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Related Experiment Video

Updated: Sep 15, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

OmicsTweezer: A distribution-independent cell deconvolution model for multi-omics Data.

Xinxing Yang¹, Faming Zhao¹, Tao Ren¹

¹Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.

|July 17, 2025

Summary

This summary is machine-generated.

OmicsTweezer overcomes batch effects in cell deconvolution for multi-omics data. This novel model uses deep learning and optimal transport to accurately estimate cell type proportions, improving disease microenvironment studies.

Keywords:

batch effects cell deconvolution deep learning optimal transport single-cell data spatial transcriptomics tissue microenvironment

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Isolation of Endothelial Cells from the Lumen of Mouse Carotid Arteries for Single-Cell Multi-Omics Experiments

Isolation of Endothelial Cells from the Lumen of Mouse Carotid Arteries for Single-Cell Multi-Omics Experiments

Published on: October 4, 2021

Area of Science:

Computational biology
Genomics
Proteomics

Background:

Cell deconvolution is crucial for analyzing tissue microenvironments using bulk omics data.
Batch effects between bulk and single-cell data impede accurate cell type proportion estimation.
Existing methods often fail to address distribution differences across datasets and omics types.

Purpose of the Study:

To develop a robust and versatile cell deconvolution model that mitigates batch effects and distribution shifts.
To enable accurate cell type proportion estimation from diverse bulk omics data (RNA-seq, proteomics, spatial transcriptomics).
To provide a unified framework for multi-omics deconvolution in disease research.

Main Methods:

Developed OmicsTweezer, a distribution-independent cell deconvolution model.
Integrated optimal transport with deep learning to align data in a shared latent space.
The model aligns simulated and real data, addressing inter-omics distribution differences.

Main Results:

OmicsTweezer effectively mitigates data shifts and distribution differences between bulk and single-cell data.
Demonstrated robustness and accuracy across simulated and real-world datasets.
Successfully deconvolved bulk RNA-seq, bulk proteomics, and spatial transcriptomics data.

Conclusions:

OmicsTweezer provides a unified and powerful framework for multi-omics cell deconvolution.
The model accurately identifies biologically meaningful cell types in cancer datasets (prostate and colon).
OmicsTweezer enhances the study of complex disease microenvironments through accurate cell type proportion estimation.