Tutorials#
The tutorials hub brings the canonical pyXenium tutorial series into one
place. The notebooks below use real pyXenium study outputs to explain what
each module does, how to rerun it on local Xenium data, and why the result
matters biologically. The final entries document optional bridges from
pyXenium’s Xenium data foundation to external spatho and SpatialPerturb
workflows.
Load a public renal RNA + protein Xenium study, inspect the XeniumSlide
components, and see how I/O preserves the structures needed for downstream
biology.
Walk through Atera WTA breast cell-cell interaction topology outputs, then compare whole-dataset benchmark results across spatial and non-spatial CCI methods.
Compare pathway topology aggregation with activity point-cloud scoring on the same breast topology study and connect the scores to cell-state programs.
Choose among renal RNA + protein analysis, RNA + contour + H&E discovery, and the breast H&E morphology/image-feature pilots on PDC or A100.
Generate HistoSeg-backed annotations, run contour-level transcript and cell composition analyses, and profile barrier-aware boundary density curves.
Use S1/S5 contours as samples for GMI sparse effect modeling, then turn completed runs into supervised spatial gene modules.
Run the Atera WTA breast S1/S5 mechanostress workflow on PDC and inspect fibroblast axis strength, tumor-stroma growth states, polarity, and coupling artifacts.
Install and call the external spatho workflow, and see how pyXenium’s
XeniumSlide structure supports AI-driven spatial pathology without adding
spatho code to pyXenium.
Write a SpatialPerturb Bridge handoff spec for Perturb-seq reference projection onto Xenium tissue without adding SpatialPerturb as a core pyXenium dependency.
Tutorial Pattern#
The research-facing notebook tutorials generally follow this structure:
OverviewBiological questionDatasetSetupCore workflowVisual outputsBiological interpretationCaveatsNext steps