TissueFormer:一种通过分组单细胞RNA配置文件标记组织的神经网络
bioRxiv : the preprint server for biology
|September 2, 2025
概括
TissueFormer分析细胞组以预测样本级特征,与专注于单个细胞的方法不同. 这种新的方法提高了从空间转录数据绘制大脑区域的准确性.
科学领域:
- 计算生物学
- 基因组学
- 神经科学
背景情况:
- 单细胞RNA测序 (scRNA-seq) 提供了深入的基因表达洞察力,但目前的分析往往错过了关键的人口级信号.
- 解释scRNA-seq数据通常集中在单个细胞上,忽视了推断样本表型的细胞组成的重要性.
- 组织身份,疾病状态和其他样本级特征通常由样本内的细胞混合决定.
研究的目的:
- 介绍基于变压器的神经网络TissueFormer,旨在分析单细胞RNA配置文件的组.
- 在保持单细胞分辨率的同时,从细胞组成中推断出种群级标签.
- 为预测受细胞多样性和组织组织影响的样本级表型提供计算框架.
主要方法:
- 开发基于变压器的神经网络架构的TissueFormer.
- TissueFormer应用于来自小鼠大脑的空间转录数据.
- 将TissueFormer的性能与使用伪体和细胞类型组成数据的单细胞基础模型和传统机器学习方法进行比较.
主要成果:
- TissueFormer成功地预测了空间转录数据中的细胞组的皮质区域.
- 该模型的性能优于现有的单细胞基础模型和机器学习方法.
- 在单个小鼠中实现了高分辨率大脑区域图的自动构建.
结论:
- TissueFormer有效地利用细胞组成来准确预测人口水平的表型.
- 该框架推进了用于高分辨率绘制的空间转录数据的分析.
- 在细胞多样性至关重要的生物和临床研究中,
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