用于全网络基因-环境与叶片维管结构关联的集成深度学习与统计框架

中文摘要 AI 辅助

叶片叶脉在结构和图案上表现出显著的多样性，但现有的基因-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。我们提出了一个集成深度学习和统计的框架。该框架实现了四个方法上的进展。首先，将完整的叶片维管结构表示为全网络图像表型。其次，微调基于深度学习的带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图像中准确提取全网络叶片维管结构。第三，通过将DiffusionEdge生成的边缘图与伯克利分割数据库（BSDS500）集成，构建一个新的带注释的叶片图像数据库。第四，应用半参数稀疏典型相关分析（SSCCA）进行变量选择，并在重复测量的高维双变量图像响应和高维预测变量之间建立模型关联，同时通过截断潜在高斯copula模型适应由边缘图表示的稀疏、零膨胀数据。两项模拟研究证明了该框架在不断增加的复杂度水平下的性能。将其应用于真实的杨树数据集，识别出与叶片维管结构相关的三个显著基因-地理相互作用，提供了新的生物学见解，并为高维复杂图像表型建立了广泛适用的方法框架。

英文摘要

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. 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. 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. Two simulation studies demonstrate the performance of the proposed framework under increasing levels of complexity. Application to a real \emph{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.

发表机构

• Binghamton University（宾厄姆顿大学）
• Illinois State University（伊利诺伊州立大学）

机构由 AI 辅助整理，请以论文原文为准。