Computational Methods for Spatial Biomedical Data Analysis

Molecular profiling technologies such as genome sequencing have transformed life sciences. The development of innovative spatial transcriptomics techniques, which enable transcriptome measurement in high spatial resolution, has achieved significant breakthroughs in recent years. Due to the enormous potential to deepen our understanding of molecular mechanisms, these new spatial molecular profiling technologies are rapidly gaining attention, and a massive amount of gene expression data with spatial information have been generated.

The emerging spatial transcriptomics data will undoubtedly lead to an explosion of innovations in spatial statistics, an area fueled by breakthrough technologies in data collection and rooted in model-driven statistical research. To fully understand the diversity and potential impacts of spatial distributions of gene expression, customized statistical models are urgently needed. Our goal is to trigger new methodological developments in statistics via collecting advanced Bayesian and frequentist statistical methods for analyzing spatial transcriptomics data. Besides, we foresee the new computation methods will deepen our knowledge of biological mechanisms.

Areas of interest for this Research Topic include, but not limited to: spatial transcriptomics data curation, global and local spatially variable gene identification, spatial clustering, cell type deconvolution, generation of super-resolution gene expression, expression and histology integrated tissue annotation, gene expression prediction from histology, spatial location recovery of single cells, etc. All types of manuscripts, including survey, theoretical, methodological, application, and software, are welcomed.