Summary The ‘Subgroup Identification’ (SGI) toolbox provides an algorithm to automatically detect clinical subgroups of samples in large-scale omics datasets. It is based on hierarchical clustering trees in combination with a specifically designed association testing and visualization framework that can process an arbitrary number of clinical parameters and outcomes in a systematic fashion. A multi-block extension allows for the simultaneous use of multiple omics datasets on the same samples. In this article, we first describe the functionality of the toolbox and then demonstrate its capabilities through application examples on a type 2 diabetes metabolomics study as well as two copy number variation datasets from The Cancer Genome Atlas. Availability and implementation SGI is an open-source package implemented in R. Package source codes and hands-on tutorials are available at https://github.com/krumsieklab/sgi. The QMdiab metabolomics data is included in the package and can be downloaded from https://doi.org/10.6084/m9.figshare.5904022. Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

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SGI：组学数据集中的自动临床亚组识别

总结 “亚组识别”(SGI) 工具箱提供了一种算法，可自动检测大规模组学数据集中的临床样本亚组。它基于层次聚类树，结合专门设计的关联测试和可视化框架，可以系统地处理任意数量的临床参数和结果。多块扩展允许在同一样本上同时使用多个组学数据集。在本文中，我们首先描述了该工具箱的功能，然后通过 2 型糖尿病代谢组学研究的应用示例以及癌症基因组图谱中的两个拷贝数变异数据集展示了它的功能。可用性和实现 SGI 是一个用 R 实现的开源包。包源代码和动手教程可在 https://github.com/krumsieklab/sgi 获得。QMdiab 代谢组学数据包含在软件包中，可以从 https://doi.org/10.6084/m9.figshare.5904022 下载。补充信息 补充数据可在 Bioinformatics 在线获取。