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

Shuyi Zhang | Zendy; Jacob R. Leistico | Zendy; Raymond J. Cho | Zendy; Jeffrey B. Cheng | Zendy; Jun S. Song | Zendy

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

Author(s) -

Shuyi Zhang,

Jacob R. Leistico,

Raymond J. Cho,

Jeffrey B. Cheng,

Jun S. Song

Publication year - 2022

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btac378

Subject(s) - computer science , cluster analysis , spectral clustering , data mining , eigenvalues and eigenvectors , graph partition , partition (number theory) , graph , theoretical computer science , algorithm , artificial intelligence , mathematics , physics , quantum mechanics , combinatorics

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.

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