Datasets:

BrentLab
/

harbison_2004

+harbison_regulator,regulator_locus_tag
+A1 (MATA1),YSC0017
+ABF1,YKL112W
+ABT1,YNR054C
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+ZMS1,YJR127C

scripts/parse_harbison_data.R ADDED Viewed

	@@ -0,0 +1,137 @@

+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)
+combined_harbison_harmonized %>%
+  select(condition, regulator_locus_tag, target_locus_tag, effect, pvalue) %>%
+  group_by(condition, regulator_locus_tag) %>%
+  write_dataset(path = here("data/harbison/hf/data"), format = "parquet")