Benchmarking UMI-based single-cell RNA-seq preprocessing workflows.

ABSTRACT:

Background

Single-cell RNA-sequencing (scRNA-seq) technologies and associated analysis methods have rapidly developed in recent years. This includes preprocessing methods, which assign sequencing reads to genes to create count matrices for downstream analysis. While several packaged preprocessing workflows have been developed to provide users with convenient tools for handling this process, how they compare to one another and how they influence downstream analysis have not been well studied.

Results

Here, we systematically benchmark the performance of 10 end-to-end preprocessing workflows (Cell Ranger, Optimus, salmon alevin, alevin-fry, kallisto bustools, dropSeqPipe, scPipe, zUMIs, celseq2, and scruff) using datasets yielding different biological complexity levels generate

SUBMITTER: You Y 

PROVIDER: S-EPMC8672463 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature