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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)
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