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<p>In spatial transcriptomics data, spatially correlated genes promise to reveal high-interest phenomena like cell–cell interactions and latent variables. But in practice, most spatial correlations arise from the spatial arrangement of cell types, obscuring the more interesting relationships we hope to discover. We introduce InSituCor, a toolkit for discovering modules of spatially correlated genes. InSituCor returns only correlations not explainable by already-known factors like the cell type landscape; this spares precious analyst effort. InSituCor supports both unbiased discovery of whole-dataset correlations and knowledge-driven exploration of genes of interest. As a special case, it evaluates ligand-receptor pairs for spatial co-regulation.</p>

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InSituCor: exploring spatially correlated genes conditional on the cell type landscape

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月24日

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  • Patrick Danaher,
  • Dan McGuire,
  • Lidan Wu,
  • Michael Patrick,
  • David Kroeppler,
  • Haiyan Zhai,
  • Deniz G. Olgun,
  • Dennis Gong,
  • Jingyi Cao,
  • William L. Hwang,
  • Joachim Schmid,
  • Joseph M. Beechem

摘要

In spatial transcriptomics data, spatially correlated genes promise to reveal high-interest phenomena like cell–cell interactions and latent variables. But in practice, most spatial correlations arise from the spatial arrangement of cell types, obscuring the more interesting relationships we hope to discover. We introduce InSituCor, a toolkit for discovering modules of spatially correlated genes. InSituCor returns only correlations not explainable by already-known factors like the cell type landscape; this spares precious analyst effort. InSituCor supports both unbiased discovery of whole-dataset correlations and knowledge-driven exploration of genes of interest. As a special case, it evaluates ligand-receptor pairs for spatial co-regulation.