Multi-Omic Spectral Clustering with the Flag Mean
TL;DR
The classic spectral clustering algorithm is employed in conjunction with the flag manifold, which allows for differing cluster structures across the omics profiles, leading to a novel approach for more flexible subtyping in multi-omics studies.
Abstract
One common goal in multi-omics studies is to identify subgroups within the study’s cohort. Many methods create subgroups through unsupervised clustering, and, to our knowledge, all of these methods attempt to infer a common subspace or latent cluster across multiple views. We argue that this is a strict assumption that may not truly exist in many datasets. We employ the classic spectral clustering algorithm in conjunction with the flag manifold. Together, this allows for differing cluster structures across the omics profiles, leading to a novel approach for more flexible subtyping in multi-omics studies. We study a data set on ventilator associated pneumonia in children. These data contain airway microbiome and transcriptome. Through simulation studies, we demonstrate that the flag mean of separate clustering subspaces can accurately capture the span of a joint clustering space. It is also robust to varying noise structures and number of features across omics profiles. Our proposed method also outperforms popular multi-omics clustering methods in the presence of differing group sizes. This proposed method is general enough to apply to other multi-omics studies as well as any multi-view study that uses spectral clustering. Code for these methods are written in R and are freely available through GitHub at https://github.com/Ghoshlab/MMOC or through CRAN at https://cran.r-project.org/web/packages/MMOC/index.html