Foundation embeddings

Skill Alim430/bioresearch-agent/skills/biomedical/foundation-embeddings

Executable biomedical workflows for AI assistants — literature analysis, biomarker discovery, and Mendelian randomization through reproducible agent skills.

Install

npx -y skills add Alim430/bioresearch-agent --skill foundation-embeddings

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Generate and evaluate single-cell foundation model embeddings (scGPT / UCE / scFoundation). Runs mock-mode pipeline validation by default — simulated embeddings that recover planted cell-type structure. Live mode stubs (pip install scgpt, UCE eval_single_anndata.py, scFoundation get_embedding.py) are documented for real deployment. Use when the user wants to embed scRNA-seq data with foundation models, compare embedding quality across models, or benchmark cell-type recovery.

SKILL.md

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BioResearch Agent — Foundation Model Embeddings Skill

Capability

Generates cell-level embeddings from three single-cell foundation models and evaluates them on a standardized cell-type recovery benchmark:

scGPT (Cui et al. 2024, Nature Methods) — 512-dim, pretrained on 33M human cells, CPU-compatible. Input: normalized + log1p AnnData. Live API: scgpt.tasks.embed_data(adata, model_dir, gene_col).
UCE (Rosen et al. 2023, bioRxiv) — 1280-dim, pretrained on 36M multi-species cells, GPU required. Input: raw counts h5ad. Live API: AnndataProcessor from evaluate.py.
scFoundation (Hao et al. 2024, Nature Methods) — 512-dim, pretrained on 50M+ human cells, GPU required. Input: CSV aligned to 19,264-gene list. Live API: get_embedding.py --input_type singlecell.

Evaluation metrics (all custom implementations, no sklearn dependency for core metrics):

Silhouette score — cluster cohesion vs separation
ARI (Adjusted Rand Index) — agreement with ground-truth labels
NMI (Normalized Mutual Information) — information-theoretic agreement
Cross-model kNN overlap — neighborhood consistency between model pairs
Robustness sweep — metrics across noise levels [0.1–0.6]

Returns per-model metrics CSV, cross-model consistency table, robustness sweep results, UMAP/PCA visualization, clustering heatmap, summary report with evidence grade, and a JSON evidence package.

Run

bioresearch run foundation-embeddings --n-cells 1000 --n-genes 2000 --seed 42 \
  --output-dir outputs/foundation-embeddings

Outputs (in `--output-dir`)

fe_per_model_metrics.csv — per-model silhouette, ARI, NMI
fe_cross_model_consistency.csv — pairwise kNN overlap between models
fe_robustness_sweep.csv — metrics across 6 noise levels for each model
fe_embedding_umap.png — side-by-side 2D projection for all 3 models
fe_clustering_heatmap.png — ARI/NMI/silhouette heatmap across noise levels
fe_summary_report.txt — human-readable summary with evidence grade
fe_evidence_package.json — reproducible Evidence Package (provenance + metrics + grade)

Note

This skill dispatches to the framework's foundation-embeddings workflow / demo_foundation_embeddings.py. By default it runs in MOCK mode: embeddings are simulated with cluster centroids + Gaussian noise + L2 normalization, matching the output dimensions of each real model. This validates that the evaluation pipeline (silhouette, ARI, NMI, kNN overlap, robustness sweep) recovers planted cell-type structure — it does NOT measure real model performance.

Evidence grade: B (mock mode validates pipeline correctness; real model benchmarks require GPU and model checkpoints).

Live Mode Deployment

To switch from mock to real model inference:

# scGPT (easiest — PyPI + CPU)
pip install scgpt
from scgpt.tasks import embed_data
embeddings = embed_data(adata, model_dir, gene_col="Gene Symbol", device="cpu")

# UCE (git clone + GPU)
git clone https://github.com/snap-stanford/UCE.git
python eval_single_anndata.py --model_path ./model_uce_65m.pt \
  --adata_path input.h5ad --output_path output.h5ad

# scFoundation (git clone + GPU + gene alignment)
git clone https://github.com/biomap-labs/scFoundation.git
python get_embedding.py --input_type singlecell --output_type cell \
  --input_file input.csv --model_path ./models/