Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges
Boris Tchatchoua Ngassam, Huilin Niu, Sunny Pang, Valeryia Shydlouskaya, Tallulah S. Andrews · 2026
Read Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges by Boris Tchatchoua Ngassam, Huilin Niu, Sunny Pang, Valeryia Shydlouskaya, Tallulah S. Andrews (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12886477.txt.
Description
Boris Tchatchoua Ngassam and co-authors review AI tools for single-cell and spatial gene data, published in 2025. Hundreds of methods have appeared in recent years. Such data sets feed deep learning well, so the area drew heavy AI interest. The review covers ten common analysis tasks.
Tasks include shrinking dimensions, merging data sets, and cleaning noise. Others cover guessing cell mixtures and tracing cell chats. The authors compare AI routes with classic statistics on each task. They flag which tools are ready for discovery work and which are not. Honest sorting helps labs spend effort where AI truly helps.
Catalog record
| CREATOR | Boris Tchatchoua Ngassam, Huilin Niu, Sunny Pang, Valeryia Shydlouskaya, Tallulah S. Andrews |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 92 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |