用对抗微调让基础模型跨模态翻译单细胞数据，无需配对样本。
Single-Cell Cross-Modal Transfer by Adversarial Fine-Tuning of Foundation Models
- 通过对抗微调使单细胞基础模型学习未配对的ST与scRNA-seq数据映射。
- 在无配对数据条件下实现优于传统多组学翻译方法的跨模态重构。
- 适合生物信息学研究者探索组织空间结构与基因表达关系。
空间转录组学（ST）是探索组织中依赖结构、邻近性和相互作用的生物学特性的强大工具。尽管其技术发展迅速，但在亚细胞尺度上同时检测数千个基因的能力仍受限。虽然单细胞RNA测序（scRNA-seq）中的细胞已脱离组织，但其全转录组数据仍保留了原位邻居信息，这激发了计算方法恢复这些空间线索。尽管配对的ST与scRNA-seq数据稀缺，但各自模态的数据均大量可用。因此，我们提出在无配对的ST与scRNA-seq数据间进行跨模态转换。本研究证明，单细胞基础模型可通过对抗微调实现该转换，并在性能上优于专为多组学转换设计的方法。
原文摘要 · Abstract (English)
Spatial transcriptomics (ST) is a powerful tool for exploring biological properties dependent on structure, proximity, and interaction in tissue. The methods underpinning ST are developing rapidly but are limited in their ability to profile many thousands of genes at a subcellular scale. Although dissociated from tissue, it is known that the whole-transcriptome readouts of cells in single-cell RNA sequencing (scRNA-seq) retain information about their former in situ neighbourhoods, motivating computational methods to recover it. While paired ST and scRNA-seq datasets are scarce, each modality in its own right is abundantly available. We therefore propose to perform cross-modal translation between unpaired ST and scRNA-seq data. In this work we show that a single-cell foundation model can perform this translation via adversarial fine-tuning. We demonstrate that our method performs favourably against methods built for multi-omics translation.
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