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Related Experiment Video

Updated: Jul 1, 2026

Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments
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Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments

Published on: December 21, 2017

Optimal transport for label transfer in single-cell multi-omics integration.

Junjun Ren1, Zhengqian Zhang1, Jiayu Wang2

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.

Briefings in Bioinformatics
|June 29, 2026
PubMed
Summary

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We developed scOT-LT, a new method for integrating single-cell RNA sequencing (scRNA-seq) and scATAC-seq data. This approach improves cell type annotation accuracy and robustly aligns multimodal single-cell datasets.

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell multi-omics integration offers deeper biological insights but faces challenges like modality-specific differences and cell-type proportion mismatches.
  • Existing methods struggle with accurate cross-modality alignment and label transfer.

Purpose of the Study:

  • To present scOT-LT, a novel semi-supervised framework for aligning and transferring labels between single-cell RNA sequencing (scRNA-seq) and scATAC-seq data.
  • To address challenges in multimodal single-cell data integration, particularly compositional heterogeneity and measurement discrepancies.

Main Methods:

  • Developed scOT-LT, a label-aware unbalanced optimal transport framework for cross-modality alignment.
  • Utilized unbalanced optimal transport to learn a shared embedding and entropic optimal transport coupling for label transfer from annotated scRNA-seq to unlabeled scATAC-seq data.
Keywords:
cross-modality alignmentlabel transfersemi-supervised learningsingle-cell multimodal integrationunbalanced optimal transport

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Last Updated: Jul 1, 2026

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Published on: December 21, 2017

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Published on: April 19, 2019

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Main Results:

  • scOT-LT demonstrated strong modality mixing and high label-transfer accuracy across multiple real-world datasets.
  • The method proved robust to downsampled scRNA-seq annotations and capable of detecting novel cell types.
  • Achieved explicit and interpretable cross-modality coupling.

Conclusions:

  • scOT-LT offers a practical and effective solution for multimodal single-cell data integration and annotation.
  • The framework enhances label-transfer performance and provides interpretable cross-modality correspondences.
  • This approach facilitates a more comprehensive understanding of molecular mechanisms through integrated single-cell omics data.