arXiv：cs.AI· Xiaoqing Lian, Pei Liu, Jiahui Tan, Quan Zou, Chen Lin, Xiangxiang Zeng·· 6 小时前
理解基因表达到图像学习：空间结构与信息保真度
Understanding Gene Expression-to-Image Learning: Spatial Structure and Information Fidelity
AI 导读
研究在固定网络与训练流程下，于全转录组数据上考察基因表达到图像（GE2I）学习机制，发现空间平滑性与基因级保真度之间存在权衡：结构化映射会产生平滑的低频模式、加速早期优化，但多个基因共享同一像素会模糊表达差异并可能降低预测性能。一对一基因到像素分配在测试的视觉骨干网络上均能提升预测表现，即使图像平滑度下降；预训练与数据增强的收益则取决于骨干网络与映射方式。
正文
Abstract:Single-cell RNA sequencing (scRNA-seq) enables cell-level analysis of biological heterogeneity, but learning effective representations from high-dimensional, unordered gene expression profiles remains challenging. Gene Expression-to-Image (GE2I) offers an effective learning strategy by arranging gene values on pixel grids for convolutional neural networks, but its underlying mechanisms remain unclear. We study GE2I on full-transcriptome data while keeping the network and training procedure fixed. Our results reveal a trade-off between spatial smoothness and gene-level fidelity. Structured mappings produce smooth, low-frequency patterns that support faster early optimization, whereas assigning multiple genes to the same pixel obscures expression differences and can reduce predictive performance. This trade-off explains why random mappings can compete with structured layouts when many genes are represented. One-to-one gene-to-pixel assignment improves prediction across the tested visual backbones even when image smoothness decreases. We further find that the benefits of pretraining and augmentation depend on the backbone and mapping. These findings provide practical guidance for pixel allocation and the transfer of vision priors to gene expression data.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Genomics (q-bio.GN) |
| Cite as: | arXiv:2610.17699 [cs.AI] |
| (or arXiv:2610.17699v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17699 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xiaoqing Lian [view email]
[v1]
Tue, 13 Oct 2026 04:26:16 UTC (5,782 KB)
来源：arXiv：cs.AI · arxiv.org