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GatorSC:多尺度细胞和基因图形与混合专家融合为单细胞转录组学

Yuxi Liu¹, Zhenhao Zhang², Mufan Qiu³

¹Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, 46202, IN, USA.

bioRxiv : the preprint server for biology

|December 18, 2025

概括

GatorSC是单细胞RNA测序 (scRNA-seq) 数据分析的新框架. 它有效地融合了多尺度的细胞和基因图,提供了强大的,耐噪声的,低维的表示,改善了下游任务,如细胞聚类和注释.

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

单细胞RNA测序 (scRNA-seq) 提供了高分辨率的细胞异质性见解.
现有的方法未充分利用scRNA-seq数据中的丰富结构信息,特别是由于噪音和稀疏性.
整合基于细胞和基因的异质图形视图对于强大的低维表示至关重要.

研究的目的:

介绍GatorSC,一个用于scRNA-seq数据的统一表示学习框架.
利用多尺度的细胞和基因图表来增强信息融合.
使用自主监督学习开发耐噪声和结构保护嵌入式.

主要方法:

GatorSC使用全球细胞-细胞,全球基因-基因和本地基因-基因图形来建模scRNA-seq数据.
混合专家架构通过一个门网通过自适应性融合图形神经网络嵌入.
一个统一的自我监督目标结合了图形重建和对比学习,用于细胞和基因图形.

主要成果:

在19个不同的scRNA-seq数据集上评估了GatorSC.
在14个基准数据集中,在细胞聚类,基因表达赋值和细胞类型注释方面超越了最先进的方法.
在阿尔茨海默病数据集中证明了准确的轨迹推断和生物特征的恢复.

结论:

GatorSC为全面的单细胞转录组分析提供了灵活而强大的基础.
该框架有效地整合了多尺度图形结构,以实现强大的表示学习.
GatorSC的方法可以扩展到多原子和空间转录原子数据.

关键词:

细胞聚类是细胞的聚类.细胞类型的注释.相反的学习学习.基因表达的归因基因表达的归因专家的混合-专家的混合在 scRNA-seq 数据中.

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