# Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases

## TL;DR

This review explores how AI is transforming the analysis of single-cell and spatial transcriptomics data, offering new tools and insights for researchers.

## Contribution

The paper provides a comprehensive survey of AI applications in transcriptomics and offers practical guidance for model selection and tool development.

## Key findings

- AI enhances scalability and integration in analyzing large-scale transcriptomic data.
- AI models show promise in tasks like trajectory inference and spatial domain detection.
- The review identifies key innovations and challenges in applying AI to transcriptomics.

## Abstract

Single‐cell RNA sequencing (scRNA‐seq) and spatial transcriptomics (ST) have revolutionized the study of cellular heterogeneity and tissue organization. However, the increasing scale and complexity of these data demand more powerful and integrative computational strategies. Although conventional statistical and machine learning methods remain effective in specific contexts, they face limitations in scalability, multimodal integration, and generalization. In response, artificial intelligence (AI) has emerged as a transformative force, enabling new modes of analysis and interpretation. In this review, we survey AI applications across the transcriptomic analysis workflow—from initial preprocessing through key downstream analyses such as trajectory inference, gene regulatory network reconstruction, and spatial domain detection. For each analytical task, we trace the developmental trajectory and evolving trends of AI models, summarize their advantages, limitations, and domain‐specific applicability. We also highlight key innovations, ongoing challenges, and future directions. Furthermore, this review provides practical guidance to assist researchers in model selection and support developers in the design of novel AI tools. An online companion supplement providing an in‐depth look at all methods discussed: https://zhanglab‐kiz.github.io/review‐ai‐transcriptomics.

Single‐cell RNA sequencing and spatial transcriptomics have unveiled cellular heterogeneity and tissue organization with unprecedented resolution. Artificial intelligence (AI) now plays a pivotal role in interpreting these complex data. This review systematically surveys AI applications across the entire analytic workflow and offers practical guidance to assist researchers in model selection and support developers in designing novel AI tools.