School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
Med Image Anal. 2021 May;70:102003. doi: 10.1016/j.media.2021.102003. Epub 2021 Mar 5.
To uncover the genetic underpinnings of brain disorders, brain imaging genomics usually jointly analyzes genetic variations and imaging measurements. Meanwhile, other biomarkers such as proteomic expressions can also carry valuable complementary information. Therefore, it is necessary yet challenging to investigate the underlying relationships among genetic variations, proteomic expressions, and neuroimaging measurements, which stands a chance of gaining new insights into the pathogenesis of brain disorders. Given multiple types of biomarkers, using sparse multi-view canonical correlation analysis (SMCCA) and its variants to identify the multi-way associations is straightforward. However, due to the gradient domination issue caused by the naive fusion of multiple SCCA objectives, SMCCA is suboptimal. In this paper, we proposed two adaptive SMCCA (AdaSMCCA) methods, i.e. the robustness-aware AdaSMCCA and the uncertainty-aware AdaSMCCA, to analyze the complicated associations among genetic, proteomic, and neuroimaging biomarkers. We also imposed a data-driven feature grouping penalty to the genetic data with aim to uncover the joint inheritance of neighboring genetic variations. An efficient optimization algorithm, which is guaranteed to converge, was provided. Using two state-of-the-art SMCCA as benchmarks, we evaluated robustness-aware AdaSMCCA and uncertainty-aware AdaSMCCA on both synthetic data and real neuroimaging, proteomics, and genetic data. Both proposed methods obtained higher associations and cleaner canonical weight profiles than comparison methods, indicating their promising capability for association identification and feature selection. In addition, the subsequent analysis showed that the identified biomarkers were related to Alzheimer's disease, demonstrating the power of our methods in identifying multi-way bi-multivariate associations among multiple heterogeneous biomarkers.
为了揭示大脑疾病的遗传基础，脑影像基因组学通常联合分析遗传变异和影像测量。同时，其他生物标志物，如蛋白质组表达，也可以提供有价值的补充信息。因此，研究遗传变异、蛋白质组表达和神经影像测量之间的潜在关系是必要的，但也具有挑战性，这有可能为大脑疾病的发病机制提供新的见解。考虑到多种类型的生物标志物，使用稀疏多视图典型相关分析（SMCCA）及其变体来识别多向关联是直接的。然而，由于多个 SCCA 目标的简单融合导致的梯度主导问题，SMCCA 并不是最优的。在本文中，我们提出了两种自适应 SMCCA（AdaSMCCA）方法，即稳健感知的 AdaSMCCA 和不确定性感知的 AdaSMCCA，以分析遗传、蛋白质组和神经影像生物标志物之间的复杂关联。我们还对遗传数据施加了一个数据驱动的特征分组惩罚，以揭示相邻遗传变异的联合遗传。提供了一种保证收敛的有效优化算法。使用两种最先进的 SMCCA 作为基准，我们在合成数据和真实的神经影像、蛋白质组和遗传数据上评估了稳健感知的 AdaSMCCA 和不确定性感知的 AdaSMCCA。与比较方法相比，所提出的两种方法都获得了更高的关联度和更清晰的典型权重分布，表明它们在识别多向生物多元关联和特征选择方面具有很大的潜力。此外，后续分析表明，所识别的生物标志物与阿尔茨海默病有关，这表明了我们的方法在识别多种异构生物标志物之间的多向双向多元关联方面的强大能力。
