Sctransform slow? Try these methods when sctransform taking too long to run.
Today, I wanted to mess around with some single-cell RNA sequencing data and figured I’d use the good old Seurat package in R. Everything was going smoothly until I hit the SCTransform step. I mean, this thing was taking forever to run! Seriously, I went to make a cup of coffee, came back, and it was still chugging along. I even had time to take a short nap! I’ve run it on smaller datasets before without a hitch, but this time it was like watching paint dry. So here’s a little recap of what I did and how I finally managed to speed things up.
Initial Setup and the Problem
First off, I loaded up my dataset, did the usual quality control, and got to the point where I needed to normalize the data. Normally, I’d just use NormalizeData, FindVariableFeatures, and ScaleData, but I decided to give SCTransform a shot since I heard it does a pretty good job. I ran it like this:
- seurat_object <- SCTransform(seurat_object, * = “*”, verbose = FALSE)
And then I waited… and waited. I started wondering if something was wrong. My dataset isn’t even that huge – only about 30,000 cells. I checked the forums and saw that other folks were having similar issues. Some even mentioned their process got killed because it took up too much memory. I get this warning:
- Warning in *(y = y…
But no one said it would stop my analysis anyway. But that’s no good for me to continue, right?
Trying to Speed Things Up
After digging around a bit more, I found a suggestion to use something called future_lapply for parallel processing. Sounded fancy, so I gave it a try. The idea is to split the data into chunks and process each chunk separately. I also noticed some warnings about memory usage, so I kept an eye on that too.
Here’s how I tweaked the code:
- Installed the future and packages if you don’t have them.
- Set up a plan for how to use multiple cores using plan(“multiprocess”, workers = 4). I have 4 cores on my machine, so I went with that.
- Used future_lapply instead of the regular lapply within the SCTransform function. The inner loop, though, I kept as a simple lapply.
Honestly, the code changes were a bit over my head at first, but I managed to get it working after some trial and error. The inner workings are complicated, but basically, it allows R to use multiple cores of your CPU to do the calculations, which should, in theory, speed things up.
The Results
Lo and behold, it actually worked! SCTransform finished in a reasonable amount of time. Instead of taking hours, it was done in under 30 minutes. I still saw some warnings, but nothing that seemed to break the process. My computer is not a potato, but it is not that good either. So I know if you are like me, this would help a bit. I also noticed the memory usage was more stable, which is a plus.
So, there you have it. If you’re pulling your hair out waiting for SCTransform to finish, give future_lapply a shot. It might just save you a ton of time and frustration. Just make sure you have the future and packages installed, and set up your plan to use multiple cores. It’s a bit of a learning curve, but totally worth it in the end. And hey, if I can figure it out, so can you!
Original article by the Author:yixunnet,If you intend to republish this content, please attribute the source accordingly:https://www.suntrekenergy.com/5405.html
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