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CSRefiner:用于微调小数据集的细胞细分模型的轻量级框架

Can Shi^1,2,3, Yumei Li^1,2, Jing Guo^1,2

¹State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, 518083, China.

Briefings in bioinformatics

|January 13, 2026

概括

CSRefiner通过提高单细胞细分精度来增强整个组织分析的空间奥米克. 这个框架提供了一个轻量级的解决方案,用于精确的空间基因表达数据,最小的训练数据.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
细胞生物学细胞生物学

背景情况:

空间奥米克技术使得以亚细胞分辨率进行转录组分析.
深度学习模型提供了高准确度,但与全组织分析和多样化的细胞群体作斗争.
当前的微调方法往往资源密集,缺乏适应性.

研究的目的:

推出CSRefiner,一种用于精确整组织单细胞空间表达分析的新型微调框架.
为解决目前基于深度学习的空间转录组学的细胞细分的局限性.
为实际应用提供可扩展和适应的解决方案.

主要方法:

开发了CSRefiner,一个轻量级和高效的微调框架.
集成支持微调空间奥米克斯中广泛使用的细分模型.
在多种染色类型和多种主流模型中评估性能.

主要成果:

在有限的注释数据的基础上,CSRefiner在整个组织单细胞细分方面实现了高精度.
在各种染色类型中表现出卓越的性能.
展示了与多个主流细分模型的兼容性.

结论:

CSRefiner为现实世界的空间转录学提供了一种实用且强大的解决方案.
该框架将操作简单性与高精度相结合,用于精确的空间基因表达分析.
从亚细胞分辨率数据中实现更可靠的生物解释.

关键词:

细胞细分细胞细分细胞细分精细调整精细调整空间的奥米克.

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