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Electrical current is defined as the rate at which charge flows. When there is a large current present, such as that used to run a refrigerator, a large amount of charge moves through the wire in a small amount of time. If the current is small, such as that used to operate a handheld calculator, a small amount of charge moves through the circuit over a long period of time. The SI unit for current is the ampere (A), named for the French physicist André-Marie Ampère (1775–1836).
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Related Experiment Video

Updated: Feb 13, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.

Boris Tchatchoua Ngassam1, Huilin Niu1, Sunny Pang1

  • 1Department of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada.

Frontiers in Bioinformatics
|February 12, 2026
PubMed
Summary

Artificial intelligence (AI) is revolutionizing bioinformatics, especially for single-cell and spatial transcriptomics. This review assesses AI methods for 10 key analysis tasks, guiding researchers on practical applications.

Keywords:
cell-cell interactionscross-dataset integrationdata denoisingdeconvolutiondimensionality reductionintegrating single-cell and spatial transcriptomics modalitiestranscriptional velocity

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Artificial intelligence (AI) is increasingly utilized in bioinformatics, with numerous methods emerging recently.
  • High-throughput single-cell and spatial transcriptomics are prime areas for AI due to deep learning's data requirements.

Purpose of the Study:

  • To review the application of AI in single-cell and spatial transcriptomics analysis.
  • To compare AI approaches with traditional statistical or heuristic methods.
  • To identify AI tools suitable for discovery researchers versus those not yet ready for general use.

Main Methods:

  • Review of AI applications across 10 common transcriptomics analysis tasks.
  • Exploration of dimensionality reduction, data integration, denoising, and augmentation.
  • Assessment of AI for deconvolution, cell-cell interactions, transcriptional velocity, and multi-modal data integration.

Main Results:

  • AI methods are being applied to a wide range of single-cell and spatial transcriptomics tasks.
  • The review categorizes AI algorithms based on their readiness for general research use.
  • Specific AI tools are highlighted for their potential utility in discovery research.

Conclusions:

  • AI offers powerful tools for advancing single-cell and spatial transcriptomics research.
  • Researchers should be aware of the current limitations and developmental stages of various AI methods.
  • The field is rapidly evolving, with AI poised to become indispensable in transcriptomic data analysis.