这篇文章介绍了 Nicheformer 模型的开发和评估，这是一个用于单细胞和空间生物学的基础深度学习模型。该研究强调了单细胞RNA测序（scRNA-seq）在细胞解离过程中丢失空间信息的问题，并通过利用空间组学数据 来解决这一问题。Nicheformer 使用了一个包含超过 1.1 亿个细胞的SpatialCorpus-110M数据集进行预训练，该数据集整合了解离式和图像式空间转录组学数据。通过使用Transformer架构和掩码语言建模损失 进行训练，Nicheformer 展示了在各种下游任务中的卓越性能，包括预测细胞类型、细胞生态位标签、细胞邻域组成和细胞密度，特别是与不考虑空间信息的基线模型相比。研究结果表明，模型能够捕获微妙的空间信息和生物学变异，例如性别相关的模式，从而实现跨模态的标签迁移。References: Schaar A C, Tejada-Lapuerta A, Palla G, et al. Nicheformer: a foundation model for single-cell and spatial omics[J]. bioRxiv, 2024: 2024.04. 15.589472.
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