Datasets:
File size: 2,860 Bytes
c6b8357 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | ---
pretty_name: sc_muris_celltype
task_categories:
- tabular-classification
tags:
- tabular
- biomedical
- benchmark
- TabBioMed
- single-cell
- classification
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/train.parquet
- split: validation
path: data/val.parquet
- split: test
path: data/test.parquet
license: mit
---
# sc_muris_celltype
Part of **TabBioMed** (*TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning*), a benchmark of 94 curated biomedical
tabular datasets. Code, preprocessing and documentation: https://github.com/AI-sandbox/TabBioMed.
Classify nine released kidney cell-type annotations from Tabula Muris Senis Smart-seq2 expression.
| Domain | Task | Samples | Features | Classes |
|---|---|---|---|---|
| Single-cell | classification | 1,817 | 16,472 | 9 |
**Splits.** Fixed train/val/test split (1271/273/273 rows).
## Files
- `data/**.parquet`: one row per sample. `row_idx` is the row index in the original
TabBioMed array; `f_<i>[_<name>]` are the features (in order); `target` (or `target_<j>`
for multi-target tasks) is the label. Categorical feature indices (0-based, among the
features): `[]`. Missing values are stored as nulls.
- `metadata.json`: TabBioMed task metadata (task type, categorical indices, source, description).
- `feature_names.json`: original feature names, in column order.
- `sample_metadata.csv`: per-row provenance (identifiers, source annotations); not features.
- `feature_metadata.csv`: per-feature annotations.
```python
from datasets import load_dataset
ds = load_dataset("TabBioMed/sc_muris_celltype")
```
A Croissant (1.1) description with Responsible-AI fields is distributed with the
TabBioMed release.
## Source and license
Dataset license: [MIT](https://opensource.org/license/mit).
- [Tabula Muris Senis Data Objects](https://doi.org/10.6084/m9.figshare.12654728). Tabula Muris Consortium and Angela Pisco (2020), DOI 10.1038/s41586-020-2496-1. Kidney representation distributed via XPgogogo/scUnified. Source license: [MIT](https://opensource.org/license/mit). Retain the MIT permission and warranty text and source attribution.
See `NOTICE.md` for attribution and modifications and `LICENSES/` for license texts.
## Citation
```bibtex
@inproceedings{tabbiomed2026,
title = {TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning},
author = {Mateo Bernad{\'o}, Pau and Polapragada, Saivenkata Nagavyjayanthi and Bonet, David and Arbiol Rakuljic, Pol and Mogas Pladevall, Laia and Comajoan Cara, Mar{\c{c}}al and Gonzalez Ferrer, Jesus and Abante, Jordi and Mas Montserrat, Daniel and Haussler, David and Ioannidis, Alexander G.},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
year = {2026}
}
```
|