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bioRxiv · 10.1101/2022.01.24.477443

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

Zhang, S.·Leistico, J. R.·Cho, R. J.·Cheng, J. B.·Song, J. S.
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Abstract

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. 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. The code used in this study can be found at https://github.com/jssong-lab/sc-spectrum

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Zhang, S., Leistico, J. R., Cho, R. J., Cheng, J. B., Song, J. S.. 2022-01-28. Spectral clustering of single-cell multi-omics data on multilayer graphs. https://doi.org/10.1101/2022.01.24.477443

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