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