# Redline

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Pablo Manjarres](https://cerebralvalley.ai/u/pablomanjarres)
- **GitHub:** https://github.com/pablomanjarres/redline
- **Demo video:** https://youtu.be/H3jq20gI7cI
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/162

Single-cell RNA-seq conclusions fail peer review on the statistics while the biology holds up. A standard pipeline treats 51,842 cells from four donors as 51,842 independent samples, so a marker gene looks significant at p = 6.2e-11, until a proper per-donor test puts it at 0.21. Reviewer 2 catches that. You didn't.

Redline audits those statistics on your own data before you submit. Hand it your .h5ad and the analysis you ran; it re-runs the load-bearing tests, marks the false discoveries on your own figures, cites the method that fixes each, and hands back corrected code that runs on your data. It flags eight error classes that QC tools and generic reviewers skip.

The honest number is a false-positive gap. On a 46-case benchmark of planted errors and clean controls, redline stays quiet on clean data at 0% false positives. One Claude call given the same write-up cries wolf 74% of the time. Both arms catch nearly every planted error, so the number that matters is 0 against 74.

Live demo, no API keys: https://science-redline.vercel.app

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/162. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt