omics-target-mining

An end-to-end, reproducible pipeline for mining public omics datasets (GEO, ArrayExpress, PRIDE, CELLxGENE) to nominate and prioritize therapeutic targets for a disease — going from "here is a condition" to "here is a ranked, druggability-annotated target shortlist" using only public data.

It is packaged as a Claude Science skill (SKILL.md + a kernel.py sidecar), but the two core statistics in kernel.py are plain, dependency-light Python functions that work on their own in any analysis.

What it does

Ten stages, each ending by saving artifacts:

1. Dataset inventory — query GEO / ArrayExpress / PRIDE / CELLxGENE, build one master table, audit for keyword-only false positives.
2. Tier & prioritize — score datasets on relevance, analytical role, tractability (Tier 1 bulk case-control → Tier 3 complementary).
3. Retrieve & QC Tier-1 bulk — harmonize IDs to gene symbols, normalize, PCA + canonical-marker sanity checks.
4. Per-study differential expression — variance-moderated Welch t-test with BH-FDR (moderated_de).
5. Cross-study meta-analysis — DerSimonian–Laird random-effects pooling with heterogeneity stats (meta_analysis).
6. Single-cell validation — scanpy QC → HVG → Harmony → Leiden → composition shift + per-cell-type DE.
7. Pathway / regulator / cell-cell communication — Enrichr (GO_BP, Reactome, Hallmark, TF libraries) + LIANA.
8. Multi-omics integration — per-gene evidence table across independently-varying layers.
9. Druggability — Open Targets GraphQL: tractability, clinical candidates, subcellular location, target class.
10. Consolidate — Tier-A shortlist with mechanism rationale and a suggested modality per target.

The kernel.py helpers (usable standalone)

Two functions, depending only on numpy, pandas, scipy, and statsmodels:

from kernel import moderated_de, meta_analysis

# Stage 4: per-study DE on log2 expression (genes x samples)
de = moderated_de(expr, labels, case="disease", control="control")
#   -> DataFrame: gene, log2FC, t, pval, padj  (variance-moderated Welch t + BH-FDR)

# Stage 5: pool per-study effects (genes x studies of log2FC and SE)
meta = meta_analysis(fc_df, se_df, min_studies=3)
#   -> DataFrame: pooled_log2FC, se, z, pval, padj, Q, I2, k, n_up, n_dn, direction
#      (DerSimonian-Laird random-effects meta-analysis)

Install

pip install -r requirements.txt

The full pipeline additionally uses requests, openpyxl, GEOparse, gseapy, mygene, and — for single-cell stages — scanpy, leidenalg, harmonypy, and liana.

Using it as a Claude Science skill

Drop the omics-target-mining/ folder (containing SKILL.md and kernel.py) into your skills directory, or publish it via the skill SDK. When the skill loads, moderated_de and meta_analysis are auto-defined in the kernel and SKILL.md drives the 10-stage workflow.

License

MIT — see LICENSE.