Omics target mining

Omics target mining

A reproducible pipeline that goes from "here is a disease" to "here is a ranked, druggability-annotated therapeutic-target shortlist" using only public data. Ten stages; each ends by saving artifacts.

Cross-disorder by design. Every stage parameterizes on the disease name, the case/control contrast, and the marker sets — nothing is hardwired to one condition. Developed on EoE; reruns for any inflammatory/immune disorder (other EGIDs, celiac, IBD, autoimmune disease) by swapping the disease terms, the Tier-1 dataset filter, and the canonical-marker panel. The EoE-derived notes below (granulocyte dropout, HLA false-positives, marker-direction heterogeneity) are stated as general gotchas, not disease specifics.

Composes with: antigen-epitope-pipeline (turn nominated antigens into HLA-resolved epitope maps for antigen-specific dx/tx) and systematic-review-orchestration (ground the target rationale in a tiered literature base). Together these three are the reusable methodology stack for target discovery in a new inflammatory disorder.

Two bespoke statistics ship in kernel.py and are auto-loaded when this skill loads: moderated_de(expr, labels) and meta_analysis(fc_df, se_df).

When to use

• "Find novel targets for <disease> from public data."
• "Build a cross-study expression signature for <condition>."
• "Which dysregulated genes are druggable / antibody-tractable?"

Environment

Create a dedicated env (heavy stack): pandas numpy scipy statsmodels scikit-learn matplotlib seaborn requests openpyxl + pip GEOparse gseapy mygene. Single-cell stages need a scanpy env (scanpy leidenalg harmonypy, and liana for cell-cell communication). Set NUMBA_CACHE_DIR=/tmp/numba_cache before importing scanpy on read-only site-packages. Load figure-style and call apply_figure_style() before plotting.

Stage 1 - Dataset inventory

Query each repository and build one master table (accession, assay, tissue, n_samples, has_control, treatment_context, platform, date, title, summary).

• GEO: NCBI E-utilities esearch/esummary with disease-name + assay terms.
• ArrayExpress (non-GEO), PRIDE (proteomics), CELLxGENE (curation API /curation/v1/collections). Audit for keyword-only false positives (co-mention of the disease); flag relevant=False with an exclusion reason rather than deleting. Save *_dataset_inventory.csv + a landscape figure (assay x repository).

Stage 2 - Tier & prioritize

Score each dataset on relevance / analytical role / tractability. Tier 1 = human in-vivo case-control bulk/array with interpretable design and usable n; Tier 2 = single-cell; Tier 3 = complementary (miRNA, methylation, proteomics, blood, in-vitro/mouse). Save dataset_tiers.csv + rationale md.

Stage 3 - Retrieve & QC Tier-1 bulk

Download supplementary count/intensity matrices. Build binary case/control labels per study (schemas differ - keep the labelling logic in a reusable script). Harmonize IDs to gene symbols (ENSG via mygene; array probes via platform annotation). Normalize: raw counts -> log-CPM; already-normalized kept as-is; linear microarray intensity -> log2. QC: per-study PCA (expect case/control separation) and a canonical-marker check (recovers known markers with correct direction => labels/orientation validated). Save a harmonized bundle as a checkpoint. Report marker direction heterogeneity-aware - a marker that reverses in one cohort is not "clean across all studies".

Stage 4 - Per-study DE

moderated_de(expr, labels) on log2 expression: variance-moderated Welch t (adds prior variance = median pooled variance), BH-FDR. Confirm canonical markers rank highly with correct direction (quote actual q-values; do not assume a uniform threshold). Underpowered studies may yield exactly 0 at FDR<0.05 - say "0", not "few". Save per-study tables + volcanoes + cross-study concordance heatmap.

Stage 5 - Cross-study meta-analysis

meta_analysis(fc_df, se_df) - DerSimonian-Laird random-effects pooling (per-study SE = |log2FC / t|), returns pooled log2FC, z, p, Q, I2, k, n_up/n_dn. BH-FDR across genes. High-confidence signature = FDR<0.05 AND |pooled FC|>=1 AND concordant in >=k_min studies. Label figures by the criterion actually plotted - if the volcano highlights FDR<0.05 & |FC|>=1, don't annotate it with counts from a stricter concordance-gated subset. Save *_meta_signature.csv + high-conf subset + volcano + forest.

Stage 6 - Single-cell validation

Pick a disease-specific 10x series (CELLxGENE atlases often lack an explicit disease tag). Load per-sample, QC-filter (n_genes 200-6000, pct_mt<25), concatenate. On a memory ceiling, keep the primary two-group contrast and hold out extra groups. Normalize -> HVG -> PCA -> Harmony (by subject) -> neighbors -> Leiden -> UMAP. Annotate clusters with marker score sets. Composition shift per cell type (per-sample fractions, Mann-Whitney) and per-cell-type case-vs-control DE. Save a slim (gzip) h5ad checkpoint, UMAP/composition/dotplot figures, composition + cell-type-DE CSVs. Note: granulocytes (eosinophils/neutrophils) often drop out of droplet scRNA-seq - absence of a cluster is not absence of the cell. harmonypy Z_corr orientation varies by version - verify shape is (cells x PCs) before assigning to obsm.

Stage 7 - Pathway / regulator / communication

Enrichr via gseapy on up/down signature (GO_BP, Reactome, Hallmark) and TF libraries (ChEA, ENCODE, TRRUST) for upstream regulators. Single-cell cell-cell communication with liana.mt.rank_aggregate on the case cells. Save enrichment tables + a pathway/regulator figure + LR dotplot. (Enrichr server maayanlab.cloud may need a network-access grant.)

Stage 8 - Multi-omics integration

Build a per-gene evidence table combining independently-varying layers: bulk-meta significance, cross-study reproducibility (k), single-cell up-confirmation. Compute the layer-count score over the FULL signature, not a pre-filtered subset - on an already-gated table the significance/k terms are constant and the "multi-layer" score collapses to one axis. Cross-check with an orthogonal layer (e.g. miRNA: down-miRNA vs up-target). Watch assay sign conventions (TaqMan Ct is inverse to expression). Save the evidence table + integration figure.

Stage 9 - Druggability

Open Targets GraphQL (api.platform.opentargets.org/api/v4/graphql) per target: tractability, drugAndClinicalCandidates, subcellularLocations, targetClass. Introspect the schema first - field names drift (knownDrugs -> drugAndClinicalCandidates; rows carry maxClinicalStage, not phase). Composite priority score: effect size + reproducibility + single-cell confirmation + accessibility (secreted > surface > intracellular for biologics) + AB-tractability + clinical precedent + existing pharmacology. Flag membrane-annotation false positives and ubiquitous targets (e.g. HLA) for manual triage. Save target_druggability.csv + figure.

Stage 10 - Consolidate

Curate a Tier-A shortlist (biological specificity + accessibility), add mechanism rationale and a suggested modality per target, and write a report

• integrated summary dashboard. State limitations honestly (dropout, underpowered layers, annotation caveats).

Reproducibility rules

fig, ax = plt.subplots() then fig.savefig(...) - never bare plt.*. Fetches in their own cell. Save expensive state as a checkpoint (harmonized bundle, slim h5ad), not every transform.