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hnoca_cells30
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.
Single-cell RNA-seq of human neural organoids from the HNOCA atlas (27 publications). Task: predict the HNOCA harmonized level-4 cell-type annotation (annot_level_4_rev2) among its 30 most frequent classes. The split is donor- and publication-disjoint: groups are connected components of the publication-cell line graph and are assigned to train/val/test (train: Bhaduri, 2020; Fiorenzano, 2021; Fleck, 2022; Huang, 2021; Kanton, 2019; Kelava, 2022; Paulsen, 2022; Pellegrini, 2020; Sawada, 2020; Uzquiano, 2022; Velasco, 2019; Xiang, 2019; val: Bowles, 2021; He, 2022; test: Andersen, 2020; Atamian, 2024; Birey, 2017; Khan, 2020; Marton, 2019; Miura, 2020; Qian, 2020; Samarasinghe, 2021; Sloan, 2017; Treutlein, 2023; Trujillo, 2019; Vertesy, 2022; Yoon, 2019); then 70000/15000/15000 cells are sampled within the splits, stratified by class. Features: log1p(CPM) expression of 22000 genes selected with analytic Pearson residuals (scanpy, flavor='pearson_residuals') computed on the raw counts of the training cells only.
| Domain | Task | Samples | Features | Classes |
|---|---|---|---|---|
| Single-cell | classification | 100,000 | 22,000 | 30 |
Splits. Fixed train/val/test split (70000/15000/15000 rows).
Files
data/**.parquet: one row per sample.row_idxis the row index in the original TabBioMed array;f_<i>[_<name>]are the features (in order);target(ortarget_<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.
from datasets import load_dataset
ds = load_dataset("TabBioMed/hnoca_cells30")
A Croissant (1.1) description with Responsible-AI fields is distributed with the TabBioMed release.
Source and license
Source: He et al. (2024), Human Neural Organoid Cell Atlas (HNOCA), hnoca_cleaned.h5ad downloaded from https://zenodo.org/records/14161275.
This derived dataset is distributed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Please also cite and respect the terms of the original source.
Citation
@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}
}
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