MG-TCCA:跨多组的张量典型相关分析。

IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-09-30 DOI:10.1109/TCBB.2024.3471930
Zhuoping Zhou, Boning Tong, Davoud Ataee Tarzanagh, Bojian Hou, Andrew J Saykin, Qi Long, Li Shen
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引用次数: 0

摘要

张量典型相关分析(TCCA)是一种常用的统计方法,用于研究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中张量数据存在的异质性问题,例如从不同群体收集的脑成像数据,这些群体的特点是性别和种族等因素。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,我们提出了一种称为多组 TCCA(MG-TCCA)的新方法,它可以对多个子组进行联合分析。我们的 MG-TCCA 方法结合了双重稀疏性结构和块坐标上升算法,能有效解决异质性问题,并利用不同组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。为了对我们的方法进行经验验证,我们开展了一项研究,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)模式(AV-45 和 FDG)之间的相关性。我们的研究结果表明,MG-TCCA 在识别性别特异性跨模态成像相关性方面超过了传统 TCCA 和稀疏 TCCA(STCCA)。MG-TCCA 性能的提高为确定 AD 多模态成像生物标记物的特征提供了宝贵的见解。
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MG-TCCA: Tensor Canonical Correlation Analysis across Multiple Groups.

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.

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来源期刊
CiteScore
7.50
自引率
6.70%
发文量
479
审稿时长
3 months
期刊介绍: IEEE/ACM Transactions on Computational Biology and Bioinformatics emphasizes the algorithmic, mathematical, statistical and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development of biological databases; and important biological results that are obtained from the use of these methods, programs and databases; the emerging field of Systems Biology, where many forms of data are used to create a computer-based model of a complex biological system
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