iSC.MEB: an R package for multi-sample spatial clustering analysis of spatial transcriptomics data

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.