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

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MultiSC: a deep learning pipeline for analyzing multiomics single-cell data.

Xiang Lin^1,2, Siqi Jiang², Le Gao^1,2

¹Department of Quantitative Health Sciences, Mayo Clinic, 13400 E Shea Blvd, Scottsdale, AZ 85259, United States.

Briefings in Bioinformatics

|October 8, 2024

Summary

This summary is machine-generated.

Researchers developed MultiSC, a novel pipeline for analyzing multi-omics single-cell data. This tool integrates gene expression, chromatin accessibility, and transcription factor protein data to reveal cell activities and gene regulatory networks.

Keywords:

Causal inference Cell type annotation Gene regulatory network Mediation analysis Multi-omics Pathway analysis scATAC-seq scRNA-seq

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Published on: December 9, 2022

Area of Science:

Single-cell biology
Genomics
Proteomics
Systems biology

Background:

Single-cell technologies offer high-resolution insights into cellular functions and processes.
Multi-omics single-cell sequencing techniques capture diverse cellular behaviors.
NEAT-seq simultaneously generates gene expression, chromatin accessibility, and transcription factor protein data, but integration tools are lacking.

Purpose of the Study:

To develop a novel computational pipeline, MultiSC, for the integrated analysis of multi-omics single-cell data.
To address the gap in tools for effectively integrating gene expression, chromatin accessibility, and transcription factor protein data.
To provide a comprehensive framework for understanding cellular activities and gene regulation.

Main Methods:

MultiSC pipeline utilizes a multimodal constraint autoencoder for data integration during clustering.
Employs a matrix factorization-based model (scMF) for predicting transcription factor target genes.
Incorporates multivariate linear regression for gene regulatory network prediction.
Includes functionalities for differential expression, mediation analysis, and causal inference.

Main Results:

The MultiSC pipeline effectively integrates multi-omics single-cell data, including gene expression, chromatin accessibility, and TF protein levels.
Demonstrated ability to provide a comprehensive view of cellular activities and gene regulatory networks.
Experimental evaluations confirmed the pipeline's performance in leveraging multi-omics data.

Conclusions:

MultiSC enhances the understanding of cellular processes by enabling effective integration and analysis of diverse omics data types.
The pipeline empowers researchers to fully leverage the potential of multi-omics single-cell data for biological discovery.
Facilitates deeper insights into cell behavior and regulatory mechanisms through integrated multi-modal analysis.