---
license: cc-by-4.0
pretty_name: pca_50k
task_categories:
- tabular-regression
tags:
- tabular
- biomedical
- benchmark
- TabBioMed
- genomics
- multiregression
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
---

# pca_50k

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.

This dataset contains genotypes from chromosome chr22 for a subset of samples from the 1000 Genomes Project and HGDP.         For this specific dataset, 50k SNPs at random positions were selected from the original sequences and missing values where introduced with a probability of 0%.         The task is to predict the first 3 PCA coordinates of each sample based on the genotypes.

| Domain | Task | Samples | Features | Classes |
|---|---|---|---|---|
| Genomics | multiregression | 2,609 | 34,635 | - |

**Splits.** Fixed train/val/test split (1827/391/391 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).


```python
from datasets import load_dataset
ds = load_dataset("TabBioMed/pca_50k")
```

A Croissant (1.1) description with Responsible-AI fields is distributed with the
TabBioMed release.

## Source and license

Source: Chromosome data was downloaded from GnomAD v3.1.2 - 1KG + HGDP callset: https://gnomad.broadinstitute.org/downloads#v3-hgdp-1kg.         PCA coordinates were computed utilizing the 300,000 most informative SNPs from chromosomes chr1-chr22. 

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

```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}
}
```