Adaptive Sparse Multiple Canonical Correlation Analysis With Application to Imaging (Epi)Genomics Study of Schizophrenia.
Adaptive Sparse Multiple Canonical Correlation Analysis With Application to Imaging (Epi)Genomics Study of Schizophrenia.
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DOI：
10.1109/tbme.2017.2771483
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发表时间：
2018-03
期刊：
影响因子：
--
通讯作者：
Wang YP
中科院分区：
文献类型：
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作者：
Hu W;Lin D;Cao S;Liu J;Chen J;Calhoun VD;Wang YP
在成像和（epi）基因组学中发现多个数据集之间的相关性是一个常见的挑战。稀疏多典型相关分析（SMCCA）是一种多变量模型，广泛用于从每个数据中提取贡献特征，同时最大化跨模态相关性。该模型是通过使用任意两个数据集之间的成对协方差的组合来实现的。然而，不同的两两协方差的尺度可能相差很大，在SMCCA中两两协方差的直接组合是不公平的。“两两协方差的不公平组合”问题限制了SMCCA的特征选择能力。在本文中，我们提出了一种新的SMCCA公式，称为自适应SMCCA，通过在组合成对协方差时引入自适应权值来克服这个问题。仿真和实际数据分析表明，自适应SMCCA在特征选择方面优于传统的SMCCA和固定权值的SMCCA。大规模数值实验表明，自适应SMCCA收敛速度与传统SMCCA相当。将其应用于精神分裂症受试者的成像（epi）遗传学研究时，我们可以检测到显着的（epi）遗传变异和脑区域，这与其他已有报道一致。此外，我们的模型还检测到一些重要的脑发育相关途径，例如神经管发育，这表明传统的SMCCA可能会忽略成像表观遗传关联。这些结果表明，自适应SMCCA非常适合检测三向或多向相关性，因此可以在多组学和成像数据集成中找到广泛的应用。
Finding correlations across multiple data sets in imaging and (epi)genomics is a common challenge. Sparse multiple canonical correlation analysis (SMCCA) is a multivariate model widely used to extract contributing features from each data while maximizing the cross-modality correlation. The model is achieved by using the combination of pairwise covariances between any two data sets. However, the scales of different pairwise covariances could be quite different and the direct combination of pairwise covariances in SMCCA is unfair. The problem of ‘unfair combination of pairwise covariances’ restricts the power of SMCCA for feature selection. In this paper, we propose a novel formulation of SMCCA, called adaptive SMCCA, to overcome the problem by introducing adaptive weights when combining pairwise covariances. Both simulation and real data analysis show the outperformance of adaptive SMCCA in terms of feature selection over conventional SMCCA and SMCCA with fixed weights. Large-scale numerical experiments show that adaptive SMCCA converges as fast as conventional SMCCA. When applying it to imaging (epi)genetics study of schizophrenia subjects, we can detect significant (epi)genetic variants and brain regions, which are consistent with other existing reports. In addition, several significant brain-development related pathways, e.g., neural tube development, are detected by our model, demonstrating imaging epigenetic association may be overlooked by conventional SMCCA. All these results demonstrate that adaptive SMCCA are well-suited for detecting three-way or multi-way correlations and thus can find widespread applications in multiple omics and imaging data integration.