Spectral clustering of single-cell multi-omics data on multilayer graphs

Abstract

Motivation Single-cell sequencing technologies that simultaneously generate multimodal cellular profiles present opportunities for improved understanding of cell heterogeneity in tissues. How the multimodal information can be integrated to obtain a common cell type identification, however, poses a computational challenge. Multilayer graphs provide a natural representation of multi-omic single-cell sequencing datasets, and finding cell clusters may be understood as a multilayer graph partition problem. Results We introduce two spectral algorithms on multilayer graphs, spectral clustering on multilayer graphs (SCML) and the weighted locally linear (WLL) method, to cluster cells in multi-omic single-cell sequencing datasets. We connect these algorithms through a unifying mathematical framework that represents each layer using a Hamiltonian operator and a mixture of its eigenstates to integrate the multiple graph layers, demonstrating in the process that the WLL method is a rigorous multilayer spectral graph theoretic reformulation of the popular Seurat weighted nearest neighbor (WNN) algorithm. Implementing our algorithms and applying them to a CITE-seq dataset of cord blood mononuclear cells yields results similar to the Seurat WNN analysis. Our work thus extends spectral methods to multimodal single-cell data analysis. Availability The code used in this study can be found at https://github.com/jssong-lab/sc-spectrum Supplementary information Supplementary data are available at Bioinformatics online.

Multi-omic approaches for host-microbiome data integration

“… 92 Extensions of spectral clustering have been developed for multi-omics datasets, such as in one approach taken by Zhang et al that finds clusters of cells in multi-modal single-cell sequencing data. 93 …”

A holistic approach to understanding immune-mediated inflammatory diseases: bioinformatic tools to integrate omics data

“…For example, collecting immunophenotyping data in addition to RNA profiling. Sc-spectrum [42] is a clustering tool developed for this purpose, by unifying two of the main algorithms previously used: the weighted nearest neighbor (WNN) algorithm and the spectral clustering on multilayer graphs (SCML) algorithm. This novel approach provides the opportunity to contrast the efficacy of both algorithms.…”