Yan Jingwen, Risacher Shannon L, Nho Kwangsik, Saykin Andrew J, Shen L I
Department of BioHealth Informatics, Indiana University, Indianapolis, 46202, USA2Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, 46202, USA*To whom correspondence should be addressed.,
Pac Symp Biocomput. 2017;22:94-104. doi: 10.1142/9789813207813_0010.
Brain imaging and protein expression, from both cerebrospinal fluid and blood plasma, have been found to provide complementary information in predicting the clinical outcomes of Alzheimer's disease (AD). But the underlying associations that contribute to such a complementary relationship have not been previously studied yet. In this work, we will perform an imaging proteomics association analysis to explore how they are related with each other. While traditional association models, such as Sparse Canonical Correlation Analysis (SCCA), can not guarantee the selection of only disease-relevant biomarkers and associations, we propose a novel discriminative SCCA (denoted as DSCCA) model with new penalty terms to account for the disease status information. Given brain imaging, proteomic and diagnostic data, the proposed model can perform a joint association and multi-class discrimination analysis, such that we can not only identify disease-relevant multimodal biomarkers, but also reveal strong associations between them. Based on a real imaging proteomic data set, the empirical results show that DSCCA and traditional SCCA have comparable association performances. But in a further classification analysis, canonical variables of imaging and proteomic data obtained in DSCCA demonstrate much more discrimination power toward multiple pairs of diagnosis groups than those obtained in SCCA.
脑成像以及来自脑脊液和血浆的蛋白质表达，已被发现能在预测阿尔茨海默病（AD）的临床结果方面提供互补信息。但此前尚未对促成这种互补关系的潜在关联进行过研究。在这项工作中，我们将进行成像蛋白质组学关联分析，以探索它们之间的相互关系。虽然传统的关联模型，如稀疏典型相关分析（SCCA），不能保证仅选择与疾病相关的生物标志物和关联，但我们提出了一种带有新惩罚项的新型判别式SCCA（记为DSCCA）模型，以考虑疾病状态信息。给定脑成像、蛋白质组学和诊断数据，所提出的模型可以进行联合关联和多类别判别分析，这样我们不仅可以识别与疾病相关的多模态生物标志物，还可以揭示它们之间的强关联。基于一个真实的成像蛋白质组数据集，实证结果表明DSCCA和传统的SCCA具有相当的关联性能。但在进一步的分类分析中，DSCCA中获得的成像和蛋白质组数据的典型变量对多组诊断组的判别能力比SCCA中获得的典型变量要强得多。
