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

Sangeeta Singh¹, Sonu Kumar², Dinesh Gupta²

¹Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, India; ICAR-Indian Grassland and Fodder Research Institute, Jhansi, Uttar Pradesh, India.

Progress in Molecular Biology and Translational Science

|April 15, 2026

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Artificial intelligence (AI) enhances the analysis of large single-cell (SC) and spatial transcriptomics (ST) datasets. AI methods like deep learning automate processing, enabling deeper insights into cellular diversity and disease mechanisms.

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).

Keywords:

Artificial Intelligence Deep learning Single-cell Spatial transcriptomics

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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.