Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.

Ge S, Sun S, Xu H, Cheng Q, Ren Z

DOI: 10.1093/bib/bbaf136
Published: 2025 Mar 4
Container: Briefings in bioinformatics
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1093/bib/bbaf136,
  title = {Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.},
  author = {Ge S and Sun S and Xu H and Cheng Q and Ren Z},
  year = {2025},
  journal = {Briefings in bioinformatics},
  doi = {10.1093/bib/bbaf136},
  url = {https://doi.org/10.1093/bib/bbaf136}
}

RIS

TY  - JOUR
TI  - Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.
AU  - Ge S
AU  - Sun S
AU  - Xu H
AU  - Cheng Q
AU  - Ren Z
PY  - 2025
JO  - Briefings in bioinformatics
DO  - 10.1093/bib/bbaf136
UR  - https://doi.org/10.1093/bib/bbaf136
ER  -

APA

S, G., S, S., H, X., Q, C., & Z, R. (2025). Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.. Briefings in bioinformatics. https://doi.org/10.1093/bib/bbaf136

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pubmed · retrieved 2026-09-27T01:15:15.205Z