README.md

---
tags:
  - biology
  - genomics
  - single-cell-rna-seq
task_categories:
  - text-classification
license: cc-by-4.0
---

# 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](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=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")
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

[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/deed.en)