Your browser doesn't support javascript.
loading
HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data.
Zhang, Weihang; Cui, Yang; Liu, Bowen; Loza, Martin; Park, Sung-Joon; Nakai, Kenta.
Afiliação
  • Zhang W; Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan.
  • Cui Y; Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan.
  • Liu B; Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan.
  • Loza M; Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan.
  • Park SJ; Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan.
  • Nakai K; Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan.
Brief Bioinform ; 25(3)2024 Mar 27.
Article em En | MEDLINE | ID: mdl-38581422
ABSTRACT
Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approaches have been proposed for single-cell RNA sequencing (scRNA-seq) annotation, high-quality cell labels are still lacking in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) data, because of extreme sparsity and inconsistent chromatin accessibility between datasets. Here, we present a novel automated cell annotation method that transfers cell type information from a well-labeled scRNA-seq reference to an unlabeled scATAC-seq target, via a parallel graph neural network, in a semi-supervised manner. Unlike existing methods that utilize only gene expression or gene activity features, HyGAnno leverages genome-wide accessibility peak features to facilitate the training process. In addition, HyGAnno reconstructs a reference-target cell graph to detect cells with low prediction reliability, according to their specific graph connectivity patterns. HyGAnno was assessed across various datasets, showcasing its strengths in precise cell annotation, generating interpretable cell embeddings, robustness to noisy reference data and adaptability to tumor tissues.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Cromatina / Redes Neurais de Computação Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Cromatina / Redes Neurais de Computação Idioma: En Ano de publicação: 2024 Tipo de documento: Article