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sciLaMA:一个单细胞表示学习框架,利用来自大型语言模型的先前知识

Hongru Hu, Shuwen Zhang, Yongin Choi

bioRxiv : the preprint server for biology

|June 12, 2025

概括

我们开发了sciL-aMA,这是一种新的框架,将大型语言模型与单细胞RNA测序数据集成在一起. 这种方法增强了细胞分析,提高了基因发现和数据解释的效率.

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洞察力,但面临着分析挑战.
现有的深度学习模型难以整合生物知识或有效地处理表格式基因表达数据.
大型语言模型 (LLM) 对scRNA-seq数据存在计算和适用性限制.

研究的目的:

引入sciL-aMA,这是一个用于scRNA-seq数据分析中的表示学习的新框架.
通过将基因嵌入与基因表达数据集成,弥合特定任务模型和LLM之间的差距.
为单细胞数据分析和基因模块发现提供计算高效和可解释的方法.

主要方法:

开发了sciL-aMA,这是一个结合多式LLM基因嵌入与scRNA-seq数据的框架.
使用配对变量自动编码器 (VAE) 架构进行集成表示学习.
为细胞和基因生成了上下文意识的表示.

主要成果:

在关键下游 scRNA-seq 任务中,sciL-aMA 的性能优于最先进的方法.
在批量效应校正和细胞聚类方面表现出卓越的性能.
实现了细胞状态特异性基因标记物和模块的有效识别.

结论:

sciL-aMA为全面的单细胞数据分析提供了一个计算效率高,统一的框架.
该模型使生物可解释的基因模块发现成为可能.
这种方法提高了LLMs在分析scRNA-seq数据中的实用性.

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