Artificial Intelligence in single-cell and spatial transcriptomics data analyses

Single-cell (SC) and spatial transcriptomics (ST) have transformed molecular biology by allowing high-resolution profiling of gene expression in individual cells and tissues. These approaches reveal cellular diversity, developmental pathways, and disease processes, yet the resulting datasets are large and complex. Artificial intelligence (AI) especially machine learning (ML) and deep learning (DL) now plays a central role in managing this complexity by automating preprocessing, reducing dimensionality, and supporting cell classification and clustering. AI methods also help integrate multi-omics layers, identify spatial patterns, and infer cellular trajectories, strengthening our ability to interpret biological systems. This chapter examines how AI advances the analysis of single-cell and spatial transcriptomics, focusing on methods such as convolutional neural networks (CNN), graph neural networks, (GNN) and variational autoencoders (VAE). It highlights applications in cancer biology, immunology, and neuroscience, including the prediction of cellular behavior and disease mechanisms relevant to personalized medicine. Remaining challenges include scalability, interpretability, and consistent data standards. The chapter concludes with future directions aimed at improving model transparency, enhancing multi-modal integration, and addressing ethical issues in clinical use, offering researchers a concise guide to applying AI for deeper insights into cellular data.