Spectrum: fast density-aware spectral clustering for single and multi-omic data

Christopher R. John, David S. Watson, Michael R. Barnes, Costantino Pitzalis, Myles J. Lewis

Abstract: Clustering patient omic data is integral to developing precision medicine because it allows the identification of disease subtypes. A current major challenge is the integration multi-omic data to identify a shared structure and reduce noise. Cluster analysis is also increasingly applied on single-omic data, for example, in single cell RNA-seq analysis for clustering the transcriptomes of individual cells. This technology has clinical implications. Our motivation was therefore to develop a flexible and effective spectral clustering tool for both single and multi-omic data.