Understanding Gene Expression-to-Image Learning: Spatial Structure and Information Fidelity
Read the original on Hugging Face Trending Papers →The paper investigates how arranging single‑cell RNA sequencing data into images (Gene Expression‑to‑Image, GE2I) affects learning with convolutional neural networks. By keeping the network and training procedure constant, the authors uncover a trade‑off between spatial smoothness and gene‑level fidelity: structured mappings yield smooth, low‑frequency patterns that accelerate early optimization, while assigning multiple genes to the same pixel can obscure expression differences and hurt predictive performance. They show that one‑to‑one gene‑to‑pixel assignments improve predictions across various visual backbones, and that the benefits of pretraining and augmentation depend on both the backbone and the mapping strategy, offering practical guidance for pixel allocation and transfer learning in gene expression analysis.
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