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. 2024 Jan 22;25(2):bbae101.
doi: 10.1093/bib/bbae101.

scMLC: an accurate and robust multiplex community detection method for single-cell multi-omics data

Yuxuan Chen  1 , Ruiqing Zheng  1 , Jin Liu  1 , Min Li  1
Affiliations

scMLC: an accurate and robust multiplex community detection method for single-cell multi-omics data

Yuxuan Chen et al. Brief Bioinform. .

Abstract

Clustering cells based on single-cell multi-modal sequencing technologies provides an unprecedented opportunity to create high-resolution cell atlas, reveal cellular critical states and study health and diseases. However, effectively integrating different sequencing data for cell clustering remains a challenging task. Motivated by the successful application of Louvain in scRNA-seq data, we propose a single-cell multi-modal Louvain clustering framework, called scMLC, to tackle this problem. scMLC builds multiplex single- and cross-modal cell-to-cell networks to capture modal-specific and consistent information between modalities and then adopts a robust multiplex community detection method to obtain the reliable cell clusters. In comparison with 15 state-of-the-art clustering methods on seven real datasets simultaneously measuring gene expression and chromatin accessibility, scMLC achieves better accuracy and stability in most datasets. Synthetic results also indicate that the cell-network-based integration strategy of multi-omics data is superior to other strategies in terms of generalization. Moreover, scMLC is flexible and can be extended to single-cell sequencing data with more than two modalities.

Keywords: cell-to-cell networks; multi-omics; multiplex community detection; single-cell sequencing.

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Figures

Figure 1
Figure 1
Overview of the scMLC framework. scMLC consists of feature selection on single- and cross-modality, construction of cell-to-cell networks, reweighting cells and performing Louvain on multiplex network.
Figure 2
Figure 2
scMLC recovers major cell types in joint profiling cell line datasets. (A) NMI and (B) ARI scores of the scMLC and other comparison methods.
Figure 3
Figure 3
scMLC recovers major cell types in joint profiling complex biological datasets. (A) NMI and (B) ARI scores of the scMLC and other comparison methods.
Figure 4
Figure 4
The overall ranking of scMLC and other comparison methods on all datasets upon (A) NMI and (B) ARI.
Figure 5
Figure 5
The clustering stability for scMLC and other comparison methods on all joint profiling datasets.
Figure 6
Figure 6
The effectiveness of the integration strategy in scMLC. (A) NMI and (B) ARI scores of the scMLC, scMLC_Fusion, scMLC_RNA and scMLC_ATAC.
Figure 7
Figure 7
(A) NMI and (B) ARI scores of the scMLC and comparison methods on the CD4 memory T cells dataset (NEAT-seq).
Figure 8
Figure 8
(A) NMI and (B) ARI scores of the scMLC and comparison methods on the PBMC dataset (DOGMA-seq).
See this image and copyright information in PMC

References

    1. Hao Y, Hao S, Andersen-Nissen E, et al. Integrated analysis of multimodal single-cell data[J]. Cell 2021;184(13):3573–3587.e29. - PMC - PubMed
    1. Kiselev VY, Kirschner K, Schaub MT, et al. SC3: consensus clustering of single-cell RNA-seq data[J]. Nat Methods 2017;14(5):483–6. - PMC - PubMed
    1. Zheng R, Li M, Liang Z, et al. SinNLRR: a robust subspace clustering method for cell type detection by non-negative and low-rank representation[J]. Bioinformatics 2019;35(19):3642–50. - PubMed
    1. Fang Z, Zheng R, Li M. scMAE: a masked autoencoder for single-cell RNA-seq clustering. Bioinformatics 2024;40(1):btae020. - PMC - PubMed
    1. Bravo González-Blas C, Minnoye L, Papasokrati D, et al. cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data[J]. Nat Methods 2019;16(5):397–400. - PMC - PubMed

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