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

Yuxuan Chen1, Ruiqing Zheng1, Jin Liu1

  • 1School of Computer Science and Engineering, Central South University, Changsha 410083, China.

Briefings in Bioinformatics
|March 17, 2024
PubMed
Summary
This summary is machine-generated.

We developed scMLC, a novel framework for clustering cells using multi-modal sequencing data. scMLC integrates gene expression and chromatin accessibility, improving cell atlas resolution and disease study accuracy.

Keywords:
cell-to-cell networksmulti-omicsmultiplex community detectionsingle-cell sequencing

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell multi-modal sequencing offers high-resolution cell atlases but faces integration challenges.
  • Effective data integration is crucial for understanding cellular states in health and disease.

Purpose of the Study:

  • To propose scMLC, a single-cell multi-modal Louvain clustering framework.
  • To address the challenge of integrating diverse sequencing data for robust cell clustering.

Main Methods:

  • scMLC constructs multiplex single- and cross-modal cell-to-cell networks.
  • It captures both modal-specific and consistent information across modalities.
  • A robust multiplex community detection method is employed for reliable cell clustering.

Main Results:

  • scMLC demonstrated superior accuracy and stability compared to 15 state-of-the-art methods on seven real-world datasets.
  • The cell-network-based integration strategy showed better generalization capabilities in synthetic data.
  • scMLC is adaptable for single-cell sequencing data with more than two modalities.

Conclusions:

  • scMLC provides an effective framework for multi-modal single-cell data integration and clustering.
  • The proposed method advances the creation of high-resolution cell atlases and aids in health and disease research.
  • scMLC's flexibility supports future multi-modal single-cell analyses.