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Modular, efficient and constant-memory single-cell RNA-seq preprocessing
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DOI：10.1038/s41587-021-00870-2.png)
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Abstract
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We describe a workflow for preprocessing of single-cell RNA-sequencing data that balances efficiency and accuracy. Our workflow is based on the kallisto and bustools programs, and is near optimal in speed with a constant memory requirement providing scalability for arbitrarily large datasets. The workflow is modular, and we demonstrate its flexibility by showing how it can be used for RNA velocity analyses. A preprocessing workflow for single-cell RNA-seq data achieves near-optimal speed.
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