Work

SHARP: Single-cell RNA-seq Hyper-fast and Accurate Processing via Ensemble Random Projection

Year: 2018

Type: preprint

Abstract: 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 which is scalable to clustering 10 million cells. Comprehensive benchmarking tests on 17 public scRNA-seq datasets demonstrate that SHARP outperforms existing methods in terms of speed and accuracy. Particularly, for large-size datasets (>40,000 cells), SHARP’s running speed far excels 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.

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.1101/461640

Authors: Shibiao Wan, Junil Kim, Kyoung Jae Won

Institutions: St. Jude Children's Research Hospital, University of Pennsylvania, University of Copenhagen, Novo Nordisk Foundation

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: Novo Nordisk, Novo Nordisk Fonden, National Institutes of Health

Awards: G3589578894, G7044228676, G9000248900