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We introduce iSC.MEB, a new tool for analyzing spatial transcriptomics data. This method accurately detects cell types and spatial domains across multiple datasets by integrating batch effect estimation and spatial clustering.

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Spatially resolved transcriptomics (SRT) technologies offer gene expression insights with spatial context.
  • Analyzing multiple SRT datasets presents challenges due to batch effects and the need for integrated spatial analysis.

Purpose of the Study:

  • To introduce iSC.MEB, an extension of SC.MEB for integrated analysis of multiple SRT datasets.
  • To enable simultaneous batch effect estimation and spatial clustering for low-dimensional SRT data representations.

Main Methods:

  • Developed integrated spatial clustering with hidden Markov random field using empirical Bayes (iSC.MEB).
  • Applied an empirical Bayes approach combined with a hidden Markov random field model.
  • Utilized low-dimensional representations of multiple SRT datasets.

Main Results:

  • iSC.MEB effectively estimates batch effects in SRT data.
  • The tool performs accurate spatial clustering and cell/domain detection.
  • Demonstrated robust performance on two independent SRT datasets.

Conclusions:

  • iSC.MEB provides a powerful integrated approach for analyzing multiple SRT datasets.
  • The method enhances the accuracy of cell type and spatial domain identification.
  • iSC.MEB is available as an open-source R package.