通过空间基因制图构建单细胞生物学的视觉基础模型
A vision foundation model for single-cell biology via spatial gene cartography
浏览论文内容
中文总结 AI 辅助
研究提出scVision视觉基础模型，利用最优传输将基因布局成图像，通过掩码图像建模预训练视觉Transformer。该模型在零样本评估中表现出色，能无监督恢复基因程序，在多研究整合中效果好，还证明基因布局位置携带信号，重塑单细胞表示学习为视觉问题。
中文摘要 AI 辅助
大多数单细胞基础模型改编自语言模型，将每个细胞表示为基因令牌序列，这会丢弃基因间关系及其表达量。本文提出scVision，一种将每个细胞渲染为连续图像的视觉基础模型。利用最优传输将基因置于单个共享的全组织布局上的固定位置，使共表达基因成为空间邻居。通过对7200万个细胞进行掩码图像建模预训练视觉Transformer，在六项独立研究的零样本评估中，scVision是最准确的细胞类型注释器，能无监督恢复基因程序，在多研究整合中表现出色，证明有生物学意义的位置携带信号，并将单细胞表示学习重塑为视觉问题。
英文摘要
Most single-cell foundation models are adapted from language models, representing each cell as a sequence of gene tokens. This discards the relationships among genes and often the magnitude of their expression. We present scVision, a vision foundation model that instead renders each cell as a continuous image. Using optimal transport, it places genes at fixed positions on a single shared, pan-tissue layout so that co-expressed genes become spatial neighbours, turning a transcriptome into an image in which gene programs appear as local texture. We pretrain a vision transformer by masked image modelling on 72 million human cells and use the frozen encoder with no fine-tuning. In zero-shot evaluations on six independent, held-out studies, scVision is the most accurate cell-type annotator and recovers gene programs without supervision, ahead of existing foundation models and classical baselines; on multi-study integration it matches the strongest token-based model while conserving the most biological structure, without ever seeing a batch label. Permuting the gene layout with the network fixed sharply lowers accuracy, more than removing the vision transformer itself, showing that biologically meaningful position, not the network, carries the signal. By preserving expression magnitude and gene relationships, scVision reframes single-cell representation learning as a vision problem, connecting it to the mature methods of computer vision.
发表机构
- Stanford University（斯坦福大学）
- Stanford University School of Medicine（斯坦福大学医学院）
机构由 AI 辅助整理，请以论文原文为准。
