NeuCA web server: a neural network-based cell annotation tool with web-app and GUI

Daoyu Duan; Sijia He; Emina Huang; Ziyi Li; Hao Feng

doi:10.1093/bioinformatics/btac108

NeuCA web server: a neural network-based cell annotation tool with web-app and GUI

Bioinformatics. 2022 Apr 12;38(8):2361-2363. doi: 10.1093/bioinformatics/btac108.

Authors

Daoyu Duan¹, Sijia He², Emina Huang³, Ziyi Li⁴, Hao Feng¹

Affiliations

¹ Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA.
² College of Arts and Sciences, Case Western Reserve University, Cleveland, OH 44106, USA.
³ Department of Surgery, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
⁴ Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

Abstract

Summary: Correctly annotating individual cell's type is an important initial step in single-cell RNA sequencing (scRNA-seq) data analysis. Here, we present NeuCA web server, a neural network-based scRNA-seq cell annotation tool with web-app portal and graphical user interface, for automatically assigning cell labels. NeuCA algorithm is accurate and exhaustive, maximizing the usage of measured cells for downstream analysis. NeuCA web server provides over 20 ready-to-use pre-trained classifiers for commonly used tissue types. As the first web-app tool with neural-network infrastructure implemented, NeuCA web will facilitate the research community in analyzing and annotating scRNA-seq data.

Availability and implementation: NeuCA web server is implemented with R Shiny application online at https://statbioinfo.shinyapps.io/NeuCA/.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Computers
Mobile Applications*
Neural Networks, Computer
Software*

Abstract

Publication types

MeSH terms

Grants and funding