Nature BiotechnologyBioinformaticsPeer-reviewed journal article
Modular and efficient pre-processing of single-cell RNA-seq
Páll MelstediD, A. Sina BooeshaghiiD, Lauren LiuiD, Fan GaoiD, Lambda MosesiD, Kyung Hoi MiniD, Eduardo da Veiga BeltrameiD, Kristján Eldjárn HjörleifssoniD, Jase GehringiD, Lior S. PachteriD
Abstract
Analysis of single-cell RNA-seq data begins with pre-processing of sequencing reads to generate count matrices. We investigate algorithm choices for the challenges of pre-processing, and describe a workflow that balances efficiency and accuracy. Our workflow is based on the kallisto (https://pachterlab.github.io/kallisto/) and bustools (https://bustools.github.io/) programs, and is near-optimal in speed and memory. The workflow is modular, and we demonstrate its flexibility by showing how it can be used for RNA velocity analyses. Documentation and tutorials for using the kallisto | bus workflow are available at https://www.kallistobus.tools/.
- assay: scRNA-seq
- research focus: dimensionality reduction
- research focus: benchmarking
- research focus: software / tool
- approach: statistical model
- approach: experimental assay
- Citations
- 513
- FWCI
- 27.60
- Field percentile
- top 1%
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