Digital Trials pipeline configured for WES
Source-only snapshot of the cluster branch for WES execution. Large reference files (HPA/MANE/ensemble FASTA, model weights, ~597 MB) are omitted: they are baked into the container images at build time and mounted from the dreamdock-data PVC at runtime, and exceed the Gitea request size limit. Pipeline entry point is main.nf, which orchestrates the biotransformer, conplex and tissue modules as a single workflow. Ligand inputs are read from the eureka workspace; protein_zarr and chembl_db come from the dreamdock-data PVC.
This commit is contained in:
Olamide Isreal
376
app_network/network2.r
Normal file
376
app_network/network2.r
Normal file
@@ -0,0 +1,376 @@
|
||||
#!/usr/bin/env Rscript
|
||||
#
|
||||
# Offline network enrichment and interaction analysis using local STRING-DB files.
|
||||
# Replicates the NETWORK_ENRICHMENT Nextflow process without internet connection.
|
||||
# R equivalent of the Python script using STRINGdb package.
|
||||
#
|
||||
|
||||
suppressPackageStartupMessages({
|
||||
library(STRINGdb)
|
||||
library(data.table)
|
||||
library(argparse)
|
||||
})
|
||||
|
||||
#' Load STRING-DB data from local files
|
||||
#'
|
||||
#' @param string_db_dir Directory containing STRING-DB files
|
||||
#' @return List containing mappings and data frames
|
||||
load_string_data <- function(string_db_dir) {
|
||||
cat("Loading STRING-DB files...\n")
|
||||
|
||||
# Load alias mapping (ENST -> ENSP)
|
||||
aliases <- fread(
|
||||
file.path(string_db_dir, "9606.protein.aliases.enst.v12.0.tsv"),
|
||||
sep = "\t",
|
||||
header = FALSE,
|
||||
col.names = c("string_protein_id", "alias", "source")
|
||||
)
|
||||
|
||||
enst_to_ensp <- aliases[source == "Ensembl_transcript",
|
||||
.(string_protein_id, alias)]
|
||||
setkey(enst_to_ensp, alias)
|
||||
|
||||
# Load protein info (for preferred names)
|
||||
info <- fread(
|
||||
file.path(string_db_dir, "9606.protein.info.v12.0.txt"),
|
||||
sep = "\t",
|
||||
header = FALSE,
|
||||
col.names = c("string_protein_id", "preferred_name", "protein_size", "annotation")
|
||||
)
|
||||
setkey(info, string_protein_id)
|
||||
|
||||
# Load protein links - USE DETAILED FILE
|
||||
links <- fread(
|
||||
file.path(string_db_dir, "9606.protein.links.detailed.v12.0.txt"),
|
||||
sep = " "
|
||||
)
|
||||
|
||||
# Load enrichment terms
|
||||
enrichment <- fread(
|
||||
file.path(string_db_dir, "9606.protein.enrichment.terms.v12.0.txt"),
|
||||
sep = "\t",
|
||||
header = FALSE,
|
||||
col.names = c("string_protein_id", "category", "term", "description")
|
||||
)
|
||||
|
||||
cat(sprintf("Loaded %d ENST->ENSP mappings\n", nrow(enst_to_ensp)))
|
||||
cat(sprintf("Loaded %d protein-protein interactions\n", nrow(links)))
|
||||
cat(sprintf("Loaded %d enrichment annotations\n", nrow(enrichment)))
|
||||
|
||||
list(
|
||||
enst_to_ensp = enst_to_ensp,
|
||||
ensp_to_name = info,
|
||||
links = links,
|
||||
enrichment = enrichment
|
||||
)
|
||||
}
|
||||
|
||||
#' Perform enrichment analysis using Fisher's exact test
|
||||
#'
|
||||
#' @param query_proteins Vector of ENSP protein IDs
|
||||
#' @param enrichment_df Data table with enrichment annotations
|
||||
#' @param ensp_to_name Data table mapping ENSP to preferred names
|
||||
#' @param ensp_to_input_map Named vector mapping ENSP back to ENST
|
||||
#' @return Data table with enrichment results
|
||||
perform_enrichment_analysis <- function(query_proteins, enrichment_df,
|
||||
ensp_to_name, ensp_to_input_map) {
|
||||
query_set <- unique(query_proteins)
|
||||
n_query <- length(query_set)
|
||||
|
||||
cat(sprintf("Analyzing enrichment for %d proteins...\n", n_query))
|
||||
|
||||
results_list <- list()
|
||||
|
||||
# Group by category
|
||||
categories <- unique(enrichment_df$category)
|
||||
|
||||
for (cat in categories) {
|
||||
cat_group <- enrichment_df[category == cat]
|
||||
|
||||
# Background: Total unique proteins annotated in this category
|
||||
category_universe <- unique(cat_group$string_protein_id)
|
||||
background_size <- length(category_universe)
|
||||
|
||||
# Iterate over terms within this category
|
||||
terms <- unique(cat_group$term)
|
||||
|
||||
for (tm in terms) {
|
||||
term_group <- cat_group[term == tm]
|
||||
term_proteins <- unique(term_group$string_protein_id)
|
||||
|
||||
# Intersection: proteins in both query and term
|
||||
intersection <- intersect(query_set, term_proteins)
|
||||
n_intersection <- length(intersection)
|
||||
|
||||
if (n_intersection == 0) next
|
||||
|
||||
# Background term count
|
||||
n_term_background <- length(term_proteins)
|
||||
|
||||
# Fisher's exact test
|
||||
# Contingency table:
|
||||
# In term Not in term
|
||||
# In query a b
|
||||
# Not in query c d
|
||||
|
||||
a <- n_intersection
|
||||
b <- n_query - a
|
||||
c <- n_term_background - a
|
||||
d <- background_size - n_term_background - b
|
||||
|
||||
# Ensure no negative values
|
||||
if (c < 0 || d < 0) next
|
||||
|
||||
# One-sided test for enrichment
|
||||
contingency_matrix <- matrix(c(a, b, c, d), nrow = 2, byrow = TRUE)
|
||||
fisher_result <- fisher.test(contingency_matrix, alternative = "greater")
|
||||
pvalue <- fisher_result$p.value
|
||||
|
||||
# Get description
|
||||
description <- term_group$description[1]
|
||||
|
||||
# Get input genes (map ENSP back to ENST input)
|
||||
intersection_sorted <- sort(intersection)
|
||||
|
||||
input_genes_list <- sapply(intersection_sorted, function(ensp) {
|
||||
if (ensp %in% names(ensp_to_input_map)) {
|
||||
ensp_to_input_map[ensp]
|
||||
} else {
|
||||
ensp
|
||||
}
|
||||
})
|
||||
|
||||
input_genes <- paste(input_genes_list, collapse = ",")
|
||||
|
||||
# Get preferred names
|
||||
preferred_names_list <- sapply(intersection_sorted, function(p) {
|
||||
name <- ensp_to_name[string_protein_id == p, preferred_name]
|
||||
if (length(name) > 0) name[1] else strsplit(p, "\\.")[[1]][length(strsplit(p, "\\.")[[1]])]
|
||||
})
|
||||
preferred_names <- paste(preferred_names_list, collapse = ",")
|
||||
|
||||
results_list[[length(results_list) + 1]] <- data.table(
|
||||
category = cat,
|
||||
term = tm,
|
||||
number_of_genes = n_intersection,
|
||||
number_of_genes_in_background = n_term_background,
|
||||
ncbiTaxonId = 9606,
|
||||
inputGenes = input_genes,
|
||||
preferredNames = preferred_names,
|
||||
p_value = pvalue,
|
||||
description = description
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
if (length(results_list) == 0) {
|
||||
return(data.table())
|
||||
}
|
||||
|
||||
# Combine results
|
||||
results_df <- rbindlist(results_list)
|
||||
|
||||
# Benjamini-Hochberg FDR correction
|
||||
results_df <- results_df[order(p_value)]
|
||||
n_tests <- nrow(results_df)
|
||||
results_df[, fdr := p_value * n_tests / seq_len(n_tests)]
|
||||
results_df[fdr > 1.0, fdr := 1.0]
|
||||
|
||||
# Reorder columns to match API output
|
||||
setcolorder(results_df, c(
|
||||
"category", "term", "number_of_genes", "number_of_genes_in_background",
|
||||
"ncbiTaxonId", "inputGenes", "preferredNames", "p_value", "fdr", "description"
|
||||
))
|
||||
|
||||
return(results_df)
|
||||
}
|
||||
|
||||
#' Extract network interactions for query proteins
|
||||
#'
|
||||
#' @param query_proteins Vector of ENSP protein IDs
|
||||
#' @param links_df Data table with protein-protein interactions
|
||||
#' @param ensp_to_name Data table mapping ENSP to preferred names
|
||||
#' @return Data table with network interactions
|
||||
get_network_interactions <- function(query_proteins, links_df, ensp_to_name) {
|
||||
query_set <- unique(query_proteins)
|
||||
|
||||
# Filter links where both proteins are in query set
|
||||
interactions <- links_df[protein1 %in% query_set & protein2 %in% query_set]
|
||||
|
||||
if (nrow(interactions) == 0) {
|
||||
return(data.table())
|
||||
}
|
||||
|
||||
# Normalize scores (STRING files are integers 0-1000, API is float 0-1)
|
||||
score_cols <- c("combined_score", "neighborhood", "fusion", "cooccurence",
|
||||
"coexpression", "experimental", "database", "textmining")
|
||||
target_cols <- c("score", "nscore", "fscore", "pscore",
|
||||
"ascore", "escore", "dscore", "tscore")
|
||||
|
||||
for (i in seq_along(score_cols)) {
|
||||
src <- score_cols[i]
|
||||
tgt <- target_cols[i]
|
||||
if (src %in% names(interactions)) {
|
||||
interactions[, (tgt) := get(src) / 1000.0]
|
||||
} else {
|
||||
interactions[, (tgt) := 0.0]
|
||||
}
|
||||
}
|
||||
|
||||
# Add preferred names
|
||||
interactions[, preferredName_A := sapply(protein1, function(p) {
|
||||
name <- ensp_to_name[string_protein_id == p, preferred_name]
|
||||
if (length(name) > 0) name[1] else strsplit(p, "\\.")[[1]][length(strsplit(p, "\\.")[[1]])]
|
||||
})]
|
||||
|
||||
interactions[, preferredName_B := sapply(protein2, function(p) {
|
||||
name <- ensp_to_name[string_protein_id == p, preferred_name]
|
||||
if (length(name) > 0) name[1] else strsplit(p, "\\.")[[1]][length(strsplit(p, "\\.")[[1]])]
|
||||
})]
|
||||
|
||||
# Rename columns to match STRING API output
|
||||
setnames(interactions, c("protein1", "protein2"), c("stringId_A", "stringId_B"))
|
||||
|
||||
interactions[, ncbiTaxonId := 9606]
|
||||
|
||||
# Select and order columns to match original output
|
||||
column_order <- c(
|
||||
"stringId_A", "stringId_B", "preferredName_A", "preferredName_B",
|
||||
"ncbiTaxonId", "score", "nscore", "fscore", "pscore",
|
||||
"ascore", "escore", "dscore", "tscore"
|
||||
)
|
||||
|
||||
interactions <- interactions[, ..column_order]
|
||||
|
||||
return(interactions)
|
||||
}
|
||||
|
||||
#' Main function
|
||||
main <- function() {
|
||||
# Parse command line arguments
|
||||
parser <- ArgumentParser(
|
||||
description = "Offline network enrichment analysis using local STRING-DB files"
|
||||
)
|
||||
|
||||
parser$add_argument(
|
||||
"interactions_file",
|
||||
type = "character",
|
||||
help = "Input TSV file with significant interactions (conplex output)"
|
||||
)
|
||||
|
||||
parser$add_argument(
|
||||
"--string-db-dir",
|
||||
type = "character",
|
||||
default = "/app",
|
||||
help = "Directory containing STRING-DB files"
|
||||
)
|
||||
|
||||
parser$add_argument(
|
||||
"--threshold",
|
||||
type = "double",
|
||||
default = 0.65,
|
||||
help = "ConPlex score threshold for protein network"
|
||||
)
|
||||
|
||||
parser$add_argument(
|
||||
"--max-proteins",
|
||||
type = "integer",
|
||||
default = 450L,
|
||||
help = "Maximum number of proteins to analyze"
|
||||
)
|
||||
|
||||
parser$add_argument(
|
||||
"--output-dir",
|
||||
type = "character",
|
||||
default = ".",
|
||||
help = "Output directory for results"
|
||||
)
|
||||
|
||||
args <- parser$parse_args()
|
||||
|
||||
# Load STRING-DB data
|
||||
string_data <- load_string_data(args$string_db_dir)
|
||||
|
||||
# Load interaction data
|
||||
cat(sprintf("\nLoading interaction data from %s...\n", args$interactions_file))
|
||||
interaction_data <- fread(args$interactions_file, sep = "\t")
|
||||
interaction_data <- interaction_data[order(-conplex_score)]
|
||||
|
||||
# Remove duplicates and limit
|
||||
# Replicates Nextflow logic: drops duplicates on transcript BEFORE limit
|
||||
interaction_data <- unique(interaction_data, by = "transcipt")
|
||||
|
||||
if (nrow(interaction_data) > args$max_proteins) {
|
||||
cat(sprintf("Limiting to top %d proteins\n", args$max_proteins))
|
||||
interaction_data <- interaction_data[1:args$max_proteins]
|
||||
}
|
||||
|
||||
# Filter by threshold
|
||||
filtered_data <- interaction_data[conplex_score > args$threshold]
|
||||
cat(sprintf("Found %d proteins above threshold %.2f\n",
|
||||
nrow(filtered_data), args$threshold))
|
||||
|
||||
# Map ENST to ENSP and keep track of mapping for output
|
||||
enst_list <- filtered_data$transcipt
|
||||
ensp_list <- character()
|
||||
ensp_to_input_map <- character()
|
||||
|
||||
for (enst in enst_list) {
|
||||
ensp <- string_data$enst_to_ensp[alias == enst, string_protein_id]
|
||||
if (length(ensp) > 0) {
|
||||
ensp <- ensp[1]
|
||||
ensp_list <- c(ensp_list, ensp)
|
||||
ensp_to_input_map[ensp] <- enst
|
||||
}
|
||||
}
|
||||
|
||||
cat(sprintf("Mapped %d ENST IDs to ENSP IDs\n", length(ensp_list)))
|
||||
|
||||
if (length(ensp_list) == 0) {
|
||||
stop("ERROR: No valid ENSP mappings found!")
|
||||
}
|
||||
|
||||
# Perform enrichment analysis
|
||||
cat("\nPerforming enrichment analysis...\n")
|
||||
enrichment_results <- perform_enrichment_analysis(
|
||||
ensp_list,
|
||||
string_data$enrichment,
|
||||
string_data$ensp_to_name,
|
||||
ensp_to_input_map
|
||||
)
|
||||
cat(sprintf("Found %d enriched terms\n", nrow(enrichment_results)))
|
||||
|
||||
# Get network interactions
|
||||
cat("\nExtracting network interactions...\n")
|
||||
network_interactions <- get_network_interactions(
|
||||
ensp_list,
|
||||
string_data$links,
|
||||
string_data$ensp_to_name
|
||||
)
|
||||
cat(sprintf("Found %d protein-protein interactions\n", nrow(network_interactions)))
|
||||
|
||||
# Generate output filename
|
||||
output_name <- gsub("_significant_interactions", "",
|
||||
tools::file_path_sans_ext(basename(args$interactions_file)))
|
||||
|
||||
# Create output directory if needed
|
||||
dir.create(args$output_dir, showWarnings = FALSE, recursive = TRUE)
|
||||
|
||||
# Save results
|
||||
enrichment_file <- file.path(args$output_dir,
|
||||
sprintf("%s_network_enrichment.tsv", output_name))
|
||||
interactions_file <- file.path(args$output_dir,
|
||||
sprintf("%s_network_interactions.tsv", output_name))
|
||||
|
||||
fwrite(enrichment_results, enrichment_file, sep = "\t")
|
||||
fwrite(network_interactions, interactions_file, sep = "\t")
|
||||
|
||||
cat("\nResults saved:\n")
|
||||
cat(sprintf(" Enrichment: %s\n", enrichment_file))
|
||||
cat(sprintf(" Interactions: %s\n", interactions_file))
|
||||
}
|
||||
|
||||
# Run main function
|
||||
if (!interactive()) {
|
||||
main()
|
||||
}
|
||||
Reference in New Issue
Block a user