BLOCCS：块稀疏典型相关分析及其在可解释组学集成中的应用
BLOCCS: Block Sparse Canonical Correlation Analysis With Application To Interpretable Omics Integration
摘要：
我们引入了块稀疏典型相关分析，它可以同时估计多对典型方向（一起称为“块”），从而显着改善稀疏方向的正交性，我们证明，这将转化为更可解释的解决方案。我们的方法建立在（Solari，Brown和Bickel 2019）的稀疏CCA方法的基础上，因为我们还将我们的块公式的双凸目标表示为单位欧几里得球中正交k-框架上的凹最小化问题，而由于目标的不确定性，该问题又被缩小到Stiefel流形，该流形通过梯度下降算法进行优化。我们的模拟结果表明，我们的方法优于现有的sCCA算法和实现的计算成本和稳定性，主要是由于我们的搜索空间的急剧收缩，以及估计的典型协变量对之间的相关性和正交性。最后，我们应用我们的方法，可作为一个R-包称为BLOCCS，多组学数据肺鳞状细胞癌（LUSC）通过癌症基因组图谱获得，并证明其在捕获有意义的生物学关联相关的研究假设，而不是虚假的显性变异的能力。
英文摘要：
We introduce Block Sparse Canonical Correlation Analysis which estimates multiple pairs of canonical directions (together a "block") at once, resulting in significantly improved orthogonality of the sparse directions which, we demonstrate, translates to more interpretable solutions. Our approach builds on the sparse CCA method of (Solari, Brown, and Bickel 2019) in that we also express the bi-convex objective of our block formulation as a concave minimization problem over an orthogonal k-frame in a unit Euclidean ball, which in turn, due to concavity of the objective, is shrunk to a Stiefel manifold, which is optimized via gradient descent algorithm. Our simulations show that our method outperforms existing sCCA algorithms and implementations in terms of computational cost and stability, mainly due to the drastic shrinkage of our search space, and the correlation within and orthogonality between pairs of estimated canonical covariates. Finally, we apply our method, available as an R-package called BLOCCS, to multi-omic data on Lung Squamous Cell Carcinoma(LUSC) obtained via The Cancer Genome Atlas, and demonstrate its capability in capturing meaningful biological associations relevant to the hypothesis under study rather than spurious dominant variations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
