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Transcriptome Analysis of Single Cells
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Pairpot: a database with real-time lasso-based analysis tailored for paired single-cell and spatial transcriptomics.

Zhihan Ruan1, Fan Lin1, Zhenjie Zhang1

  • 1Centre for Bioinformatics and Intelligent Medicine, College of Computer Science, Nankai University, No.38 Tongyan Road, 300350 Tianjin, China.

Nucleic Acids Research
|November 4, 2024
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Summary

Pairpot is a new database for paired single-cell and spatially resolved transcriptomics (SRT) data. It offers real-time analysis to uncover biological insights from complex spatial gene expression patterns.

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Area of Science:

  • * Computational Biology
  • * Genomics
  • * Bioinformatics

Background:

  • * Paired single-cell and spatially resolved transcriptomics (SRT) data offer complementary insights into biological processes and disease mechanisms.
  • * Existing SRT databases lack sufficient single-cell and SRT (SC-SP) pairs and real-time analytical capabilities, limiting biological discovery.
  • * Uncovering potential biological insights from integrated SC-SP data requires robust curation and advanced analytical tools.

Purpose of the Study:

  • * To develop Pairpot, a novel database for paired single-cell and SRT data with integrated real-time heuristic analysis.
  • * To curate a comprehensive collection of high-quality SC-SP pairs and associated datasets.
  • * To enable efficient identification of heterogeneous domains and cell proportions within spatial transcriptomic data.

Main Methods:

  • * Curated 99 high-quality SC-SP pairs, encompassing 1,425,656 spots from 299 datasets.
  • * Integrated multiple tissue slices and established associations between single-cell and SRT data.
  • * Implemented semi-supervised learning for real-time heuristic analysis: Lasso-View (SRT domain refinement), Pair-View (cell proportion inference), and Layer-View (3D hierarchical visualization).

Main Results:

  • * Successfully curated 99 high-quality SC-SP pairs and constructed association networks.
  • * Developed and validated real-time heuristic analysis tools (Lasso-View, Pair-View, Layer-View) for SC-SP data.
  • * Demonstrated Pairpot's efficiency in identifying heterogeneous spatial domains and inferring cell proportions.

Conclusions:

  • * Pairpot provides a valuable resource for researchers studying spatial biology and disease mechanisms.
  • * The database and its real-time analysis tools facilitate deeper understanding of cellular heterogeneity in spatial contexts.
  • * Pairpot enhances the utility of paired single-cell and SRT data for biological discovery.