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Abstract:
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A key analytic task in spatial transcriptomics is to identify genes or genomic features that display spatial as well as differential expression patterns. To model the technological variability in cross-platform spatial transcriptomics data, here we propose to use self-adaptive spatial models that can flexibly capture a large dynamic range of observed spatial expression profiles across experimental platforms induced by platform- and gene-specific statistical properties such as heavy tails, sparsity, and gene expression distributions. Empirical evidence of the attractiveness of the method is demonstrated via extensive simulation studies and real data analyses. We also apply our method to structurally similar digital pathology data thus providing a unified modeling framework for both imaging-based and sequencing-based spatial molecular profiles.
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