Memory Issue while doing single cell seq analysis

Question

[cogen859]

Hi,

I am new to scrna seq analysis and other type of analyses on single cell data. My current dataset contains roughly 90K cells and 23K genes and I want to perform LR interaction analysis. I have been running into memory issues while running these analysis pipelines- SingleCellSignalR and Omnipath. I also wanted to test if my seurat cluster analysis is optimal, therefore decided to use R package-ChooseR. And again I ran into memory issues.

Machine Details: Azure Windows VM (64GB RAM and 128GB Temp Storage) and R 64bit version.

Please suggest any steps how I can overcome this type of memory issues. Thank you in advance for your help :)

Answer 1

[GenoMax]

If you are running out of memory then using a VM with more RAM is the only option.

Answer 2

[parashar.dhapola]

Hi!

Not a direct solution to your problem because it does not use the packages that you are already using. But if you (and others who ended on this post) are facing the RAM bottleneck when analyzing single-cell data, then Scarf might be useful.

Here is the Github link: https://github.com/parashardhapola/scarf

Documentation: https://scarf.readthedocs.io/

Paper: https://www.nature.com/articles/s41467-022-32097-3

[4galaxy77]

HI Parashar, thanks for the post - it looks like a nice piece of software. It's usually the done thing to let people know you are the author of a library/software when sharing it. Not accusing you of anything untoward, just letting you know :)