Convert very large sparse matrix to matrix (scRNA-seq analysis)

Question

[daianehemerich]

I'm using the Seurat object (scRNA-seq dataset) available on https://singlecell.broadinstitute.org/single_cell/study/SCP1376/a-single-cell-atlas-of-human-and-mouse-white-adipose-tissue (from https://www.nature.com/articles/s41586-022-04518-2). I extracted the UMI counts using: GetAssayData(object = adipocytes, slot = "counts") which returned a large R S4 dgCMatrix. It has everything I need, genes as row names, cells as column names, UMI counts as values. I'm trying to convert it to data frame to then use it as input on CELLEX, but because it's too big, I'm unable to convert it to data frame or matrix. Is there a way to convert this sparse matrix to data frame, keeping columns and row names?

Thanks in advance,

[Friederike]

Can you explain a bit more what cellex is and what type of input it expects and does with it?

[daianehemerich]

CELLEX (https://github.com/perslab/CELLEX) uses different metrics to estimate cell-type specificity profiles. The output of CELLEX is later used on CELLECT (https://github.com/perslab/CELLECT) to be integrated with GWAS summary statistics for cell-type prioritization. CELLEX uses as input a matrix of UMI counts (integer) for each gene and cell (genes are row names, cells are column names). Basically, it uses as input the exact information in the sparse matrix I was able to extract, just not in sparse matrix format.