spatial-mapping-tangram
Installation
SKILL.md
Spatial Mapping with Tangram
Overview
Tangram fits a cell-by-spot probability matrix so that the expression of the mapped single cells reproduces the spatial expression of shared training genes. Every Tangram task starts from that matrix: per-spot cell type proportions, predicted expression of genes the panel did not measure, and, with a histology image, a cell type for every segmented nucleus. The mapping always converges to something, so this skill validates it on genes held out of training before any result is reported. The reference must come from the same tissue and state as the section and share informative genes with it.
| Task | Route |
|---|---|
| Cell type proportions per spot / labels on single-cell-resolution data | Workflow Steps 1-7 |
| Predict unmeasured or dropout-affected genes | Steps 1-6, then Route: Gene Imputation |
| One cell type per segmented nucleus (Visium + full-resolution image) | Route: Nuclei Deconvolution (replaces Step 1, then Steps 2-3, Nucleus 2-4, Step 6) |
| Deeper quality checks (k-fold CV, cells vs clusters, seed stability) | After Step 6, Route: Advanced Validation |