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Related Experiment Video

Updated: Aug 25, 2025

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

Multimodal Single-Cell Translation and Alignment with Semi-Supervised Learning.

Ran Zhang¹, Laetitia Meng-Papaxanthos², Jean-Philippe Vert³

¹Department of Genome Sciences, University of Washington, Seattle, Washington, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|October 17, 2022

Summary

This summary is machine-generated.

Polarbear, a new machine learning framework, integrates single-cell multi-omics data. It predicts missing cellular data and aligns cells across different measurements, improving multimodal data analysis.

Keywords:

cross-modality translation and multi-omics alignment single cell multi-omics

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Related Experiment Videos

Last Updated: Aug 25, 2025

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

Analysis of Translation in the Developing Mouse Brain using Polysome Profiling

Analysis of Translation in the Developing Mouse Brain using Polysome Profiling

Published on: May 22, 2021

Area of Science:

Single-cell biology
Computational biology
Genomics

Background:

Single-cell multi-omics technologies offer deep insights into cellular regulation.
Current methods typically analyze only one cellular activity per cell (e.g., transcription, epigenetics).
Integrating data from multiple single-cell assays remains a significant challenge.

Purpose of the Study:

To develop a computational framework for multimodal single-cell data integration.
To enable prediction of missing molecular profiles in single cells.
To facilitate accurate alignment of single cells across different data modalities.

Main Methods:

Proposed Polarbear, a semi-supervised machine learning framework.
Utilized beta-variational autoencoders for robust cell representation learning.
Employed co-assay data for training modality translation models, leveraging public single-assay data.

Main Results:

Polarbear effectively predicts missing modality profiles for single cells.
Achieved improved accuracy in single-cell cross-modality alignment compared to supervised methods.
Demonstrated successful integration of multimodal single-cell data.

Conclusions:

Polarbear offers a robust solution for multimodal single-cell data integration.
The semi-supervised approach overcomes limitations of sparse and low-quantity co-assay data.
Enables a more comprehensive understanding of cellular heterogeneity and regulation.