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<p>Spatial omics technologies localize individual molecules at subcellular resolution, yet growing numbers of molecules, features and replicates set analysis challenges. We present <i>smoppix</i>, a nonparametric analysis method based on the probabilistic index, to test for several uni- and bivariate localization patterns. It exploits the high-dimensionality of the data for variance weighting and for providing a background null distribution, unique for every molecule. Moreover, <i>smoppix</i> sidesteps segmentation, edge correction, warping and density estimation, and is scalable thanks to an exact permutation null distribution. We unearth spatial patterns in datasets from four kingdoms, and validate some findings experimentally on spikemoss roots.</p>

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smoppix: unified nonparametric analysis of single-molecule spatial omics data using probabilistic indices

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月2日

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  • Stijn Hawinkel,
  • Xilan Yang,
  • Ward Poelmans,
  • Hans Motte,
  • Tom Beeckman,
  • Steven Maere

摘要

Spatial omics technologies localize individual molecules at subcellular resolution, yet growing numbers of molecules, features and replicates set analysis challenges. We present smoppix, a nonparametric analysis method based on the probabilistic index, to test for several uni- and bivariate localization patterns. It exploits the high-dimensionality of the data for variance weighting and for providing a background null distribution, unique for every molecule. Moreover, smoppix sidesteps segmentation, edge correction, warping and density estimation, and is scalable thanks to an exact permutation null distribution. We unearth spatial patterns in datasets from four kingdoms, and validate some findings experimentally on spikemoss roots.