组织叙述者:用大型语言模型对空间转录学进行生成建模.
Sizhe Liu1, Junjie Tang1, Jian Ma1
1Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
bioRxiv : the preprint server for biology
|December 15, 2025
概括
组织叙述者使用语言建模来分析空间转录学数据,生成细胞概况和预测相互作用. 该框架增强了对多细胞系统中的组织组织和细胞通信的理解.
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:
- 空间转录学 (ST) 提供具有空间背景的基因表达数据,对于理解细胞相互作用至关重要.
- 目前ST的计算方法往往缺乏生成能力,并难以将生物知识纳入准确的解释.
- 模拟细胞行为和预测细胞间通信在现场仍然是一个挑战.
研究的目的:
- 介绍TissueNarrator,这是一个用于空间奥米克分析的新框架.
- 利用大型语言模型 (LLM) 来理解空间条件化的基因表达模式.
- 开发一种生成模型来模拟细胞行为和预测细胞间相互作用.
主要方法:
- 用基于等级的基因列表和空间坐标将组织部分表示为"空间句子".
- 应用预先训练的LLM来学习受空间环境影响的基因表达模式.
- 使用框架生成细胞概况,预测相互作用,并进行中扰动分析.
主要成果:
- 组织叙述者在各种ST技术 (MERFISH,Perturb-FISH,CosMx SMI) 中表现出卓越的定量性能.
- 该模型成功生成了现实的,对环境有意识的细胞概况,并预测了生物学上有意义的细胞间相互作用.
- 它确定了关键的体受体和信号通路,参与组织组织.
结论:
- 组织叙述者建立了一个可扩展的,用于空间奥米克数据分析的生成范式.
- 该框架将生物知识与空间背景相结合,使组织系统的先进建模和模拟成为可能.
- 一种对话推断模式允许自然语言查询组织组织,提高可访问性.
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