| --- |
| tags: |
| - biology |
| - genomics |
| - bulk-rna-seq |
| task_categories: |
| - text-classification |
| license: cc-by-4.0 |
| --- |
| |
| # Virtual Cell — Distilled Bulk Encoder Example Dataset |
|
|
| A minimal sample dataset for verifying the data format and running quick |
| end-to-end checks with |
| [ConvergeBio/virtual-cell-distil-bulk](https://huggingface.co/ConvergeBio/virtual-cell-distil-bulk). |
|
|
| > **This dataset is not intended for training or evaluation.** It contains a |
| > small number of samples and is not representative of a real distribution. |
| > Metrics produced from this dataset should not be interpreted. |
|
|
| ## Dataset contents |
|
|
| Derived from a public sepsis bulk RNA-seq study ([GSE185263](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE185263)). Preprocessed |
| into the model's input format as a minimal working example. |
|
|
| | Split | Samples | Class distribution | |
| |---|---|---| |
| | train | 88 | 53 sepsis / 35 healthy | |
| | validation | 22 | 13 sepsis / 9 healthy | |
|
|
| Labels: `0` = healthy, `1` = sepsis. |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("ConvergeBio/virtual-cell-distil-bulk-example") |
| train_ds = ds["train"] |
| val_ds = ds["validation"] |
| ``` |
|
|
| ## Schema |
|
|
| | Column | Shape | Type | Description | |
| |---|---|---|---| |
| | `bulk_expression` | [18301] | float32 | Log-normalised bulk gene expression, aligned to `gene_names.txt` | |
| | `labels` | scalar | int | Class index (0 = healthy, 1 = sepsis) | |
| | `disease_state` | scalar | str | Original label string | |
| | `sample_id` | scalar | str | GEO sample accession ID | |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the original study: |
|
|
| ```bibtex |
| @article{baghela2022sepsis, |
| author = {Baghela, A. and others}, |
| title = {Predicting sepsis severity at first clinical presentation: The role of endotypes and mechanistic signatures}, |
| journal = {EBioMedicine}, |
| year = {2022}, |
| volume = {75}, |
| pages = {103776}, |
| doi = {10.1016/j.ebiom.2021.103776}, |
| note = {GEO accession: GSE185263}, |
| } |
| ``` |
|
|
| If you use the Virtual Cell distilled bulk encoder, 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) |
|
|