Datasets:
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.
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
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:
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:
@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:
@article{convergecell2026,
author = {ConvergeBio},
title = {ConvergeCELL: An end-to-end platform from patient transcriptomics to therapeutic hypotheses},
year = {2026},
note = {Preprint available on bioRxiv},
}