The problem. Spatial transcriptomics adds coordinates to expression, and with them a new stack of computational problems that don’t exist in dissociated single-cell data. Before touching any one method I wanted the map — what are the questions, and what families of methods answer them?

The idea. This review organizes the field by task. Spatial clustering / domain detection — grouping spots or cells into tissue regions using expression and neighborhood. Deconvolution — for spot-based assays (Visium), estimating the cell-type mixture inside each spot from a single-cell reference. Spatially variable genes — finding genes whose expression depends on location, not just cell type. Cell–cell interaction / communication — inferring signalling from spatial co-occurrence. For each it lays out the statistical and ML approaches and their assumptions.

Why it matters. This is the orientation read for the spatial track — the one that gives me the shared vocabulary a core-facility conversation assumes (domains, deconvolution, SVGs, niches). It also frames spatial analysis the way I already think about single-cell: a pipeline of decision points, each with defensible defaults and failure modes, which is exactly the reproducibility-and-standards lens a facility cares about.

Verdict. As a review there’s no single result to stress-test, and being 2022 it predates the imaging-platform benchmarks and the newest domain methods (BANKSY, the 2025 comparisons) — so it’s the map, not the frontier. But for loading the concepts in one pass it’s ideal, and it’s the natural first post of a spatial series before zooming into deconvolution or segmentation. Read it the way I read the synbio field-map: to place everything that follows.