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Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects.

Lu Huo^1,2, Jiao Jiao Li³, Ling Chen²

¹Data Science Institute, University of Technology Sydney, Ultimo, NSW 2007, Australia.

Briefings in Bioinformatics

|June 10, 2021

Summary

Single-cell multi-omics sequencing analyzes multiple cell layers, advancing disease research like COVID-19. This survey highlights machine learning and bioinformatics tools for complex data analysis, identifying key challenges and future opportunities.

Keywords:

COVID-19 graph-based algorithms integrative methods single-cell multi-omics sequencing single-cell sequencing variational inference

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Area of Science:

Biotechnology
Genomics
Molecular Biology

Background:

Single-cell sequencing provides genomic information from individual cells.
Single-cell multi-omics sequencing analyzes multiple molecular layers (DNA, RNA, protein) from the same cell.
This technology is crucial for understanding cellular mechanisms and complex diseases.

Purpose of the Study:

To survey recent advancements in single-cell multi-omics sequencing.
To review applications in understanding complex diseases, particularly COVID-19.
To summarize analytical techniques and address data challenges.

Main Methods:

Overview of single-cell multi-omics sequencing technologies.
Review of machine learning and bioinformatics approaches for data analysis.
Discussion of data consistency, diversity, error correction, and imputation.

Main Results:

Variational inference and graph-based learning are popular analytical methods.
Seurat V3 is a common tool for data integration and imputation.
Identified challenges in data consistency, diversity, error correction, and imputation.

Conclusions:

Single-cell multi-omics sequencing is a powerful tool for biological research.
Machine learning and bioinformatics are essential for analyzing complex single-cell data.
Future research should focus on improving data accuracy and imputation methods.