biosets 1.0.1

Bioinformatics datasets and tools

BioSets: Dataset Creation for Biological Research

Please note that this project is in the early stages of development. The documentation and features are subject to change.

Overview

BioSets is a library built on top of the datasets library for loading, manipulating, and processing biological datasets for machine learning purposes. It supports genomics, transcriptomics, proteomics, metabolomics, and other types of biological data.

This repository contains tools and documentation for creating biological datasets using BioSets. The library loads biological data from local files, creates custom datasets, and handles large volumes of biological information. BioSets is intended for researchers and data scientists in bioinformatics, systems biology, and biotechnology.

Features

🧬 Loading sample metadata and feature metadata: BioSets loads both sample metadata and feature metadata.

🧬 Support for various biological data types: Includes predefined classes for genomic variants, gene expression data, clinical trial data, and OTU tables.

🧬 Automatic Sample/Batch Detection: Automatically detects sample and batch information from the loaded data to handle batch effects and confounding factors.

🧬 Custom dataset creation: Create custom datasets with specific features, metadata, and labels.

🧬 Integration with datasets library: BioSets builds on the datasets library's functionality. Note that if path is not a value found in biosets.list_experiment_types(), it acts like Huggingface's datasets library.

Getting Started

To use the BioSets library, clone the repository and install the necessary dependencies. After setting up your environment, create your dataset by following the steps below.

Installation

Install BioSets using pip:

pip install biosets

Creating a Biological Dataset

To create a dataset for biological research using BioSets, follow these steps:

1. Organize Your Data: Prepare your biological data in a structured format that BioSets can process (e.g., directory of relevant files).

2. Load Your Data with Metadata: Use load_dataset() to load your data along with sample metadata and feature metadata:

   from biosets import load_dataset
   
   dataset = load_dataset(
       "snp",
       data_files="/path/to/snp_data.csv",
       sample_metadata_files="/path/to/sample_metadata.csv",
       feature_metadata_files="/path/to/feature_metadata.csv",
   )
   

3. Utilize Metadata for Analysis: The loaded dataset allows you to access and use metadata in downstream analyses. For example, you can handle abundance data differently based on its type:

   from biosets.features import Abundance
   for k, v in dataset.features.items():
       if isinstance(v, Abundance):
           print(f"Processing abundance feature: {k}")
   

Dataset Examples

Loading Specific Experiments

Use specific experiment types for loading data, such as otu, maldi, rna, or snp to ensure the appropriate configuration is applied:

🧬 OTU Data

dataset = load_dataset("otu", data_files="/path/to/otu_data.csv")

🧬 RNA Data

dataset = load_dataset("rna", data_files="/path/to/rna_data.csv")

🧬 SNP Data

dataset = load_dataset("snp", data_files="/path/to/snp_data.csv")

Next Steps

After creating your biological dataset, you can use BioSets for feature extraction, model training, or data visualization.

For more advanced usage, refer to the dataset loading documentation. For building custom datasets, refer to the custom dataset creation documentation.

For any additional information, refer to the datasets library documentation.

Contributing

Contributions are welcome! If you have suggestions for improvements or new features, open an issue or submit a pull request. For major changes, open an issue first to discuss it.

License

This project contains portions derived from various sources under the Apache License, Version 2.0. For full details, please refer to the LICENSE file.