SciBet as a portable and fast single cell type identifier

Chenwei Li, Baolin Liu, Boxi Kang, Zedao Liu, Yedan Liu, Changya Chen, Xianwen Ren () and Zemin Zhang ()
Additional contact information
Chenwei Li: Peking University
Baolin Liu: Peking University
Boxi Kang: Peking University
Zedao Liu: Peking University
Yedan Liu: Peking University
Changya Chen: Children’s Hospital of Philadelphia
Xianwen Ren: Peking University
Zemin Zhang: Peking University

Nature Communications, 2020, vol. 11, issue 1, 1-8

Abstract: Abstract Fast, robust and technology-independent computational methods are needed for supervised cell type annotation of single-cell RNA sequencing data. We present SciBet, a supervised cell type identifier that accurately predicts cell identity for newly sequenced cells with order-of-magnitude speed advantage. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. Facing the exponential growth in the size of single cell RNA datasets, this user-friendly and cross-platform tool can be widely useful for single cell type identification.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.nature.com/articles/s41467-020-15523-2 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15523-2

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-15523-2

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().