标题： 基于FDR校正的稀疏典型相关分析及其在成像基因组学中的应用 显示英文标题

标题： FDR-Corrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics

摘要： 减少假发现的数量目前是生命科学领域中最紧迫的问题之一。 这对神经影像学和基因组学中的许多应用尤为重要，因为在这些领域中数据集通常是高维的，这意味着解释变量的数量超过了样本量。 错误发现率（FDR）是一种可以用来解决该问题的标准。 因此，它作为多重假设检验工具获得了极大的欢迎。 典型相关分析（CCA）是一种统计技术，用于理解在同一组样本上收集的两组测量值之间的交叉相关性（例如，同一精神疾病患者的大脑成像和基因组数据），而稀疏CCA则将经典方法扩展到高维设置。 在这里，我们提出了一种将FDR概念应用于稀疏CCA的方法，并提出了一种控制FDR的方法。 所提出的FDR校正直接影响解的稀疏性，使其适应未知的真实稀疏水平。 理论推导以及模拟研究显示，我们的程序确实将典型向量的FDR保持在用户指定的目标水平以下。 我们将所提出的方法应用于费城神经发育队列的成像基因组学数据集。 我们的结果显示，通过功能磁共振成像（fMRI）测量的情绪识别任务期间的大脑活动所得出的大脑连接性特征与相应受试者的基因组数据相关联。 显示英文摘要

摘要： Reducing the number of false discoveries is presently one of the most pressing issues in the life sciences. It is of especially great importance for many applications in neuroimaging and genomics, where datasets are typically high-dimensional, which means that the number of explanatory variables exceeds the sample size. The false discovery rate (FDR) is a criterion that can be employed to address that issue. Thus it has gained great popularity as a tool for testing multiple hypotheses. Canonical correlation analysis (CCA) is a statistical technique that is used to make sense of the cross-correlation of two sets of measurements collected on the same set of samples (e.g., brain imaging and genomic data for the same mental illness patients), and sparse CCA extends the classical method to high-dimensional settings. Here we propose a way of applying the FDR concept to sparse CCA, and a method to control the FDR. The proposed FDR correction directly influences the sparsity of the solution, adapting it to the unknown true sparsity level. Theoretical derivation as well as simulation studies show that our procedure indeed keeps the FDR of the canonical vectors below a user-specified target level. We apply the proposed method to an imaging genomics dataset from the Philadelphia Neurodevelopmental Cohort. Our results link the brain connectivity profiles derived from brain activity during an emotion identification task, as measured by functional magnetic resonance imaging (fMRI), to the corresponding subjects' genomic data.