Artificial Intelligence in single-cell and spatial transcriptomics data analyses.

Area of Science:

• Molecular Biology
• Bioinformatics
• Computational Biology

Background:

• Single-cell (SC) and spatial transcriptomics (ST) provide high-resolution gene expression data.
• These datasets are large and complex, posing analytical challenges.
• Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is crucial for managing this complexity.

Purpose of the Study:

• To examine how AI advances the analysis of SC and ST data.
• To highlight AI applications in biological research and personalized medicine.
• To provide researchers with a guide to applying AI for cellular data interpretation.

Main Methods:

• Focus on AI methods like convolutional neural networks (CNN), graph neural networks (GNN), and variational autoencoders (VAE).
• AI automates preprocessing, dimensionality reduction, cell classification, and clustering.
• AI integrates multi-omics data, identifies spatial patterns, and infers cellular trajectories.

Main Results:

• AI significantly enhances the interpretation of complex SC and ST datasets.
• Applications span cancer biology, immunology, and neuroscience.
• AI aids in predicting cellular behavior and disease mechanisms relevant to personalized medicine.

Conclusions:

• AI is central to unlocking the full potential of SC and ST data.
• Challenges include scalability, interpretability, and data standards.
• Future directions focus on model transparency, multi-modal integration, and ethical considerations.