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

ABSTRACT: 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.

SUBMITTER: Wan S

PROVIDER: S-EPMC7050522 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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

Wan Shibiao S Kim Junil J Won Kyoung Jae KJ

Genome research 20200128 2

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 wh ...[more]

PMID: 31992615

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