In clinical and biomedical research, multiple high-dimensional datasets are nowadays routinely collected from omics and imaging devices. Multivariate methods, such as Canonical Correlation Analysis (CCA), integrate two (or more) datasets to discover and understand underlying biological mechanisms. For an explorative method like CCA, interpretation is key. We present a sparse CCA method based on soft-thresholding that produces near-orthogonal components, allows for browsing over various sparsity levels, and permutation-based hypothesis testing. Our soft-thresholding approach avoids tuning of a penalty parameter. Such tuning is computationally burdensome and may render unintelligible results. In addition, unlike alternative approaches, our method is less dependent on the initialisation. We examined the performance of our approach with simulations and illustrated its use on real cancer genomics data from drug sensitivity screens. Moreover, we compared its performance to Penalised Matrix Analysis (PMA), which is a popular alternative of sparse CCA with a focus on yielding interpretable results. Compared to PMA, our method offers improved interpretability of the results, while not compromising, or even improving, signal discovery. he software and simulation framework are available at https://github.com/nuria-sv/toscca.

翻译：在临床和生物医学研究中，如今常从组学和成像设备中收集多个高维数据集。典型相关分析（CCA）等多变量方法可整合两个（或多个）数据集，以发现并理解潜在的生物学机制。对于CCA这类探索性方法而言，结果的解释性至关重要。我们提出了一种基于软阈值的稀疏CCA方法，该方法能生成近似正交的成分，支持在不同稀疏度水平下进行浏览，并具备基于置换的假设检验功能。我们的软阈值方法无需调整惩罚参数——此类调参过程计算负担沉重，且可能产生难以解释的结果。此外，与替代方法不同，我们的方法对初始化的依赖性更低。我们通过模拟实验检验了该方法的性能，并利用药物敏感性筛选的真实癌症基因组学数据展示了其应用。同时，我们将其性能与惩罚矩阵分析（PMA）进行了比较——PMA是另一种侧重产生可解释结果的流行稀疏CCA方法。相较于PMA，我们的方法在提升结果可解释性的同时，信号发现能力也未受影响，甚至有所增强。相关软件及模拟框架可从https://github.com/nuria-sv/toscca获取。