File size: 7,411 Bytes
b8df475 1fcd609 b8df475 1fcd609 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 |
library(tidyverse)
library(here)
library(arrow)
library(readxl)
# from fetchngs, the multiqc metadata has an easy way to map between
# accessions and samples
multqc_chec_config = yaml::read_yaml("~/Downloads/multiqc_config.yml")
chec_meta <- map_dfr(multqc_chec_config$sample_names_rename,
~tibble(!!!setNames(., multqc_chec_config$sample_names_rename_buttons)))
# genomic feature harmonization table ----
# see https://huggingface.co/datasets/BrentLab/yeast_genome_resources
genomicfeatures = arrow::open_dataset(here("data/genome_files/hf/features")) %>%
as_tibble()
chec_data = read_excel(here("data/mahendrawada_2024_rnaseq/41586_2025_8916_MOESM5_ESM.xlsx"),
sheet="Table-S3b") %>%
dplyr::rename(mahedrawada_target = `...1`) %>%
pivot_longer(-mahedrawada_target,
names_to = "mahedrawada_regulator_orig",
values_to = "peak_score") %>%
arrange(mahedrawada_regulator_orig, mahedrawada_target) %>%
filter(!is.na(peak_score)) %>%
mutate(mahedrawada_regulator = case_when(
mahedrawada_regulator_orig == "MED15" ~ "GAL11",
mahedrawada_regulator_orig == "YNR063W" ~ "PUL4",
.default = mahedrawada_regulator_orig
))
rnaseq_data = read_excel(here("data/mahendrawada_2024_rnaseq/41586_2025_8916_MOESM5_ESM.xlsx"),
sheet="Table-S3c") %>%
dplyr::rename(mahedrawada_target = `...1`) %>%
pivot_longer(-c(mahedrawada_target, Kmeans_clusters),
names_to = "mahedrawada_regulator_tmp",
values_to = "log2fc") %>%
arrange(mahedrawada_regulator_tmp, mahedrawada_target) %>%
filter(!is.na(log2fc)) %>%
separate(mahedrawada_regulator_tmp, into = c("mahedrawada_regulator_orig", "cond")) %>%
replace_na(list(cond="SC")) %>%
mutate(
mahedrawada_regulator = case_when(
mahedrawada_regulator_orig == "MED15" ~ "GAL11",
mahedrawada_regulator_orig == "GALG4" ~ "GAL4",
mahedrawada_regulator_orig == "YNR063W" ~ "PUL4",
.default = mahedrawada_regulator_orig),
cond = case_when(
mahedrawada_regulator_orig == "GALG4" ~ "GAL",
.default = cond
))
mahedrawada_genomicfeatures <- read_excel(
here("data/mahendrawada_2024_rnaseq/41586_2025_8916_MOESM4_ESM.xlsx"),
sheet = "ref_all"
) %>%
mutate(gene_name = case_when(
gene_name == "YPR022C" ~ "SDD4",
gene_name == "YNR063W" ~ "PUL4",
.default = gene_name
))
stopifnot(setequal(intersect(chec_data$mahedrawada_target, mahedrawada_genomicfeatures$gene_id),
chec_data$mahedrawada_target))
stopifnot(setequal(intersect(rnaseq_data$mahedrawada_target, mahedrawada_genomicfeatures$gene_id),
rnaseq_data$mahedrawada_target))
stopifnot(setequal(intersect(chec_data$mahedrawada_regulator, mahedrawada_genomicfeatures$gene_name),
chec_data$mahedrawada_regulator))
stopifnot(setequal(intersect(rnaseq_data$mahedrawada_regulator, mahedrawada_genomicfeatures$gene_name),
rnaseq_data$mahedrawada_regulator))
# note: verified by hand that where the symbol != gene_name, that the gene_name
# was listed as an alias of the current official symbol.
# Where the gene_name is == to the gene_id, that id is == to the locus_tag
stopifnot(
tibble(mahedrawada_target = union(chec_data$mahedrawada_target,
rnaseq_data$mahedrawada_target)) %>%
left_join(genomicfeatures %>% select(locus_tag, symbol),
by = c("mahedrawada_target" = "locus_tag")) %>%
left_join(mahedrawada_genomicfeatures %>% select(gene_name, gene_id),
by = c("mahedrawada_target" = "gene_id")) %>%
filter(!complete.cases(.)) %>%
nrow() == 0)
stopifnot(
tibble(mahedrawada_regulator = union(chec_data$mahedrawada_regulator,
rnaseq_data$mahedrawada_regulator)) %>%
left_join(genomicfeatures %>%
select(locus_tag, symbol),
by = c("mahedrawada_regulator" = "symbol")) %>%
left_join(mahedrawada_genomicfeatures %>%
select(gene_name, gene_id),
by = c("mahedrawada_regulator" = "gene_name")) %>%
filter(!complete.cases(.) | locus_tag != gene_id) %>%
nrow() == 0)
chec_data_final = chec_data %>%
left_join(
genomicfeatures %>%
select(locus_tag, symbol) %>%
mutate(mahedrawada_target = locus_tag) %>%
dplyr::rename(target_locus_tag = locus_tag, target_symbol = symbol)) %>%
left_join(
genomicfeatures %>%
select(locus_tag, symbol) %>%
mutate(mahedrawada_regulator = symbol) %>%
dplyr::rename(regulator_locus_tag = locus_tag, regulator_symbol = symbol)) %>%
select(regulator_locus_tag, regulator_symbol, target_locus_tag, target_symbol, peak_score) %>%
arrange(regulator_locus_tag, target_locus_tag)
rnaseq_data_final = rnaseq_data %>%
left_join(
genomicfeatures %>%
select(locus_tag, symbol) %>%
mutate(mahedrawada_target = locus_tag) %>%
dplyr::rename(target_locus_tag = locus_tag, target_symbol = symbol)) %>%
left_join(
genomicfeatures %>%
select(locus_tag, symbol) %>%
mutate(mahedrawada_regulator = symbol) %>%
dplyr::rename(regulator_locus_tag = locus_tag, regulator_symbol = symbol)) %>%
select(regulator_locus_tag, regulator_symbol, target_locus_tag, target_symbol, log2fc) %>%
arrange(regulator_locus_tag, target_locus_tag)
sample_id_map = tibble(
regulator_locus_tag = union(rnaseq_data_final$regulator_locus_tag,
chec_data_final$regulator_locus_tag)) %>%
mutate(sample_id = row_number())
# chec_data_final %>%
# left_join(sample_id_map) %>%
# select(sample_id, all_of(colnames(chec_data_final))) %>%
# write_parquet(here("~/code/hf/mahendrawada_2025/chec_mahendrawada_2025.parquet"),
# compression = "zstd",
# write_statistics = TRUE,
# use_dictionary = c(
# sample_id = TRUE,
# regulator_locus_tag = TRUE,
# regulator_symbol = TRUE,
# target_locus_tag = TRUE,
# target_symbol = TRUE
# )
# )
#
# rnaseq_data_final %>%
# left_join(sample_id_map) %>%
# select(sample_id, all_of(colnames(rnaseq_data_final))) %>%
# write_parquet(here("~/code/hf/mahendrawada_2025/rnaseq_mahendrawada_2025.parquet"),
# compression = "zstd",
# write_statistics = TRUE,
# use_dictionary = c(
# sample_id = TRUE,
# regulator_locus_tag = TRUE,
# regulator_symbol = TRUE,
# target_locus_tag = TRUE,
# target_symbol = TRUE
# )
# )
#
# mahedrawada_genomicfeatures %>%
# left_join(genomicfeatures %>%
# select(locus_tag, symbol) %>%
# mutate(gene_id = locus_tag)) %>%
# write_parquet(here("~/code/hf/mahendrawada_2025/features_mahendrawada_2025.parquet"),
# compression = "zstd",
# write_statistics = TRUE,
# use_dictionary = c(
# gene_id = TRUE,
# gene_name = TRUE,
# chr = TRUE,
# locus_tag = TRUE,
# symbol = TRUE,
# coactivator = TRUE
# )
# )
|