Points2regions: Fast Interactive Clustering Of In Situ Transcriptomics Data

Andersson, A.; Behanova, A.; Avenel, C.; Wählby, C.; Malmberg, F.

doi:10.1101/2022.12.07.519086

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bioRxiv · 10.1101/2022.12.07.519086

Points2regions: Fast Interactive Clustering Of In Situ Transcriptomics Data

Andersson, A.·Behanova, A.·Avenel, C.·Wählby, C.·Malmberg, F.

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Abstract

Imaging-based spatial transcriptomics techniques generate image data that, once processed, results in a set of spatial points with categorical labels for different mRNA species. A crucial part of analyzing downstream data involves the analysis of these point patterns. Here, biologically interesting patterns can be explored at different spatial scales. Molecular patterns on a cellular level would correspond to cell types, whereas patterns on a millimeter scale would correspond to tissue-level structures. Often, clustering methods are employed to identify and segment regions with distinct point-patterns. Traditional clustering techniques for such data are constrained by reliance on complementary data or extensive machine learning, limiting their applicability to tasks on a particular scale. This paper introduces Points2Regions, a practical tool for clustering spatial points with categorical labels. Its flexible and computationally efficient clustering approach enables pattern discovery across multiple scales, making it a powerful tool for exploratory analysis. Points2Regions has demonstrated efficient performance in various datasets, adeptly defining biologically relevant regions similar to those found by scale-specific methods. As a Python package integrated into TissUUmaps and a Napari plugin, it offers interactive clustering and visualization, significantly enhancing user experience in data exploration. In essence, Points2Regions presents a user-friendly and simple tool for exploratory analysis of spatial points with categorical labels.

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Andersson, A., Behanova, A., Avenel, C., Wählby, C., Malmberg, F.. 2022-12-10. Points2regions: Fast Interactive Clustering Of In Situ Transcriptomics Data. https://doi.org/10.1101/2022.12.07.519086

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