ArticleBioinformatics advances2023
iSC.MEB: an R package for multi-sample spatial clustering analysis of spatial transcriptomics data.
Article in Bioinformatics advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A comprehensive comparison on clustering methods for multi-slice spatially resolved transcriptomics data analysis.Briefings in bioinformatics · 2025Article
- Benchmarking spatial transcriptomics technologies with the multi-sample SpatialBenchVisium dataset.Genome biology · 2025Article
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Authors and funding
4 authors.
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Abstract
Summary: Emerging spatially resolved transcriptomics (SRT) technologies are powerful in measuring gene expression profiles while retaining tissue spatial localization information and typically provide data from multiple tissue sections. We have previously developed the tool SC.MEB-an empirical Bayes approach for SRT data analysis using a hidden Markov random field. Here, we introduce an extension to SC.MEB, denoted as integrated spatial clustering with hidden Markov random field using empirical Bayes (iSC.MEB) that permits the users to simultaneously estimate the batch effect and perform spatial clustering for low-dimensional representations of multiple SRT datasets. We demonstrate that iSC.MEB can provide accurate cell/domain detection results using two SRT datasets. Availability and implementation: iSC.MEB is implemented in an open-source R package, and source code is freely available at https://github.com/XiaoZhangryy/iSC.MEB. Documentation and vignettes are provided on our package website (https://xiaozhangryy.github.io/iSC.MEB/index.html). Supplementary information: Supplementary data are available at
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Registered trials
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