SHARP: hyperfast and accurate processing of single-cell RNA-seq data via ensemble random projection Article Swipe

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Shibiao Wan , Junil Kim , Kyoung‐Jae Won ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.1101/gr.254557.119 · OA: W3003444384

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.