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library(tidyverse)
library(DESeq2)
library(arrow)
library(here)
run_degron_deseq2 <- function(regulator_sym, env_cond, timepoint_val,
degron_data = degron,
baseline_data = baseline_control) {
# Filter degron data for the specified regulator, condition, and timepoint
degron_meta_filtered <- degron_data$meta %>%
filter(
regulator_symbol == regulator_sym,
env_condition == env_cond,
timepoint == timepoint_val) %>%
mutate(degron_treatment = factor(degron_treatment,
levels = c("DMSO", "IAA")))
if (nrow(degron_meta_filtered) != 6) {
stop(sprintf("We expect exactly 3 replicates in each conditition for a total of 6 samples. These parameters result in %s", nrow(degron_meta_filtered)))
}
# Get the IAA and DMSO samples
iaa_samples <- degron_meta_filtered %>%
filter(degron_treatment == "IAA") %>%
pull(sra_accession)
dmso_samples <- degron_meta_filtered %>%
filter(degron_treatment == "DMSO") %>%
pull(sra_accession)
if (length(iaa_samples) != 3 || length(dmso_samples) != 3) {
stop("Missing IAA or DMSO samples for the specified parameters")
}
# Get counts for these samples
counts_filtered <- degron_data$counts_long %>%
filter(sra_accession %in% c(iaa_samples, dmso_samples))
# Convert to wide format (genes x samples)
counts_wide <- counts_filtered %>%
select(target_locus_tag, sra_accession, count) %>%
pivot_wider(
names_from = sra_accession,
values_from = count) %>%
column_to_rownames("target_locus_tag")
# Create sample metadata
sample_meta <- degron_meta_filtered %>%
select(sra_accession, degron_treatment) %>%
column_to_rownames("sra_accession")
# Ensure counts columns match metadata rows
counts_wide <- counts_wide[, rownames(sample_meta)]
# Create DESeq2 dataset
dds <- DESeqDataSetFromMatrix(
countData = counts_wide,
colData = sample_meta,
design = ~ degron_treatment
)
# Run DESeq2
dds <- DESeq(dds)
# Get results
res <- results(dds)
# Return both the DESeq object and results
return(list(
dds = dds,
results = res %>%
as_tibble(rownames="target_locus_tag") %>%
mutate(sample_id = paste(unique(degron_meta_filtered$sample_id), collapse="_")) %>%
left_join(distinct(degron$counts_long %>% select(target_locus_tag, target_symbol))) %>%
mutate(regulator_locus_tag = unique(degron_meta_filtered$regulator_locus_tag),
regulator_symbol = unique(degron_meta_filtered$regulator_symbol),
env_condition = unique(degron_meta_filtered$env_condition),
timepoint = unique(degron_meta_filtered$timepoint)) %>%
dplyr::relocate(sample_id, regulator_locus_tag, regulator_symbol,
env_condition, timepoint),
metadata = degron_meta_filtered
))
}
baseline_control = list(
counts_long = arrow::read_parquet(
"~/code/hf/mahendrawada_2025/wt_baseline_counts.parquet"),
meta = arrow::read_parquet(
"~/code/hf/mahendrawada_2025/wt_baseline_counts_meta.parquet")
)
degron = list(
counts_long = arrow::read_parquet("~/code/hf/mahendrawada_2025/degron_counts.parquet"),
meta = arrow::read_parquet("~/code/hf/mahendrawada_2025/degron_counts_meta.parquet")
)
z = run_degron_deseq2("CYC8", 'standard_30C', 30, degron, baseline_control)
x = map(unique(degron$meta$regulator_symbol),
~run_degron_deseq2(., 'standard_30C', 30, degron, baseline_control))
results_df <- map_dfr(x, ~.$results)
# results_df %>%
# write_parquet("~/code/hf/mahendrawada_2025/rnaseq_reprocessed.parquet",
# compression = "zstd",
# write_statistics = TRUE,
# chunk_size = 6708,
# use_dictionary = c(
# sample_id = TRUE,
# regulator_locus_tag = TRUE,
# regulator_symbol = TRUE,
# target_locus_tag = TRUE,
# target_symbol = TRUE
# )
# )
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