Sign in
BioinformaticsNature Communications
Peer-reviewed journal article

SciBet: a portable and fast single cell type identifier

Chenwei LiiD, Baolin LiuiD, Boxi KangiD, Zedao Liu, Yedan LiuiD, Changya CheniD, Xianwen ReniD, Zemin ZhangiD

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 Bayesian classifier that accurately predicts cell identity for newly sequenced cells or cell clusters. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. This user-friendly and cross-platform tool can be widely useful for single cell type identification.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers
Citations
177
FWCI
6.81
Field percentile
top 2%

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISNguyen '20Tan '19Lin '20Abdelaal '19Çobanoğlu '20Ferrari '25
70% semantic matchBioinformatics
Vy Nguyen, Johannes Griss·bioRxiv·Dec 22, 2020· 2 citations