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<p>It can be challenging to perform an integrative statistical analysis of multi-view high-dimensional data acquired from different experiments on each subject who participated in a joint study. Canonical Correlation Analysis (CCA) is a statistical procedure for identifying relationships between such datasets. In that context, Structured Sparse CCA (ScSCCA) is a rapidly emerging methodological area that aims for robust modeling of the interrelations between the different data modalities by assuming the corresponding CCA directional vectors to be sparse. Although it is a rapidly growing area of statistical methodology development, there is a need to develop related methodologies in the Bayesian paradigm. In this manuscript, we propose a novel ScSCCA approach, called the Multiplicative Half-Cauchy Factor Model, where we employ a Bayesian infinite factor model and aim to achieve robust estimation by encouraging sparsity in the modeling framework. We utilize a new multiplicative Half-Cauchy process prior to encourage sparsity at the level of the latent variable loading matrices. We conduct multiple simulations to compare the performance of the proposed method with that of other frequently used CCA procedures, and we apply the developed procedures to analyze multi-omics data arising from a breast cancer study.</p>

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A Bayesian Methodology for Estimation for Sparse Canonical Correlation

  • 期刊论文

  • 发表日期：

    2026年5月7日

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  • Siddhesh Kulkarni,
  • Subhadip Pal,
  • Jeremy T. Gaskins

摘要

It can be challenging to perform an integrative statistical analysis of multi-view high-dimensional data acquired from different experiments on each subject who participated in a joint study. Canonical Correlation Analysis (CCA) is a statistical procedure for identifying relationships between such datasets. In that context, Structured Sparse CCA (ScSCCA) is a rapidly emerging methodological area that aims for robust modeling of the interrelations between the different data modalities by assuming the corresponding CCA directional vectors to be sparse. Although it is a rapidly growing area of statistical methodology development, there is a need to develop related methodologies in the Bayesian paradigm. In this manuscript, we propose a novel ScSCCA approach, called the Multiplicative Half-Cauchy Factor Model, where we employ a Bayesian infinite factor model and aim to achieve robust estimation by encouraging sparsity in the modeling framework. We utilize a new multiplicative Half-Cauchy process prior to encourage sparsity at the level of the latent variable loading matrices. We conduct multiple simulations to compare the performance of the proposed method with that of other frequently used CCA procedures, and we apply the developed procedures to analyze multi-omics data arising from a breast cancer study.