AnnData
AnnData is a Python package for handling annotated data matrices, storing experimental measurements (X) alongside observation metadata (obs)…
Maintainer FreedomIntelligence · Last updated April 1, 2026
Analyze high-resolution spatial platforms like Slide-seq, Stereo-seq, and Visium HD. Use when working with subcellular resolution or high-density spatial data.
Original source
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-spatial-transcriptomics-spatial-multiomics
Skill Snapshot
Source Doc
| Platform | Resolution | Spots/Beads | Coverage |
|---|---|---|---|
| Visium | 55 µm | ~5,000 | Tissue-wide |
| Visium HD | 2 µm | ~11M | Subcellular |
| Slide-seq | 10 µm | ~100,000 | High-density |
| Stereo-seq | 0.5 µm | >200M | Subcellular |
| MERFISH | Single-molecule | N/A | Targeted genes |
Goal: Run standard spatial analyses (autocorrelation, neighborhood enrichment, ligand-receptor) on high-resolution spatial data.
Approach: Adjust neighbor graph density for high-resolution platforms, then apply standard Squidpy workflows.
import squidpy as sq
import scanpy as sc
## Spatial neighbors (for high-resolution, adjust n_neighs based on density)
sq.gr.spatial_neighbors(adata, coord_type='generic', n_neighs=10, spatial_key='spatial')
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