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library(Rsubread)
library(limma)
library(edgeR)
library(DESeq2)
library(magrittr)
library(ggrepel)
library(tidyverse)
library(vsn)
library(ggplot2)
library(NMF)
library(biomaRt)
library(ggpubr)
library(factoextra)
library(ggfortify)
library(plotly)
library(reshape2)
library(fgsea)
library(msigdbr)
library(data.table)
library(colorspace)
library(readxl)
library(fs)
library(betAS)
set.seed(1906)
library(data.table)
library(fs)
# --- STEP 1: Build the flat list of bindingvalues tables ---
build_bindingvalues_list <- function(
base_path = "~/Projects/StressGranules/AS.WC_Transcriptome/shRNAExp"
) {
base_path <- fs::path_expand(base_path)
subdirs <- fs::dir_ls(base_path, type = "directory", recurse = FALSE)
folder_names <- fs::path_file(subdirs)
out_list <- list()
for (folder in folder_names) {
folder_path <- fs::path(base_path, folder)
matches <- fs::dir_ls(folder_path, regexp = "BindingValues_IR_HEPG2\\.txt$", type = "file")
if (length(matches) == 0) {
message("No 'BindingValues_both.txt' file found in: ", folder_path)
next
}
if (length(matches) > 1) {
info <- fs::file_info(matches)
matches <- matches[order(info$modification_time, decreasing = TRUE)]
message("Multiple BindingValues_both.txt files in ", folder_path,
"; using latest: ", fs::path_file(matches[1]))
}
dt <- data.table::fread(matches[1])
out_list[[folder]] <- list(bindingvalues = dt)
}
return(out_list)
}
# --- STEP 2: Restructure into nested hierarchy ---
restructure_bindingvalues_full <- function(bindingvalues_list) {
out <- list()
for (rbp_name in names(bindingvalues_list)) {
df <- bindingvalues_list[[rbp_name]]$bindingvalues
if (is.null(df) || nrow(df) == 0) next
setDT(df)
# Group by Target, dPSI, Metric
nested_list <- df[, .(data = list(.SD)), by = .(Target, dPSI, Metric)]
# Split by Target
rbp_nested <- split(nested_list, by = "Target", keep.by = FALSE)
rbp_nested <- lapply(rbp_nested, function(target_dt) {
# Split by dPSI
target_split <- split(target_dt, by = "dPSI", keep.by = FALSE)
lapply(target_split, function(dpsi_dt) {
# Define metric groups
pval_metrics <- c("pvalinc", "pvaldec")
oddsrat_metrics <- c("oddsratinc", "oddsratdec")
raw_metrics <- c(
"IncreasedEvents", "DecreasedEvents", "MaintainedEvents",
"IncreasedTargetEvents", "TotalIncreasedEvents",
"MaintainedTargetEvents", "TotalMaintainedEvents",
"DecreasedTargetEvents", "TotalDecreasedEvents"
)
# Helper to clean each dataset
clean_data <- function(dt_list) {
if (length(dt_list) == 0) return(NULL)
lapply(dt_list, function(tbl) {
# Keep only relevant columns safely
if ("Pos" %in% names(tbl) && "Value" %in% names(tbl)) {
tbl[, .(Pos, Value)]
} else if ("ID" %in% names(tbl) && "Value" %in% names(tbl)) {
tbl[, .(ID, Value)]
} else if ("Value" %in% names(tbl)) {
tbl[, .(Value)]
} else {
tbl
}
})
}
# Extract and clean each group
pval_list <- clean_data(
setNames(dpsi_dt[Metric %in% pval_metrics]$data,
dpsi_dt[Metric %in% pval_metrics]$Metric)
)
oddsrat_list <- clean_data(
setNames(dpsi_dt[Metric %in% oddsrat_metrics]$data,
dpsi_dt[Metric %in% oddsrat_metrics]$Metric)
)
raw_list <- clean_data(
setNames(dpsi_dt[Metric %in% raw_metrics]$data,
dpsi_dt[Metric %in% raw_metrics]$Metric)
)
# Build hierarchical structure
list(
pval = if (length(pval_list)) pval_list else NULL,
oddsrat = if (length(oddsrat_list)) oddsrat_list else NULL,
raw = if (length(raw_list)) raw_list else NULL
)
})
})
out[[rbp_name]] <- rbp_nested
}
return(out)
}
# --- STEP 3: Example usage ---
CharmObj_bindingvalues <- build_bindingvalues_list()
# Example usage:
CharmObj_bindingvalues_nested <- restructure_bindingvalues_full(CharmObj_bindingvalues)
saveRDS(CharmObj_bindingvalues_nested, "~/Projects/CHARM/data/Binding_IR_HEPG2.RDS")