An Integrated Deep Learning and Statistical Framework for Whole-Network Gene—Environment Association with Leaf Vascular Architecture

一种用于叶片脉络结构全网络基因-环境关联的深度学习与统计集成框架

Leaf veins exhibit remarkable diversity in architecture and patterning, yet existing gene—environment association studies have primarily quantified leaf venation using a small collection of low-dimensional summary traits, thereby discarding most of the structural information contained in the original images. 叶片脉络在结构和图案上表现出显著的多样性，然而现有的基因-环境关联研究主要使用少量低维汇总特征来量化叶脉，从而丢弃了原始图像中包含的大部分结构信息。

We propose an integrated deep learning and statistical framework. The proposed framework achieves four methodological advances. First, it represents the complete leaf vascular architecture as a whole-network image phenotype. Second, it fine-tunes the deep learning-based Edge Detection with Transformers (EDTER) model to accurately extract whole-network leaf vascular architecture from RGB images by jointly learning local and global contextual features. 我们提出了一种深度学习与统计学的集成框架。该框架实现了四项方法论突破：首先，它将完整的叶片脉络结构表示为全网络图像表型；其次，它通过微调基于深度学习的“基于Transformer的边缘检测”（EDTER）模型，通过联合学习局部和全局上下文特征，从RGB图像中精确提取全网络叶脉结构。

Third, it constructs a new annotated leaf image database by integrating edge maps generated by DiffusionEdge with the Berkeley Segmentation Database (BSDS500). Fourth, it applies Semiparametric Sparse Canonical Correlation Analysis (SSCCA) to perform variable selection and model associations between repeatedly measured high-dimensional Bivariate image responses and high-dimensional predictors while simultaneously accommodating sparse, zero-inflated data represented by edge maps through a truncated latent Gaussian copula model. 第三，它通过整合由DiffusionEdge生成的边缘图与伯克利分割数据库（BSDS500），构建了一个新的标注叶片图像数据库；第四，它应用半参数稀疏典型相关分析（SSCCA）进行变量选择，并对重复测量的高维双变量图像响应与高维预测因子之间的关联进行建模，同时通过截断潜在高斯Copula模型来处理边缘图所代表的稀疏、零膨胀数据。

Two simulation studies demonstrate the performance of the proposed framework under increasing levels of complexity. Application to a real Populus dataset identifies three significant gene—geography interactions associated with leaf vascular architecture, providing new biological insights and establishing a broadly applicable methodological framework for high-dimensional complex image phenotypes. 两项模拟研究证明了该框架在不同复杂度水平下的性能。将其应用于真实的杨树（Populus）数据集，识别出了三个与叶片脉络结构相关的显著基因-地理相互作用，这不仅提供了新的生物学见解，也为高维复杂图像表型建立了一个具有广泛适用性的方法论框架。