利用异构图神经网络对 scRNA-seq 数据进行结构保留整合。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-09-23 DOI:10.1093/bib/bbae538
Xun Zhang, Kun Qian, Hongwei Li
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引用次数: 0

摘要

整合来自多个实验批次的单细胞 RNA 测序(scRNA-seq)数据能更全面地描述细胞状态。鉴于现有方法忽略了细胞和基因之间的结构信息,我们提出了一种使用异质图神经网络(scHetG)的结构保留scRNA-seq数据整合方法。通过建立代表多批细胞和基因之间相互作用的异质图,并将异质图神经网络与对比学习相结合,scHetG同时获得了具有结构信息的细胞和基因嵌入。对不同物种、组织和尺度的综合评估表明,scHetG 是一种有效的方法,既能消除批次效应,又能保留细胞和基因的结构信息,包括特定批次的细胞类型和特定细胞类型的基因共表达模式。
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Structure-preserved integration of scRNA-seq data using heterogeneous graph neural network.

The integration of single-cell RNA sequencing (scRNA-seq) data from multiple experimental batches enables more comprehensive characterizations of cell states. Given that existing methods disregard the structural information between cells and genes, we proposed a structure-preserved scRNA-seq data integration approach using heterogeneous graph neural network (scHetG). By establishing a heterogeneous graph that represents the interactions between multiple batches of cells and genes, and combining a heterogeneous graph neural network with contrastive learning, scHetG concurrently obtained cell and gene embeddings with structural information. A comprehensive assessment covering different species, tissues and scales indicated that scHetG is an efficacious method for eliminating batch effects while preserving the structural information of cells and genes, including batch-specific cell types and cell-type specific gene co-expression patterns.

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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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