NeuCA: A Neural Network-Based Cell Annotation Method with Web-App and GUI

• Activity Number: 65 - Computational Challenges in Software and Algorithms
• Type: Contributed
• Date/Time: Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
• Sponsor: Section on Statistical Computing
• Author(s): Ziyi Li and Hao Feng*
• Companies: The University of Texas MD Anderson Cancer Center and Case Western Reserve University
• Keywords: Gene expression; scRNA-seq; neural network; hierarchical tree; bioinformatics; computational biology

Abstract

The initial important step of analyzing scRNA-seq data is usually to accurately annotate cells. Taking advantage of the numerous existing scRNA-seq databases, many supervised label assignment methods have been developed. However, existing tools produce unignorable amount of “unassigned” cells and suffer from low accuracy among correlated cell types. Here, we present a neural network-based cell annotation method called NeuCA (Neural network-based Cell Annotation). NeuCA can utilize the hierarchical structure information of the cell types to improve the annotation accuracy. It can achieve more accurate and exhaustive cell annotation results compared with existing methods. Additionally, the applications on eight real datasets show that NeuCA has stable performance for intra- and inter-study annotation, as well as cross-condition annotation. NeuCA is freely available as an R/Bioconductor package at https://bioconductor.org/packages/NeuCA. It is also available as a stand-along web-app at https://statbioinfo.shinyapps.io/NeuCA/, with neural-network infrastructure embedded and over 20 pre-trained, ready-to-use classifiers from various tissue types.