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scConfluence: single-cell diagonal integration with regularized Inverse Optimal Transport on weakly connected

Jules Samaran1, Gabriel Peyré2, Laura Cantini3

  • 1Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, Paris, France.

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|September 5, 2024
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Summary
This summary is machine-generated.

scConfluence enhances single-cell data integration by combining autoencoders and optimal transport, overcoming limitations of current methods. This approach preserves biological information and improves cell population analysis across diverse datasets.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell data integration is crucial for understanding cellular heterogeneity.
  • Existing diagonal integration methods often lose biological information and struggle with modality-specific populations.

Purpose of the Study:

  • To introduce scConfluence, a novel method for single-cell diagonal integration.
  • To overcome limitations of current state-of-the-art integration techniques.

Main Methods:

  • scConfluence utilizes uncoupled autoencoders on complete features.
  • Regularized Inverse Optimal Transport is applied to weakly connected features.

Main Results:

  • scConfluence outperforms existing methods in single-cell integration benchmarks.
  • Accurate prediction of spatial patterns for Scgn, Synpr, and Olah in scRNA-smFISH data.
  • Improved classification of B cells and Monocytes in scRNA-scATAC-CyTOF integration.
  • Revealed joint contribution of Fezf2 and dendrite morphology in Intra Telencephalic neurons.

Conclusions:

  • scConfluence offers a robust solution for multimodal single-cell data integration.
  • The method preserves biological information and enhances cell type classification.
  • Demonstrates broad applicability in diverse single-cell analysis scenarios.