A novel structure-aware sparse learning algorithm for brain imaging genetics.
A novel structure-aware sparse learning algorithm for brain imaging genetics.
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DOI：
10.1007/978-3-319-10443-0_42
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发表时间：
2014
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脑成像遗传学是一个新兴的研究领域，其中遗传变异，如单核苷酸多态性（SNPs）和神经影像数量性状（QT）之间的关联进行评估。稀疏典型相关分析（SCCA）是一种双多变量分析方法，具有揭示复杂的多SNP-多QT关联的潜力。现有的SCCA算法大多采用软阈值策略，假设数据中的特征是相互独立的。这种独立性假设通常不成立的遗传数据成像，从而不可避免地限制了产生最佳解决方案的能力。我们提出了一种新的结构感知的SCCA算法（表示为S2 CCA），不仅消除了输入数据的独立性假设，但也将类组结构的模型。在模拟和真实的成像遗传数据上，与广泛使用的SCCA实现的经验比较表明，S2 CCA可以产生改进的预测性能和生物学上有意义的发现。
Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.