# Sickle Cell Repurposing — Recovery Test Report

> **Status: DRAFT SCAFFOLD — not yet run.** Filled in during Week 4 from
> `notebooks/05_recovery_test.ipynb`. Target length: ~2 pages, readable by a sceptical
> pharma scientist in 5 minutes.

## Pre-registered success criteria

> ⚠️ **Commit this section to git _before_ running the recovery test** (PLAN.md §8, §10).

The MVP passes if:

- Hydroxyurea ranks in the **top 10%** (top 30 of 300), **AND**
- L-glutamine ranks in the **top 25%** (top 75) **OR** is documented as unscorable due to a
  missing LINCS signature, **AND**
- At least **4 of 5** negative-control drugs rank in the **bottom half**.

_Pre-registered on: TBD (date of commit)_

---

## Section 1 — Methodology

_5–6 sentences: what was built, the GEO dataset used, the drug-set composition, and the
scoring method (CMap connectivity, Lamb 2006 / Subramanian 2017)._

## Section 2 — Recovery test result

| Drug | Rank | Percentile | Pass? |
|---|---|---|---|
| Hydroxyurea | TBD | TBD | TBD |
| L-glutamine | TBD | TBD | TBD |

Negative controls (expected: bottom half):

| Control drug | Rank | Bottom half? |
|---|---|---|
| TBD | TBD | TBD |

**Overall: PASS / FAIL against pre-registered criteria — TBD**

## Section 3 — Top 10 candidates

| Rank | Drug | Score | Known mechanism | Biological plausibility |
|---|---|---|---|---|
| 1 | TBD | TBD | TBD | TBD |

_Note: HDAC inhibitors and broad kinase inhibitors often dominate connectivity rankings due
to widespread expression effects — flag these honestly (PLAN.md §9.4)._

## Section 4 — One non-obvious candidate worth investigating

_A single paragraph on the most interesting result. Language must be careful: this is a
computational hypothesis to test, not a discovery (PLAN.md §9.7)._

## Section 5 — Honest limitations

- Cell-composition confound in whole-blood expression (PLAN.md §9.1)
- LINCS L1000 cell-line limitations — landmark genes measured mostly in cancer lines (§9.2)
- Missing signatures (e.g. L-glutamine) (§9.3)
- No mechanistic validation layer — discovery hypothesis generation, not validated prediction (§9.6)

## Section 6 — What v2 would fix

- Cell-type deconvolution of the disease signature
- Knowledge graph fallback for missing-signature drugs
- A second disease to test generalization (the real test — sickle cell results do not prove
  the platform generalizes, §9.5)