add dataset card

README.md

@@ -1,39 +1,124 @@
----
-dataset_info:
-  features:
-  - name: input_ids
-    sequence:
-      sequence: float32
-  - name: attention_mask
-    sequence: bool
-  - name: entity_id
-    dtype: int64
-  - name: augmentation_id
-    dtype: int64
-  - name: cell_types
-    sequence: string
-  - name: labels
-    dtype: int64
-  - name: sample_id
-    dtype: string
-  - name: tissue
-    dtype: string
-  - name: disease
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 1464492698.909091
-    num_examples: 40
-  - name: validation
-    num_bytes: 549184762.0909091
-    num_examples: 15
-  download_size: 166251037
-  dataset_size: 2013677461.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
----
+# Virtual Cell — Patient Example Dataset
+
+A minimal sample dataset for verifying the data format and running quick
+end-to-end checks with
+[ConvergeBio/virtual-cell-patient](https://huggingface.co/ConvergeBio/virtual-cell-patient).
+
+> **This dataset is not intended for training or evaluation.** It contains a
+> small number of patients and is not representative of a real distribution.
+> Metrics produced from this dataset should not be interpreted.
+
+## Dataset contents
+
+Derived from a public type 1 diabetes scRNA-seq study (GSE148073). Preprocessed
+into the model's input format as a minimal working example.
+
+| Split | Patients | Rows |
+|---|---|---|
+| train | 8 | 40 |
+| validation | 3 | 15 |
+
+Each row is one augmented view of a patient (5 augmentations per patient).
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("ConvergeBio/virtual-cell-patient-example", token="...")
+train_ds = ds["train"]
+val_ds   = ds["validation"]
+```
+
+## Schema
+
+| Column | Shape | Type | Description |
+|---|---|---|---|
+| `input_ids` | [500, 18301] | float32 | Log-normalized gene expression matrix, aligned to `gene_names.txt` |
+| `attention_mask` | [500] | bool | Cell mask (all ones for fixed cell count) |
+| `labels` | scalar | int | Class index |
+| `entity_id` | scalar | int | Patient identifier — groups augmented views of the same patient |
+| `sample_id` | scalar | str | Original sample accession ID |
+
+## Preparing your own dataset
+
+### Input format
+
+Each patient is a single `.h5ad` (AnnData) file:
+
+```
+adata.X      — cell × gene expression matrix (float32, log-normalized)
+adata.obs    — cell-level metadata (cell_type optional)
+adata.var    — gene metadata (index must be HGNC gene symbols)
+```
+
+Values should be library-size normalized (target sum 10,000) and `log1p`
+transformed. The gene axis must be aligned to the 18,301 genes in
+`gene_names.txt` (from the model repo) — missing genes are zero-filled,
+extra genes are dropped.
+
+### Quality control (optional)
+
+Recommended filters before building the dataset:
+
+| Parameter | Default | Description |
+|---|---|---|
+| min genes per cell | 200 | Remove low-complexity cells |
+| max genes per cell | 5,000 | Remove likely doublets |
+| max mitochondrial % | 10% | Remove dying cells |
+
+### Building the HuggingFace dataset
+
+For each patient, randomly sample 500 cells into a `[500, 18301]` float32
+matrix. Repeat this sampling independently multiple times per patient to
+create augmented views — each view becomes a separate row with the same
+`entity_id`.
+
+**Augmentation is strongly encouraged.** The model aggregates predictions
+across views at inference time, producing more robust results. A factor of
+5 augmentations per patient is a good default; 1 is supported but not
+recommended.
+
+Assign each patient a unique integer `entity_id`. All augmented views of
+the same patient must share the same `entity_id`.
+
+The final dataset should be saved in HuggingFace Datasets format:
+
+```python
+from datasets import DatasetDict
+dd = DatasetDict({"train": train_ds, "validation": val_ds})
+dd.save_to_disk("my_dataset")
+# or push directly:
+dd.push_to_hub("my-org/my-dataset")
+```
+
+## Citation
+
+If you use this dataset, please cite the original study:
+
+```bibtex
+@article{sachs2022singlecell,
+  author    = {Fasolino, Maria and others},
+  title     = {Single-cell multi-omics analysis of human pancreatic islets reveals
+               novel cellular states in type 1 diabetes},
+  journal   = {Nature Metabolism},
+  year      = {2022},
+  doi       = {10.1038/s42255-022-00531-x},
+  note      = {GEO accession: GSE148073},
+}
+```
+
+If you use the Virtual Cell patient model, please also cite:
+
+```bibtex
+@article{convergecell2026,
+  author    = {ConvergeBio},
+  title     = {ConvergeCELL: An end-to-end platform from patient transcriptomics to therapeutic hypotheses},
+  year      = {2026},
+  note      = {Preprint available on bioRxiv},
+}
+```
+
+## License
+
+[TBD]