Comments (1)
Hi Brian,
Thanks for your patience.
My advice would be to try the following:
- Ensure that your input data is loaded as float32 precision. This should reduce the memory footprint.
- If your research question permits, filter out genes that are not of interest. To my understanding, CELLECT only uses the protein coding genes, so you could try and reduce to these, but I would recommend you check with the CELLECT devs.
- Subsample cells from the cell groups of interest.
We are working on a version 2, which addresses the performance issues by e.g. using sparse matrices, but I expect it will not be ready for a few months.
I hope this answers your question and helps you to move on with your work.
Best,
Tobias
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Related Issues (20)
- .txt.gz files for "mapping"-functions are not found HOT 1
- Syntax Error in det.py file HOT 12
- Update parameter name in CELLEX wiki HOT 1
- Improve mapping documentation HOT 3
- Make tutorial for how to import data from H5ad/loom(/Seurat)
- Additional methods for gene filtering
- Warn if negative gene expression values
- parse_input does not detect invalid metadata value types HOT 5
- IndexError: tuple index out of range HOT 1
- gene mapping function: from symbol to ensembl HOT 3
- eso.results[βesmuβ] data.frame lacks index column name HOT 1
- [BUG]: MissingOutputException in line 200 of /home/wbone/CELLECT/cellect-ldsc.snakefile:
- Metadata_class use HOT 6
- [BUG]: Error saving summary data HOT 3
- Load input data as sparse matrix HOT 2
- [BUG]:Error running eso.compute HOT 2
- [BUG]: Operands could not be broadcast together with shapes (15831,12) (15831,15830,13) HOT 3
- [BUG]: eso.compute error. HOT 3
- np.float needs to be changed float due to deprecation in Numpy HOT 3
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