<p align="center">
$${\Huge{\textbf{\textsf{\color{#2E8B57}Bio\color{#4682B4}sets}}}}$$
<br/>
<br/>
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</p>
**Biosets** is a specialized library that extends 🤗 [Datasets](https://github.com/huggingface/datasets) for bioinformatics data, providing the following main features:
- **Bioinformatics Specialization**: Streamlines data management specific to bioinformatics, such as handling samples, features, batches, and associated metadata.
- **Automatic Column Detection**: Infers sample, batch, input features, and target columns, simplifying downstream preprocessing.
- **Custom Data Classes**: Leverages specialized data classes (`ValueWithMetadata`, `Sample`, `Batch`, `RegressionTarget`, etc.) to manage metadata-rich bioinformatics data.
- **Polars Integration**: Optional [Polars](https://github.com/pola-rs/polars) integration enables high-performance data manipulation, ideal for large datasets.
- **Flexible Task Support**: Native support for binary classification, multiclass classification, multiclass-to-binary classification, and regression, adapting to diverse bioinformatics tasks.
- **Integration with 🤗 Datasets**: `load_dataset` function supports loading various bioinformatics formats like CSV, JSON, NPZ, and more, including metadata integration.
- **Arrow File Caching**: Uses [Apache Arrow](https://github.com/apache/arrow) for efficient on-disk caching, enabling fast access to large datasets without memory limitations.
Biosets helps bioinformatics researchers focus on analysis rather than data handling, with seamless compatibility with 🤗 Datasets.
## Installation
### With pip
You can install **Biosets** from PyPI:
```bash
pip install biosets
```
### With conda
Install **Biosets** via conda:
```bash
conda install -c patrico49 biosets
```
## Usage
**Biosets** provides a straightforward API for handling bioinformatics datasets with integrated metadata management. Here's a quick example:
```python
from biosets import load_biodata
bio_data = load_dataset(
data_files="data_with_samples.csv",
sample_metadata_files="sample_metadata.csv",
feature_metadata_files="feature_metadata.csv",
target_column="metadata1",
experiment_type="metagenomics",
batch_column="batch",
sample_column="sample",
metadata_columns=["metadata1", "metadata2"],
drop_samples=False
)["train"]
```
For further details, check the [advance usage documentation](./docs/DATA_LOADING.md).
## Main Differences Between Biosets and 🤗 Datasets
- **Bioinformatics Focus**: While 🤗 Datasets is a general-purpose library, Biosets is tailored for the bioinformatics domain.
- **Seamless Metadata Integration**: Biosets is built for datasets with metadata dependencies, like sample and feature metadata.
- **Automatic Column Detection**: Reduces preprocessing time with automatic inference of sample, batch, feature, and label columns.
- **Specialized Data Classes**: Biosets introduces custom classes (e.g., `Sample`, `Batch`, `ValueWithMetadata`) to enable richer data representation.
## Disclaimers
Biosets may run Python code from custom `datasets` scripts to handle specific data formats. For security, users should:
- Inspect dataset scripts prior to execution.
- Use pinned versions for any repository dependencies.
If you manage a dataset and wish to update or remove it, please open a discussion or pull request on the Community tab of 🤗's datasets page.
## BibTeX
If you'd like to cite **Biosets**, please use the following:
```bibtex
@misc{smyth2024biosets,
title = {psmyth94/biosets: 1.1.0},
author = {Patrick Smyth},
year = {2024},
url = {https://github.com/psmyth94/biosets},
note = {A library designed to support bioinformatics data with custom features, metadata integration, and compatibility with 🤗 Datasets.}
}
```
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