论文标题
SINGR：用于同时进行数据集成的非高斯组件分析的R软件包
singR: An R package for Simultaneous non-Gaussian Component Analysis for data integration
论文作者
论文摘要
本文介绍了一个R软件包，该软件包实现同时进行数据集成的非高斯组件分析。 Sing使用非高斯信息的信息来提取在多个数据集中共享的特征加载和分数（潜在变量）。我们通过两个示例来描述和实施功能。第一个示例是与图像一起使用的玩具示例。第二个示例是一项模拟研究，从静止状态的功能磁共振成像数据集和从工作内存功能磁共振成像数据集中整合功能连通性估计值和任务激活图。 SIN模型可以产生关节组件，这些组件可以准确反映由多个数据集共享的信息，尤其是对于具有神经影像学等非高斯特征的数据集。
This paper introduces an R package that implements Simultaneous non-Gaussian Component Analysis for data integration. SING uses a non-Gaussian measure of information to extract feature loadings and scores (latent variables) that are shared across multiple datasets. We describe and implement functions through two examples. The first example is a toy example working with images. The second example is a simulated study integrating functional connectivity estimates from a restingstate functional magnetic resonance imaging dataset and task activation maps from a working memory functional magnetic resonance imaging dataset. The SING model can produce joint components that accurately reflect information shared by multiple datasets, particularly for datasets with non-Gaussian features such as neuroimaging.
