library(tidyverse) library(readxl) library(here) library(arrow) # genomic feature harmonization table ---- # see https://huggingface.co/datasets/BrentLab/yeast_genome_resources genes_table = arrow::open_dataset(here("data/genome_files/hf/features")) %>% as_tibble() # function to read in the harbison raw data ---- read_in_harbison_data = function(xls_path, sheet_name, values_colname, n_max=6229){ read_excel(xls_path, sheet=sheet_name, skip=1, n_max = n_max) %>% dplyr::rename(locus_tag=`...1`, gene_name=`...2`, description=`...3`) %>% mutate(across(!c(locus_tag, gene_name, description), as.numeric)) %>% pivot_longer(!c(locus_tag, gene_name, description), names_to='tf_cond', values_to=values_colname) %>% separate(tf_cond, c('tf', 'condition'), sep="_") } # read in the raw pvalue data ---- # note that n_max is set to 6229, which is the end of the genomic data. on some # sheets, there is data accidently left in by the original researcher harbison_pval_df_list = list( ypd = read_in_harbison_data( here('data/harbison/pvalbygene_forpaper_abbr.xls'), 'YPD', 'pvalue'), other_conds = read_in_harbison_data( here('data/harbison/pvalbygene_forpaper_abbr.xls'), "other conditions", "pvalue") ) harbison_pval_df = bind_rows(harbison_pval_df_list) # read in the raw effect ratio data ---- harbison_effect_df_list = list( ypd = read_in_harbison_data( here('data/harbison/ratiobygene_forpaper_abbr.xls'), 'YPD', "effect"), other_conds = read_in_harbison_data( here('data/harbison/ratiobygene_forpaper_abbr.xls'), 'Other Conditions', 'effect')) harbison_effect_df = bind_rows(harbison_effect_df_list) # combine the effect and pvalue data ---- combined_harbison = harbison_effect_df %>% select(-gene_name) %>% left_join(select(harbison_pval_df, -gene_name)) %>% # remove the control sample and remove locus tags that correspond to # now deleted ORFs (these weren't merged into other annotations, they were # simply complely removed from the annotation set) filter(!locus_tag %in% c('YCL006C', 'YCR103C', 'YER187W-A')) %>% dplyr::rename(harbison_locus_tag = locus_tag) %>% mutate(tf = trimws(toupper(tf))) # create a lookup map from the harbison targets to SGD 3-1 ---- locus_tags = combined_harbison %>% select(harbison_locus_tag) %>% distinct() %>% left_join(genes_table, by = c("harbison_locus_tag" = "locus_tag")) %>% mutate(locus_tag = harbison_locus_tag) %>% select(harbison_locus_tag, locus_tag, symbol) locus_tags_aliases = read_csv('data/harbison/locus_tags_aliases.csv') %>% left_join(genes_table, by = c('curr_notation'='locus_tag')) %>% dplyr::rename(locus_tag = curr_notation) %>% select(harbison_locus_tag, locus_tag, symbol) locus_tags_complete = locus_tags %>% filter(!is.na(symbol)) %>% bind_rows(locus_tags_aliases) %>% dplyr::rename(target_locus_tag = locus_tag, target_symbol = symbol) stopifnot(nrow(locus_tags_complete) == nrow(locus_tags)) stopifnot(setequal(unique(combined_harbison$harbison_locus_tag), locus_tags_complete$harbison_locus_tag)) # create a lookup map from the regulator names to SGD 3-1 ---- tf_symbol_to_locus_tag_df = combined_harbison %>% select(tf) %>% mutate(tf = trimws(toupper(tf))) %>% distinct() %>% left_join(genes_table, by = c('tf' = 'symbol')) %>% select(tf, locus_tag) %>% dplyr::rename(regulator_locus_tag = locus_tag) %>% mutate(regulator_symbol = tf) %>% select(tf, regulator_symbol, regulator_locus_tag) tf_locus_tag_to_locus_tag_df = tf_symbol_to_locus_tag_df %>% filter(!complete.cases(.)) %>% select(tf) %>% left_join(genes_table, by = c('tf' = 'locus_tag')) %>% filter(!is.na(symbol)) %>% select(tf, symbol) %>% dplyr::rename(regulator_symbol = symbol) %>% mutate(regulator_locus_tag = tf) tf_name_df_na = read_csv("data/harbison/tf_name_aliases.csv") %>% left_join(genes_table) %>% # tf_main is the current SGD 3-1 symbol select(tf, tf_main, locus_tag) %>% dplyr::rename(regulator_symbol = tf_main, regulator_locus_tag = locus_tag) %>% select(tf, regulator_symbol, regulator_locus_tag) harbison_tf_map = bind_rows( tf_symbol_to_locus_tag_df[complete.cases(tf_symbol_to_locus_tag_df),], tf_locus_tag_to_locus_tag_df, tf_name_df_na ) stopifnot(setequal(harbison_tf_map$tf, unique(combined_harbison$tf))) # create the harmonized data ---- combined_harbison_harmonized = combined_harbison %>% left_join(harbison_tf_map) %>% left_join(locus_tags_complete) %>% dplyr::rename(harbison_regulator=tf) %>% select(-description) harbison_db_meta <- read_csv("data/promotersetsig_db_20251127.csv", col_types = cols(composite_binding = col_character())) db_id_map = harbison_db_meta %>% filter(source_name == "harbison_chip") %>% select(id, regulator_locus_tag, condition) %>% distinct() %>% dplyr::rename(db_id = id) %>% bind_rows( tibble( regulator_locus_tag = c("YSC0017"), db_id = 0, condition = c('YPD') )) combined_harbison_harmonized_for_parquet = combined_harbison_harmonized %>% select(regulator_locus_tag, regulator_symbol, condition, target_locus_tag, target_symbol, effect, pvalue) %>% left_join(db_id_map) %>% arrange(db_id) %>% group_by(db_id) %>% mutate(sample_id = cur_group_id()) %>% ungroup() %>% select(sample_id, db_id, regulator_locus_tag, regulator_symbol, condition, target_locus_tag, target_symbol, effect, pvalue) # combined_harbison_harmonized_for_parquet %>% # write_parquet("~/code/hf/harbison_2004/harbison_2004.parquet", # compression = "zstd", # write_statistics = TRUE, # chunk_size = 6226, # use_dictionary = c( # sample_id = TRUE, # condition = TRUE, # regulator_locus_tag = TRUE, # regulator_symbol = TRUE, # target_locus_tag = TRUE, # target_symbol = TRUE # ) # ) # # # combined_harbison_harmonized %>% # select(harbison_locus_tag, target_locus_tag) %>% # distinct() %>% # write_csv("~/code/hf/harbison_2004/scripts/harbison_locus_tag_to_target_locus_tag.csv") # # combined_harbison_harmonized %>% # select(harbison_regulator, regulator_locus_tag) %>% # distinct() %>% # write_csv("~/code/hf/harbison_2004/scripts/harbison_regulator_to_regulator_locus_tag.csv")