Every one of these was run end to end on public data, and the script that produced it is downloadable. Nothing here is illustrative: if a number appears, it came out of the pipeline — including the ones that are inconvenient for us.
2,638 cells, 6 clusters, 0% unassigned — and the annotation cross-checked blind against a reference classifier (6/6 agreement).
4,025 spots, 10 spatial domains; the top spatially variable genes recover known lymph-node architecture without being told what to look for.
DESeq2 on public GEO data with the design read from the source metadata; milk-protein genes top the result, which is the biological check that the pipeline behaved.
On 8-donor data the two approaches disagree on 68.3% of differential-expression calls; all 10 canonical interferon genes are recovered by the design-aware analysis.
Donor clustering reduced 20.2% while the treatment signal moved -0.1%. Most integration reports show only the first number.
QC thresholds re-run at three settings (ARI 0.894/0.872); the permissive setting recovers a platelet population the strict one deletes. Plus real doublet detection.
"Cell type X increased after treatment" is the claim most often made wrongly. This one is tested at subject level.
A self-contained report as it actually arrives: figures embedded, QC table, annotation cross-check, a manuscript-ready methods paragraph, and a stated limitations section.
Start with the free QC report (no sign-up, file deleted after the report is built), or build a plan to see which modules your design actually needs.