SGI: Automatic clinical subgroup identification in omics datasets

Abstract

SummaryThe 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 paper, we describe the functionality of the toolbox and demonstrate an application example on a blood metabolomics dataset with various clinical biochemistry readouts in a type 2 diabetes case-control study. Availability and implementationSGI 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.