SGI: Automatic clinical subgroup identification in omics datasets

Mustafa Buyukozkan, Karsten Suhre, Jan Krumsiek, Alfonso Valencia
2021 Bioinformatics  
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
more » ... per, 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 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.
doi:10.1093/bioinformatics/btab656 pmid:34529048 pmcid:PMC8723155 fatcat:bxvzsnqlxbhvxbf3tyr5f47vp4