| --- |
| pretty_name: "Lodestar — benchmark inputs" |
| license: other |
| --- |
| |
| # 🧬 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: |
| ```bash |
| 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](https://huggingface.co/spaces/ScientaLab/lodestar-eval)** |
|
|
| ## 📊 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.* |
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|
