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Genome-wide vs. single-cell #56
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genome-wide learns patterns over genes/rows, single-cell learns patterns over samples/columns. @genesofeve is this correct? |
It just changes how the data is split for parallel processing. If genome-wide the data is split into pieces that contain a group of genes and all of the cells/samples. If single-cell each split contains some of the cells and all of the genes. Generally, if you have more genes than cells/samples you should use genome-wide and vice versa. |
Hi, @jeanettejohnson @rossinerbe , |
From the vignette + documentation, it's not entirely clear to me what the difference between distributed = "genome-wide" and distributed = "single-cell" is. What is changing? I note that for the SeuratWrapper vignette (https://htmlpreview.github.io/?https://github.com/satijalab/seurat-wrappers/blob/master/docs/cogaps.html) "genome-wide" is used despite handling scRNA-seq data - is this correct?
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