domains / families / science / spatial-domains
Identify spatially coherent tissue domains (regions like cortical layers, tumor vs stroma) in Visium, Visium HD, Xenium, MERFISH, Slide-seq, and Stereo-seq data with Squidpy, BANKSY, BayesSpace, STAGATE, and GraphST. Use when distinguishing a domain (a region with many cell types) from a cell type (one cell's identity) and a niche (local cell-type composition); choosing a domain method by tissue geometry (laminar/continuous vs high-resolution imaging vs non-contiguous); tuning the spatial-weight knob (BANKSY lambda, BayesSpace smoothing, SpaGCN histology weight, GNN graph radius) to avoid over-smoothing into blobs or under-smoothing into salt-and-pepper; choosing the number of domains k as a biological decision with k+-1 sensitivity; and reading the Yuan 2024 benchmark with the DLPFC continuous-laminar caveat.
The two skills cover the same task but with different depth and scope. The first copy (OpenClaw) is a practical, code-heavy recipe for Squidpy/Scanpy, including a specific combined-graph implementation, a custom smoothing function, and marker/annotation steps. The second copy (GPTomics) is a conceptual and methodological guide that distinguishes domains from niches, provides a decision matrix for choosing between BayesSpace, BASS, STAGATE, GraphST, SpaGCN, and BANKSY, and emphasizes the 'spatial-weight' knob and biological choice of k. The first copy lacks the method-selection logic and the domain/niche distinction, while the second copy lacks the specific Python implementation for combined graphs and the custom smoothing code. Combining them provides both the 'how to choose' and the 'how to code' aspects.