Fix Run.HiTME Stalling With Seurat Objects: A Troubleshooting Guide
Understanding the Problem
The Run.HiTME Stalling Issue: A Deep Dive
So, you're running Run.HiTME on a list of Seurat objects, and it just...stops. Frustrating, right? You might see it chugging along for a few samples, maybe 3-5, sometimes even 10-20, and then bam! It gets stuck. This often happens during the scGate process.
Here’s a snippet of the problematic 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))
}
The stall typically occurs within the scGate function, specifically in the second bplapply progress bar. This points to an issue in this section:
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")
The real culprit seems to be inside scGate, during this function call:
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
})
It’s common to see the progress bar getting stuck on the last worker, suggesting a potential deadlock or some other concurrency issue within the bplapply function. This is where things get tricky, as timeout mechanisms don't always behave as expected.
Why Timeouts Don't Always Work
You might think, "Okay, I'll just set a timeout!" and try something like this:
bparam = BiocParallel::MulticoreParam(workers = ncores,
progressbar = TRUE,
timeout = timeout)
But, surprise! It doesn’t always solve the problem. The built-in timeout parameter in BiocParallel sometimes fails to terminate the stuck worker processes, especially within nested parallel operations like the one in scGate.
To illustrate, consider these examples:
# This runs:
bplapply(X = 1:3, BPPARAM = MulticoreParam(workers = 3, progressbar = T, timeout = 3), FUN = function(m) {
Sys.sleep(2)
m+1
})
# This returns a timeout error:
bplapply(X = 1:3, BPPARAM = MulticoreParam(workers = 3, progressbar = T, timeout = 3), FUN = function(m) {
Sys.sleep(5)
m+1
})
While the timeout works in a simple, controlled scenario, it falls short when dealing with complex, nested parallel computations. This is a crucial distinction to keep in mind.
The Zombie Process Problem
So, what happens when a worker gets stuck? You might try wrapping the code in a tryCatch block with a withTimeout function, hoping to catch the timeout and retry.
for (a in 1:5) {
cat(paste("Attempt", a, "to Run.HiTME ...\n"))
result <- tryCatch({
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) {
cat(" Timeout occurred, retrying...\n")
NULL
}, error = function(er) {
cat(" Error occurred, retrying...\n")
NULL
})
if (!is.null(result)) {
cat(paste(" Success on attempt", a, "with result:", result, "\n"))
result <- NULL
break
}
}
This does timeout, but here’s the catch: the stuck worker process doesn’t get shut down. It becomes a zombie process, lingering in the background. If you try to run Run.HiTME again, scGate won’t even start because bplapply can’t initialize properly with the zombie process still active. You’ll see these zombie processes hanging around in your activity monitor, refusing to go away.
Potential Solutions and Workarounds
Okay, so we've identified the problem. Now, let’s talk solutions. Dealing with these kinds of issues often involves a bit of detective work and trying different strategies. Here are a few approaches you can try:
1. Reduce the Number of Workers
Sometimes, the simplest solution is the best. Overloading the system with too many parallel processes can lead to deadlocks or other concurrency issues. Try reducing the ncores parameter to a smaller number. For example, if you were using 8 cores, try 4 or even 2. This can reduce the strain on the system and prevent the stall.
2. Implement a More Robust Timeout Mechanism
The built-in timeout in BiocParallel isn't always reliable, as we've seen. You might need to implement a more robust timeout mechanism. One approach is to use a combination of tryCatch, withTimeout (from the R.utils package), and manual process management.
Here’s a refined version of the earlier timeout code:
library(R.utils)
library(BiocParallel)
run_hitme_with_timeout <- function(obj, scGate_models, ref.maps, ncores, timeout_val = 600) {
result <- tryCatch({
withTimeout({
Run.HiTME(object = obj,
scGate.model = scGate_models,
ref.maps = ref.maps,
verbose = FALSE,
bparam = MulticoreParam(workers = ncores, progressbar = TRUE))
}, timeout = timeout_val)
}, TimeoutException = function(te) {
cat(" Timeout occurred.\n")
# Add code here to kill zombie processes if necessary
return(NULL)
}, error = function(er) {
cat(" Error occurred: ", er$message, "\n")
return(NULL)
})
return(result)
}
# Example usage
obj.list <- list(obj1, obj2, obj3) # Replace with your actual list of Seurat objects
ncores <- 4 # Or whatever number of cores you're using
for (i in 1:length(obj.list)) {
cat(paste("Processing object", i, "...\n"))
result <- run_hitme_with_timeout(obj = obj.list[[i]], scGate_models = scGate_models, ref.maps = ref.maps, ncores = ncores)
if (is.null(result)) {
cat(paste(" Failed to process object", i, "after timeout or error.\n"))
# Handle failure, e.g., retry, skip, or exit
} else {
cat(paste(" Successfully processed object", i, "\n"))
}
}
This code wraps the Run.HiTME call in a withTimeout block. If a timeout occurs, it catches the TimeoutException. Important: You'll need to add code within the TimeoutException block to identify and kill any zombie processes. This typically involves listing active processes and killing the ones associated with the stalled bplapply call.
3. Process Objects Sequentially
Okay, this might sound like a step backward, but hear me out. If parallel processing is consistently causing issues, sometimes the most reliable solution is to process each Seurat object sequentially. This eliminates the concurrency issues altogether.
for (i in 1:length(obj.list)) {
tryCatch({
Run.HiTME(object = obj.list[[i]],
scGate.model = scGate_models,
ref.maps = ref.maps,
verbose = FALSE,
bparam = BiocParallel::SerialParam())
}, error = function(e) {
cat("Error processing object ", i, ": ", e$message, "\n")
# Handle the error, e.g., skip the object or stop the loop
})
}
By using BiocParallel::SerialParam(), you force Run.HiTME to run each object one after the other. This will be slower, but it’s often more stable. Plus, the tryCatch block ensures that if one object fails, the loop can continue with the next one.
4. Investigate System Resources
Sometimes, stalls can be due to resource limitations. Check your system's CPU, memory, and disk I/O usage while Run.HiTME is running. If you're maxing out any of these resources, it could lead to stalls. You might need to:
- Increase system memory (RAM).
- Use faster storage (e.g., SSD instead of HDD).
- Close other resource-intensive applications.
5. Optimize scGate Parameters
The scGate function itself has several parameters that can affect its performance. Experiment with these parameters to see if you can reduce the computational load. For example, you might try:
- Reducing the
k.paramvalue (number of nearest neighbors). - Adjusting
pca.dim(number of PCA dimensions). - Filtering genes or cells before running
scGate.
6. Debugging Inside scGate
If you're comfortable diving into the scGate code, you can add debugging statements or use a debugger to pinpoint exactly where the stall is occurring. This might give you clues about the specific conditions that trigger the issue.
For example, you could add print statements inside the bplapply loop in scGate to track the progress of each worker:
preds <- bplapply(X = names(model), BPPARAM = BPPARAM, FUN = function(m) {
cat("Starting model ", m, "\n") # Debugging statement
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)
cat("Finished model ", m, "\n") # Debugging statement
x
})
Conclusion
Dealing with stalled processes in R can be a headache, but with a systematic approach, you can often find a solution. Remember, the key is to:
- Understand the problem: Know where the stall is occurring.
- Try simple solutions first: Reduce workers, process sequentially.
- Implement robust timeouts: Don’t rely solely on built-in timeouts.
- Monitor resources: Ensure your system isn’t being overloaded.
Hopefully, these tips help you get your Run.HiTME process running smoothly! If you have any other tricks or solutions, feel free to share them in the comments below. Happy analyzing!
