Zhou Zhuoping, Tong Boning, Tarzanagh Davoud Ataee, Hou Bojian, Saykin Andrew J, Long Qi, Shen Li
IEEE/ACM Trans Comput Biol Bioinform. 2024 Sep 30;PP. doi: 10.1109/TCBB.2024.3471930.
Tensor Canonical Correlation Analysis (TCCA) is a commonly employed statistical method utilized to examine linear associations between two sets of tensor datasets. However, the existing TCCA models fail to adequately address the heterogeneity present in real-world tensor data, such as brain imaging data collected from diverse groups characterized by factors like sex and race. Consequently, these models may yield biased outcomes. In order to surmount this constraint, we propose a novel approach called Multi-Group TCCA (MG-TCCA), which enables the joint analysis of multiple subgroups. By incorporating a dual sparsity structure and a block coordinate ascent algorithm, our MG-TCCA method effectively addresses heterogeneity and leverages information across different groups to identify consistent signals. This novel approach facilitates the quantification of shared and individual structures, reduces data dimensionality, and enables visual exploration. To empirically validate our approach, we conduct a study focused on investigating correlations between two brain positron emission tomography (PET) modalities (AV-45 and FDG) within an Alzheimer's disease (AD) cohort. Our results demonstrate that MG-TCCA surpasses traditional TCCA and Sparse TCCA (STCCA) in identifying sex-specific cross-modality imaging correlations. This heightened performance of MG-TCCA provides valuable insights for the characterization of multimodal imaging biomarkers in AD.
张量典型相关分析（TCCA）是一种常用的统计方法，用于检验两组张量数据集之间的线性关联。然而，现有的TCCA模型未能充分解决现实世界张量数据中存在的异质性问题，例如从以性别和种族等因素为特征的不同群体收集的脑成像数据。因此，这些模型可能会产生有偏差的结果。为了克服这一限制，我们提出了一种名为多组TCCA（MG-TCCA）的新方法，该方法能够对多个亚组进行联合分析。通过结合双重稀疏结构和块坐标上升算法，我们的MG-TCCA方法有效地解决了异质性问题，并利用不同组之间的信息来识别一致的信号。这种新方法有助于量化共享结构和个体结构，降低数据维度，并实现可视化探索。为了实证验证我们的方法，我们进行了一项研究，重点调查阿尔茨海默病（AD）队列中两种脑正电子发射断层扫描（PET）模态（AV-45和FDG）之间的相关性。我们的结果表明，在识别性别特异性跨模态成像相关性方面，MG-TCCA优于传统的TCCA和稀疏TCCA（STCCA）。MG-TCCA的这种更高性能为AD中多模态成像生物标志物的表征提供了有价值的见解。
