ScientaLab/primo

🧬 Lodestar — benchmark inputs

Lodestar is a blind benchmark for transcriptomic foundation models: it measures how well a model's patient-level embeddings capture real clinical signal.

This repo holds the inputs you embed. It's deliberately blind — datasets are named d001, d002, … with no disease, tissue, or target revealed. You grade the embedding, not task-specific tuning.

📦 What's inside

• datasets.yaml — the manifest: each dataset's id, file path, and shape (n_samples, n_genes, gene_id_type).
• d001/expression.h5ad, d002/… — raw counts as AnnData: rows = samples (sample_id), columns = NCBI gene ids (with gene_symbols).

🚀 Run the benchmark

1. Download the data:

   hf download ScientaLab/lodestar --repo-type dataset --local-dir lodestar
   

2. Embed every dataset with your model — one vector per sample (any dimension; it may differ per dataset).
3. Assemble one submission file covering all datasets — dataset_id, sample_id, then one column per embedding dim (e0, e1, …). Format: CSV / TSV / Parquet, or NPZ with dataset_ids / sample_ids / embeddings.
4. Submit & see your rank → 🏆 Lodestar Space

📊 How it's scored

A fixed linear probe is trained on frozen cross-validation folds over your embeddings and scored — AUROC (classification) or Pearson r (regression). Scores become a 0–1 skill and roll up per medical specialty. Only submissions that cover every dataset are ranked. Labels stay private — grading happens server-side.

➡️ Ready? → https://huggingface.co/spaces/ScientaLab/lodestar-eval

Each dataset is redistributed under its source's original open license.