BioC2024

Spatial transcriptomics platforms enable the measurement of transcriptome-scale gene expression levels at spatial resolution, and have become widely applied to study spatial variation in cell type composition and gene expression patterns within tissue samples. Depending on the technological platform, the spatial resolution of the measurements may either be at molecular resolution or consist of pooled measurements from one or more cells per spatial location, and measurements may also be characterized by high levels of sparsity due to sampling variation. The identification of spatial domains consisting of tissue regions with relatively uniform cell type composition or mixtures and consistent gene expression signatures represents a key step during computational analysis workflows. Spatial domains may then be further investigated by applying tools for cell type compositional analyses or differential analyses. We have developed a new method, smoothclust, to identify spatial domains in spatial transcriptomics data in an interpretable and computationally scalable manner, based on spatial smoothing of gene expression values followed by unsupervised clustering. We have evaluated the method using data from several technological platforms and compared against existing and baseline methods. The method is available as an R package from Bioconductor and is integrated into Bioconductor-based analysis workflows for spatial transcriptomics data.