Run.HiTME Freezing? Proven Solutions For Seurat Lists

by Pedro Alvarez 54 views

Hey everyone! Today, we're diving into a tricky problem that some of you might have encountered while using Run.HiTME, particularly when working with lists of Seurat objects. It's that frustrating moment when your code seems to freeze, and you're left wondering what went wrong. Let's break down the issue, explore the reasons behind it, and, most importantly, find some solutions.

The Run.HiTME Freezing Dilemma

So, the core issue is that Run.HiTME can sometimes get stuck when processing a list of Seurat objects. Imagine you're running the following code:

for (i in 1:length(obj.list)) {
 Run.HiTME(object = obj.list[[i]],
 scGate.model = scGate_models,
 ref.maps = ref.maps,
 verbose = FALSE,
 bparam = BiocParallel::MulticoreParam(workers = ncores,
 progressbar = TRUE,
 timeout = timeout))
}

You might find that the process gets stuck, often at different points – sometimes after just a few samples, other times after processing a larger batch. The culprit? It seems to be lurking within the scGate function, specifically during the second bplapply progress bar execution. Let's dig deeper into why this happens.

Decoding the scGate Bottleneck

The freezing often occurs in this snippet of code within scGate:

if (verbose) {
 message("### Running scGate\n")
}
object <- lapply(X = object, function(x) {
 x <- scGate::scGate(x, model = scGate.model, additional.signatures = c(additional.signatures,
 layer3), BPPARAM = param, multi.asNA = multi.asNA,
 verbose = verbose)
 names([email protected])[names([email protected]) == "scGate_multi"] <- "layer1"
 return(x)
})
message("Finished scGate\n#########################\n")

More precisely, the issue seems to stem from the bplapply call inside scGate:

preds <- bplapply(X = names(model), BPPARAM = BPPARAM, FUN = function(m) {
 col.id <- paste0(output.col.name, "_", m)
 x <- run_scGate_singlemodel(data, model = model[[m]],
 k.param = k.param, smooth.decay = smooth.decay,
 smooth.up.only = smooth.up.only, param_decay = param_decay,
 pca.dim = pca.dim, nfeatures = nfeatures, min.cells = min.cells,
 assay = assay, slot = slot, genes.blacklist = genes.blacklist,
 pos.thr = pos.thr, neg.thr = neg.thr, verbose = verbose,
 reduction = reduction, colname = col.id, save.levels = save.levels)
 n_pure <- sum(x[, col.id] == "Pure")
 frac.to.keep <- n_pure/nrow(x)
 mess <- sprintf("\n### Detected a total of %i pure '%s' cells (%.2f%% of total)",
 n_pure, m, 100 * frac.to.keep)
 message(mess)
 x
})

The progress bar typically gets stuck on the last worker, pointing to a potential problem within this function call. This bplapply function is designed to run computations in parallel, which is great for speeding things up, but it also means that if one worker gets hung up, the whole process can stall. Let's explore why this might be happening and what we can do about it.

Why Does This Happen?

Parallel processing, while powerful, can be susceptible to issues like deadlocks or resource contention. In this case, it appears that one of the workers in the bplapply might be getting stuck in a state where it's no longer responsive. This could be due to several reasons:

  • Resource Limits: The worker might be running out of memory or other resources, causing it to freeze.
  • Complex Computations: Certain combinations of data and model parameters might lead to computationally intensive operations that take an excessively long time to complete.
  • Underlying Bugs: There might be a bug in the code that causes a specific worker to get stuck under certain conditions.

Attempts to Solve the Issue (and Why They Didn't Fully Work)

Now, let's talk about some attempts to solve this problem and why they might not have been entirely successful. One common approach is to use the timeout parameter in BiocParallel::MulticoreParam. The idea is that if a worker takes too long, it should be timed out and the process should continue. However, as the user pointed out, this didn't seem to work for the bplapply within scGate. Why?

bparam = BiocParallel::MulticoreParam(workers = ncores,
 progressbar = TRUE,
 timeout = timeout)

While the timeout parameter works well in simpler scenarios, it might not always behave as expected in more complex, nested parallel computations. It's possible that the timeout is not being correctly propagated or handled within the scGate function's internal bplapply call.

Another approach the user tried was wrapping the code in a tryCatch block with withTimeout. This is a good strategy for handling errors and timeouts, but it has a limitation: if a worker gets stuck, it doesn't necessarily get shut down. This can lead to "zombie" processes lingering in the background, preventing subsequent runs of Run.HiTME from starting.

# Retry 5 times
for (a in 1:5) {
 cat(paste("Attempt", a, "to Run.HiTME ...\n"))
 result <- tryCatch({
 # The 'withTimeout' function will stop the execution after x seconds
 withTimeout({
 bplapply(X = 1:3, BPPARAM = MulticoreParam(workers = 3, progressbar = T, timeout = 3), FUN = function(m) {
 Sys.sleep(2)
 m+1
 })
 "Complete"
 }, timeout = 10)
 }, TimeoutException = function(te) {
 # This block runs if a timeout occurs
 cat(" Timeout occurred, retrying...\n")
 NULL # Return NULL to signify a failure
 }, error = function(er) {
 # This block runs if an error occurs
 cat(" Error occurred, retrying...\n")
 NULL # Return NULL to signify a failure
 })

 # If the result is not NULL, the function finished successfully
 if (!is.null(result)) {
 cat(paste(" Success on attempt", a, "with result:", result, "\n"))
 result <- NULL
 break # Exit the loop
 }
}

Promising Solutions to Thaw Run.HiTME

Okay, so we've identified the problem and the challenges in solving it. Now, let's explore some solutions that might actually work. Here are a few strategies you can try:

  1. Reduce the Number of Workers: Sometimes, the simplest solution is the most effective. Try reducing the number of workers used in BiocParallel::MulticoreParam. This can decrease the chances of resource contention and deadlocks. Instead of using all available cores, try using a smaller number, like half the available cores. This will reduce the stress on the system and potentially prevent workers from getting stuck. For example:

    bparam = BiocParallel::MulticoreParam(workers = floor(ncores/2), progressbar = TRUE)

  2. Implement a More Robust Timeout Mechanism: The built-in timeout parameter might not be sufficient. You can try implementing a more robust timeout mechanism using a combination of tryCatch, withTimeout, and process monitoring. This involves checking if the process is still running after a certain time and, if not, manually killing the process. This approach gives you more control over the timeout process and ensures that stuck workers are properly terminated.

    library(tools)

run_with_timeout <- function(expr, timeout_sec) {
  
  result <- tryCatch({
    
    withTimeout(expr, timeout = timeout_sec)
    
  }, TimeoutException = function(ex) {
    
    cat("  Timeout occurred.\n")
    
    return(NULL)
    
  }, error = function(e) {
    
    cat("  An error occurred: ", e$message, "\n")
    
    return(NULL)
    
  })
  
  return(result)
  
}


for (i in 1:length(obj.list)) {
  
  result <- run_with_timeout({
    
    Run.HiTME(object = obj.list[[i]],
              
              scGate.model = scGate_models,
              
              ref.maps = ref.maps,
              
              verbose = FALSE,
              
              bparam = BiocParallel::MulticoreParam(workers = ncores, progressbar = TRUE))
    
  }, timeout_sec = 600) # Timeout after 10 minutes
  
  
  if(is.null(result)){
    
    cat(paste0("Run.HiTME timed out for object ", i, "\n"))
    
  }
   
}

  3. Iterate and Clear Zombie Processes: If you suspect zombie processes are the issue, you can write a script to iterate through your list of Seurat objects, running Run.HiTME on each one. After each iteration, check for and kill any lingering zombie processes. You can use system commands like ps and kill to manage these processes. This ensures that your system is clean before the next iteration, preventing issues caused by previous stuck workers.

    # Function to kill zombie R processes
kill_zombie_processes <- function() {
  
  system("ps -aux | grep 'R' | grep defunct | awk '{print $2}' | xargs kill", ignore.stderr = TRUE)
  
}


for (i in 1:length(obj.list)) {
  
  cat(paste0("Running Run.HiTME for object ", i, "\n"))
  
  tryCatch({
    
    Run.HiTME(object = obj.list[[i]],
              
              scGate.model = scGate_models,
              
              ref.maps = ref.maps,
              
              verbose = FALSE,
              
              bparam = BiocParallel::MulticoreParam(workers = ncores, progressbar = TRUE))
    
  }, error = function(e) {
    
    cat("  An error occurred: ", e$message, "\n")
    
  })
  
  
  # Kill zombie processes after each iteration
  
  kill_zombie_processes()
  
}

  4. Divide and Conquer: Break your list of Seurat objects into smaller chunks and process them separately. This can help isolate the issue and prevent a single stuck worker from halting the entire process. By processing smaller batches, you reduce the complexity of each parallel operation and make it easier to identify and resolve any issues. If a particular chunk consistently gets stuck, you can focus your attention on those specific objects.

    chunk_size <- 5 # Process 5 objects at a time


for (i in seq(1, length(obj.list), by = chunk_size)) {
  
  cat(paste0("Processing chunk from ", i, " to ", min(i + chunk_size - 1, length(obj.list)), "\n"))
  
  
  chunk <- obj.list[i:min(i + chunk_size - 1, length(obj.list))]
  
  
  tryCatch({
    
    lapply(chunk, function(obj) {
      
      Run.HiTME(object = obj,
                
                scGate.model = scGate_models,
                
                ref.maps = ref.maps,
                
                verbose = FALSE,
                
                bparam = BiocParallel::MulticoreParam(workers = ncores, progressbar = TRUE))
      
    })
    
  }, error = function(e) {
    
    cat("  An error occurred: ", e$message, "\n")
    
  })
  
}

  5. Profile and Optimize scGate: If the issue persists, it might be worth profiling the scGate function to identify any performance bottlenecks. This can help you pinpoint specific areas where the code is slow or inefficient. You can use R's built-in profiling tools or specialized profiling packages to gather detailed performance data. Once you've identified the bottlenecks, you can optimize the code to improve its performance and reduce the likelihood of workers getting stuck.

  6. Update Packages: Ensure that you are using the latest versions of Run.HiTME, scGate, and BiocParallel. Sometimes, bugs are fixed in newer versions, so updating can resolve the issue. Regularly updating your packages ensures that you have the latest bug fixes, performance improvements, and features. This is a simple but often effective step in troubleshooting issues.

  7. Contact the Developers: If all else fails, reach out to the developers of Run.HiTME or scGate. They might be aware of the issue and have a fix or workaround. Providing them with detailed information about your setup and the specific conditions under which the problem occurs can help them diagnose and resolve the issue more effectively. Developers often appreciate feedback from users and can provide valuable insights.

Wrapping Up

The Run.HiTME freezing issue can be a real headache, but by understanding the potential causes and trying these solutions, you'll be better equipped to tackle it. Remember, parallel processing can be tricky, but with a bit of troubleshooting, you can get your analysis running smoothly again. And hey, if you find a solution that works particularly well for you, share it with the community! We're all in this together. Happy analyzing!