Integrative Methods and Practical Challenges for Single-Cell Multi-omics

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell multimodal omics (scMulti-omics) technologies are rapidly advancing.
• These technologies allow simultaneous measurement of multiple molecular layers (e.g., DNA methylation, RNA expression, protein abundance) from individual cells.
• This comprehensive data acquisition offers unprecedented biological insights but poses significant analytical challenges.

Purpose of the Study:

• To review existing integrative computational methods for scMulti-omics data analysis.
• To summarize available computational tools for diverse scMulti-omics datasets.
• To identify current challenges and future trends in scMulti-omics modeling.

Main Methods:

• Literature review of integrative computational methods for scMulti-omics.
• Survey and categorization of publicly available scMulti-omics analysis tools.
• Case study analysis to evaluate tool functionalities and practical challenges.

Main Results:

• A comprehensive overview of integrative analysis approaches for scMulti-omics data.
• A summary of current computational tools, detailing their capabilities and limitations.
• Identification of key challenges in applying these tools to real-world biological questions.

Conclusions:

• The field of scMulti-omics is rapidly evolving, necessitating advanced computational strategies.
• Further development of robust and user-friendly tools is crucial for widespread adoption and discovery.
• Addressing current analytical bottlenecks will drive future advancements in single-cell biology.