Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges.

Area of Science:

• Genomics and Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Single-cell technologies offer high-resolution insights into cellular heterogeneity.
• Understanding cellular heterogeneity is crucial for cancer, immunology, and disease research.
• Machine learning (ML) is increasingly vital for analyzing complex single-cell multi-omics data.

Purpose of the Study:

• To provide a comprehensive review of ML applications in single-cell multi-omics data analysis.
• To cover essential pre-processing steps and advanced analytical methods.
• To highlight current challenges and future directions in the field.

Main Methods:

• Review of ML algorithms and tools for scRNA-seq data pre-processing (imputation, batch correction, cell identification).
• Exploration of ML methods for copy number variation, pseudo-time trajectory, phylogenetic inference, cell-cell interactions, and regulatory network analysis.
• Discussion of integrated analysis techniques for scRNA-seq and spatial transcriptomics data.

Main Results:

• ML significantly improves the accuracy and depth of single-cell multi-omics data analysis.
• A wide array of ML tools are available for various analytical tasks, from basic pre-processing to complex network inference.
• Integrated analysis of multi-omics and spatial data using ML holds great promise for biological discovery.

Conclusions:

• Machine learning is indispensable for unlocking the full potential of single-cell multi-omics data.
• Addressing challenges in multi-omics integration is key for advancing biological understanding and clinical applications.
• This review serves as a guide to current ML methodologies and future research avenues in single-cell biology.