Journal
GENOME RESEARCH
Volume 30, Issue 2, Pages 205-213Publisher
COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT
DOI: 10.1101/gr.254557.119
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Funding
- National Institutes of Health (National Institute of Diabetes and Digestive and Kidney Diseases) [R01 DK106027]
- Novo Nordisk Foundation [NNF17CC0027852]
- Lundbeck Foundation [R313-2019-421]
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To process large-scale single-cell RNA-sequencing (scRNA-seq) data effectively without excessive distortion during dimension reduction, we present SHARP, an ensemble random projection-based algorithm that is scalable to clustering 10 million cells. Comprehensive benchmarking tests on 17 public scRNA-seq data sets show that SHARP outperforms existing methods in terms of speed and accuracy. Particularly, for large-size data sets (more than 40,000 cells), SHARP runs faster than other competitors while maintaining high clustering accuracy and robustness. To the best of our knowledge, SHARP is the only R-based tool that is scalable to clustering scRNA-seq data with 10 million cells.
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