AI 中文总结
研究针对单细胞数据独特表格结构及隐私问题，提出用联邦学习设计的Tabula模型，开发Chiron平台。该模型在下游基准测试表现出色，揭示调控逻辑，提名年轻化因子，推动单细胞基础建模发展，迈向隐私保护虚拟细胞。
AI 中文摘要
预训练基础模型（FMs）已开始改变单细胞基因组学，但扩展它们会引发隐私问题。此外，与文本数据不同，单细胞数据无序且具有独特的表格结构，当前的单细胞FMs忽略了这一点。我们引入了Tabula，一种通过联邦学习（FL）设计的隐私保护FM，它明确地对单细胞数据的表格结构进行建模。为了部署Tabula，我们进一步开发了Chiron，一个支持去中心化人工智能代理的平台，用于跨机构协作训练而不共享原始数据。除了在下游基准测试中表现出色外，Tabula还揭示了跨多种生物系统的组合调控逻辑。使用新的成对年轻和老年人类成纤维细胞的scRNA-seq数据集，Tabula通过年龄和身份评分引导的计算机优先排序提名了年轻化因子，优于传统方法。因此，Tabula通过将表格学习与FL相结合，代表了单细胞基础建模的重要进展，为人类健康的隐私保护虚拟细胞铺平了道路。
英文摘要
Pre-trained foundation models (FMs) have begun transforming single-cell genomics, but scaling them raises privacy concerns. Moreover, unlike text data, single-cell data is unordered and exhibits a unique tabular structure that current single-cell FMs overlook. We introduce Tabula, a privacy-preserving FM designed with federated learning (FL) that explicitly models the tabular structure of single-cell data. To deploy Tabula, we further developed Chiron, a decentralized AI agent-enabled platform for collaborative training across institutions without sharing raw data. Beyond strong performance across downstream benchmarks, Tabula reveals combinatorial regulatory logic across diverse biological systems, including hematopoiesis, pancreatic endogenesis, neurogenesis, and cardiogenesis. Using a new scRNA-seq dataset of paired young and aged human fibroblasts, Tabula nominates rejuvenation factors through age- and identity score-guided in silico prioritization, outperforming conventional approaches. Thus, Tabula represents an important advance in single-cell foundation modeling by integrating tabular learning with FL, paving the way toward privacy-preserving virtual cells for human health.
Comments98 pages, 4 main figures, and 15 supplementary figures
