Single-Cell Analysis — Foundation Learning Guide¶
For a reader with no background in this area. If you have watched the Foundations film series, this follows directly from it.
The one idea to hold¶
Cluster first, then name. Cells that behave alike group together; matching those groups to known marker genes tells you what they are.
What this subject does with that idea¶
Takes a sample of thousands of individual cells and works out how many distinct cell types are present.
Bulk measurements average across cell types and hide the population that matters. Single-cell resolution recovers it.
Decision support for a qualified clinician — never autonomous diagnosis or prescribing. Every output on this page is intended to inform a clinician's judgement, not replace it.
What it cannot do¶
Cluster annotation is by marker-gene overlap against a canonical panel. It is a strong heuristic, not a ground truth.
Stating the limit is not a disclaimer bolted on the end — it is how you tell a tool that helps from a tool that misleads.
Vocabulary you will meet¶
| Term | Plain meaning |
|---|---|
| Capability | One named thing the platform can do, registered in capabilities.json |
live / planned |
Whether a capability actually answers today, or is intended |
| Decision support | Output that informs a clinician; it never decides |
| LIVE / REPRESENTATIVE / BURST | Whether a demo ran now, was pre-computed, or ran on remote GPUs |
Try it¶
The demo for this subject is E8:
.venv/bin/python scripts/run_demo.py E8
If it reports BLOCKED, the message names exactly what is missing. That is intentional — a demo
that cannot run says so rather than showing you something canned.