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：在临床与生物医学研究中，如今常从组学和成像设备中例行采集多个高维数据集。典型相关分析（CCA）等多元方法可整合两个（或更多）数据集以发现和理解潜在生物学机制。对于CCA这类探索性方法而言，结果解释性至关重要。本文提出一种基于软阈值的稀疏CCA方法，该方法可生成近正交分量、支持跨多种稀疏度水平的浏览，并实现基于置换的假设检验。我们的软阈值方法无需调整惩罚参数——这种参数调优不仅计算负担沉重，还可能导致结果难以理解。此外，与其他方法不同，我们的方法对初始化的依赖性较低。我们通过模拟实验检验了方法的性能，并在来自药物敏感性筛选的真实癌症基因组学数据上展示了其应用。同时，我们将该方法与注重结果可解释性的流行稀疏CCA替代方法——惩罚矩阵分析（PMA）进行了性能比较。相较于PMA，我们的方法在提升结果可解释性的同时，能够保持甚至增强信号发现能力。软件及模拟框架可从https://github.com/nuria-sv/toscca获取。