用图像知识增强基因表达数据，提升生物分析精度
A Cross Modal Knowledge Distillation & Data Augmentation Recipe for Improving Transcriptomics Representations through Morphological Features
- 从图像中蒸馏形态特征，融合到基因表达数据中
- 在弱配对数据上实现跨模态对齐，提升预测能力
- 适合需要可解释性的生物医学研究者
理解细胞对刺激的响应对生物发现和药物开发至关重要。转录组学提供可解释的基因层面洞察，而显微成像则蕴含丰富预测特征但难以解读。弱配对数据（样本共享生物学状态）支持多模态学习，但此类数据稀缺，限制了其训练与推理应用。本文提出一种框架，通过显微图像知识蒸馏增强转录组学表征。利用弱配对数据，方法实现模态对齐与绑定，将形态信息融入基因表达表示。为应对数据稀缺，提出（1）Semi-Clipped：基于预训练基础模型改进的CLIP适配器，用于跨模态蒸馏，达到当前最优性能；（2）PEA（Perturbation Embedding Augmentation）：一种新型数据增强技术，在保留生物学信息的前提下增强转录组数据。上述策略提升了转录组学的预测能力并保持可解释性，实现复杂生物任务下的高维单模态表征。
原文摘要 · Abstract (English)
Understanding cellular responses to stimuli is crucial for biological discovery and drug development. Transcriptomics provides interpretable, gene-level insights, while microscopy imaging offers rich predictive features but is harder to interpret. Weakly paired datasets, where samples share biological states, enable multimodal learning but are scarce, limiting their utility for training and multimodal inference. We propose a framework to enhance transcriptomics by distilling knowledge from microscopy images. Using weakly paired data, our method aligns and binds modalities, enriching gene expression representations with morphological information. To address data scarcity, we introduce (1) Semi-Clipped, an adaptation of CLIP for cross-modal distillation using pretrained foundation models, achieving state-of-the-art results, and (2) PEA (Perturbation Embedding Augmentation), a novel augmentation technique that enhances transcriptomics data while preserving inherent biological information. These strategies improve the predictive power and retain the interpretability of transcriptomics, enabling rich unimodal representations for complex biological tasks.
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