gabrielmoreira 🩸 Rare-Disease Blood RNA-seq Outlier Detection
Reproduces the diagnostic principle of the Genomics England NGRL paper (Blood-based RNA-Seq of 5,412 individuals, medRxiv 2026.03.19.26348811). For each case sample, scores per-gene expression against a control reference panel and flags candidates falling in a curated dosage-sensitive disease-gene panel.
When To Use
- A WGS-negative or WGS-VUS rare-disease patient with a paired blood RNA-seq sample
- A clinical bioinformatician triaging candidate diagnoses before MDT review
- A population-biobank team building an ancestry-matched control reference for outlier calling (e.g. Qatar Biobank for Sidra paediatric cases)
Method
Per-gene robust outlier scoring on log2(CPM+1):
- Library-size normalise (CPM), log-transform
- For each gene: compute median and MAD across the control panel
- For each case-gene cell: modified z = 0.6745 (x − median) / MAD
- Flag |z| ≥ threshold (default 3) and gene in disease panel
- Rank by |z|, separate down-outliers (haploinsufficiency-consistent) from up-outliers
This implements the diagnostic principle of OUTRIDER (per-gene outlier vs control panel) without the autoencoder, so it runs in seconds with no R/Bioconductor stack. For clinical-grade calls swap to the full DROP pipeline (gagneurlab/drop) which adds OUTRIDER's denoising autoencoder, FRASER2 splicing outliers, and confounder correction. The skill's I/O contract is the same so the upgrade is drop-in.
Input Contract
- Counts matrix (
.csvor.tsv): rows = genes (HGNC symbol), columns = sample IDs - Cases file (
.txt): one case sample ID per line - Controls file (
.txt): one control sample ID per line (typically n ≥ 50) - Disease panel (optional,
.csvwithgeneandmechanismcolumns): defaults to a built-in 50-gene haploinsufficient panel
Output Structure
rdoutlier_report/
├── report.md # per-case candidate diagnoses + clinical narrative
├── result.json # standard ClawBio envelope
├── figures/
│ └── case_outlier_heatmap.png # z-scores across cases × top genes
├── tables/
│ ├── outlier_calls.csv # all flagged outliers with z-score, direction, mechanism
│ └── per_gene_stats.csv # control median + MAD per gene
└── reproducibility/
├── commands.sh
├── environment.yml
└── checksums.sha256
Demo
python clawbio.py run rdoutlier --demo
Generates 100 synthetic Gulf-ancestry control samples + 2 cases with injected outliers (FBN1 down, NF1 up) across a 200-gene panel. Demonstrates the diagnostic loop end-to-end in seconds.
Production Path (Sidra / QBB Reference)
| Component | Demo | Production |
|---|---|---|
| Aligner + quantifier | none (synthetic counts) | STAR + featureCounts (or Salmon) |
| Outlier algorithm | robust per-gene z-score | OUTRIDER autoencoder + FRASER2 splicing |
| Control panel | 100 synthetic samples | QBB n≈12K PAXgene blood RNA-seq |
| Confounder correction | none | DROP pipeline (RIN, batch, hidden factors) |
| Disease panel | 50 haploinsufficient genes | ClinGen haploinsufficient + PanelApp |
| Return-of-result loop | report.md | Sidra MDT reflex from WGS-negative referrals |
Safety
- Local-only processing, no network calls in core pipeline
- Compatible with secure research environments (Genomics England RE pattern; Sidra clinical genomics environment)
- Disclaimer required on every report
Disclaimer
ClawBio is a research and educational tool. It is not a medical device and does not provide clinical diagnoses. Consult a healthcare professional before making any medical decisions.