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Nicheformer，基于 Transformer 的单细胞/ 空间组学 基础模型，首次高质量连接解离单细胞 ↔ 空间组学，让旧数据焕新。 该方法训练了 1.1 亿细胞 的 基础模型，把 空间组学 学到的“生态位信息”，精准迁移到 解离单细胞 上。简单地说，就是给海量 scRNA-seq 补上空间维度。 Nicheformer is the first foundation model to learn directly from spatial organization, offering a way to reconstruct how cells sense and influence their neighbors. 我们提出Nicheformer，这是一种基于大规模单细胞与空间转录组学数据预训练的基础模型，旨在实现数据有限场景下的空间依赖性预测任务。

This is the official repository for nicheformer This page describes the architecture and implementation details of the base nicheformer model The model uses masked language modeling to learn representations of cellular contexts and their relationships.

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