>% filter(complete.cases(.)), target_map$alias_df) stopifnot(setequal(target_map_df$hu_target, unique(hu_df$hu_target))) final_df = hu_df %>% left_join(tf_map_df) %>% left_join(target_map_df) # never used -- don't bother # hu_reimand_from_db = read_csv("data/hu/hu_2007_reimand_db_20251127.csv") # from https://www.nature.com/articles/ng2012#Sec10 supplement_table_1 = readxl::read_excel("data/hu/41588_2007_BFng2012_MOESM20_ESM.xlsx") %>% janitor::clean_names() %>% dplyr::rename(regulator_locus_tag = `tf_systematic_name`) %>% select(-tf_gene_name) %>% mutate(heat_shock = str_detect(growth_condition, "heat-shock"), tetracycline_treatment = str_detect(growth_condition, "tetracycline treatment")) %>% select(-growth_condition) supplement_table_1_filt = supplement_table_1 %>% filter(!heat_shock, !tetracycline_treatment) supplement_table_1_metadata = supplement_table_1_filt %>% bind_rows( supplement_table_1 %>% filter( !heat_shock, regulator_locus_tag %in% setdiff(final_df$regulator_locus_tag, supplement_table_1_filt$regulator_locus_tag))) final_df_for_parquet = final_df %>% mutate(sample_id = cur_group_id()) %>% select(sample_id, regulator_locus_tag, regulator_symbol, target_locus_tag, target_symbol, effect, pval) %>% ungroup() %>% arrange(regulator_locus_tag) %>% left_join(supplement_table_1_metadata) final_df_for_parquet %>% write_parquet(here("~/code/hf/hu_2007_reimand_2010/hu_2007_reimand_2010.parquet"), compression = "zstd", chunk_size = 6249, write_statistics = TRUE, use_dictionary = c( sample_id = TRUE, regulator_locus_tag = TRUE, regulator_symbol = TRUE, target_locus_tag = TRUE, target_symbol = TRUE, heat_shock = TRUE, tetracycline_treatment = TRUE ))