鉴定(生物学)
计算机科学
基因调控网络
电池类型
图形
计算生物学
机制(生物学)
人工神经网络
人工智能
作者
Qijin Yin,Qiao Liu,Zhuoran Fu,Wanwen Zeng,Boheng Zhang,Xuegong Zhang,Rui Jiang,Hairong Lv
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2022-04-08
卷期号:38 (11): 2996-3003
标识
DOI:10.1093/bioinformatics/btac199
摘要
Single cell technologies play a crucial role in revolutionizing biological research over the past decade, which strengthens our understanding in cell differentiation, development, and regulation from a single-cell level perspective. Single-cell RNA sequencing (scRNA-seq) is one of the most common single cell technologies, which enables probing transcriptional states in thousands of cells in one experiment. Identification of cell types from scRNA-seq measurements is a fundamental and crucial question to answer. Most previous studies directly take gene expression as input while ignoring the comprehensive gene-gene interactions.We propose scGraph, an automatic cell identification algorithm leveraging gene interaction relationships to enhance the performance of the cell type identification. ScGraph is based on a graph neural network to aggregate the information of interacting genes. In a series of experiments, we demonstrate that scGraph is accurate and outperforms eight comparison methods in the task of cell type identification. Moreover, scGraph automatically learns the gene interaction relationships from biological data and the pathway enrichment analysis shows consistent findings with previous analysis, providing insights on the analysis of regulatory mechanism.scGraph is freely available at https://github.com/QijinYin/scGraph and https://figshare.com/articles/software/scGraph/17157743.Supplementary data are available at Bioinformatics online.
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