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Sampling and ranking spatial transcriptomics data embeddings to identify tissue architecture.

Yu Lin1,2, Yan Wang1,3, Yanchun Liang3,4

  • 1School of Artificial Intelligence, Jilin University, Changchun, China.

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|August 29, 2022
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Summary

Selecting the best spatial transcriptomics data embeddings is crucial for accurate tissue analysis. Our new method, MP-MIM, uses message passing and spatial autocorrelation to identify optimal embeddings, improving deep learning tool performance.

Keywords:
deep learningembedding evaluationmessage passingspatial autocorrelationspatial transcriptomicstissue architecture

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Spatial transcriptomics enables tissue architecture and function analysis.
  • Computational methods generate embeddings for clustering and segmentation.
  • Embedding performance varies with data distribution, necessitating selection for new datasets.

Purpose of the Study:

  • To develop a method for evaluating and selecting effective embeddings for spatial transcriptomics data.
  • To enhance the usability of deep learning tools in spatial transcriptome analysis.

Main Methods:

  • Developed message passing-Moran's I with maximum filtering (MP-MIM) for embedding evaluation.
  • Applied graph convolution to aggregate spatial transcriptomics data.
  • Utilized global Moran's I to measure spatial autocorrelation and select optimal embeddings.

Main Results:

  • MP-MIM accurately identifies high-quality embeddings for spatial transcriptomics data.
  • Selected embeddings show a high correlation between predicted and ground truth tissue architecture.
  • Validated on sixteen human brain spatial transcriptomics samples.

Conclusions:

  • MP-MIM offers a novel approach to pre-select optimal embeddings for new spatial transcriptomics data.
  • The method improves the reliability and application of deep learning in analyzing tissue architecture.