Literature DB >> 35176143

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

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

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: 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.
© The Author(s) (2022). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Year:  2022        PMID: 35176143      PMCID: PMC9004646          DOI: 10.1093/bioinformatics/btac108

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  3 in total

1.  CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing.

Authors:  Jurrian K de Kanter; Philip Lijnzaad; Tito Candelli; Thanasis Margaritis; Frank C P Holstege
Journal:  Nucleic Acids Res       Date:  2019-09-19       Impact factor: 16.971

2.  scmap: projection of single-cell RNA-seq data across data sets.

Authors:  Vladimir Yu Kiselev; Andrew Yiu; Martin Hemberg
Journal:  Nat Methods       Date:  2018-04-02       Impact factor: 28.547

3.  A neural network-based method for exhaustive cell label assignment using single cell RNA-seq data.

Authors:  Ziyi Li; Hao Feng
Journal:  Sci Rep       Date:  2022-01-18       Impact factor: 4.996

  3 in total

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