ENH: Memory Optimizations & low_memory Flag #324
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asistradition
requested review from
BorisMuzellec,
maikia and
umarteauowkin
as code owners
for more information, see https://pre-commit.ci
BorisMuzellec
requested changes
Oct 18, 2024
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Hi @asistradition, thanks a lot for this PR!
Overall I agree with the changes you propose, I just have a few comments regarding the storage and naming of ._cooks_outlier (cf above).
Just for the sake of curiosity, were you limited in your usage of PyDESeq2 because of memory consumption? If so, I'd be curious to have an idea of your setup and the size of your dataset :)
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Yes, this is part of my standard single-cell workflow, and memory limitations are the main bottleneck. 60k x 35k won't be a problem with this PR. |
Co-authored-by: Boris Muzellec <[email protected]>
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What does your PR implement? Be specific.
The
replace_cooksdata was stored as a pandas DataFrame. This is memory inefficient for large data. It has been refactored from a DataFrame to a numpy array.A new
low_memoryargument toDeseqDataSetis available. When set to True, large data arrays saved into the AnnData elements.obsmand.layersare deleted if there is no further use for them in the standard deseq workflow. This reduces peak memory consumption dramatically.