omicsync 0.1.0

Multi-omics data harmonisation for Python

omicsync

A Python library for multi-omics data harmonisation.

omicsync handles the tedious work of aligning sample IDs, normalising each modality consistently, and exporting to downstream tools so you can focus on biology, not data wrangling.

---

Installation

pip install omicsync

With optional extras:

pip install "omicsync[mofa]"       # MOFA2 factor analysis
pip install "omicsync[geo]"        # GEO data loading
pip install "omicsync[anndata]"    # AnnData export
pip install "omicsync[torch]"      # PyTorch tensor export
pip install "omicsync[all]"        # Everything

---

Quick Start

import omicsync as oms
from omicsync.loaders.csv import load_multimodal_csv

# Load multiple modalities from CSV files
dataset = load_multimodal_csv({
    "rna":     "brca_rna.tsv",
    "protein": "brca_rppa.tsv",
    "cnv":     "brca_cnv.tsv",
}, study_id="TCGA-BRCA")

# Align, normalise, filter — all chainable
dataset.align_samples().normalize().filter_features(min_variance=0.01)

# Export to DataFrame or MOFA2
df = dataset.to_dataframe()          # samples × features, prefixed columns
mofa_input = dataset.to_mofa2()      # dict ready for mofapy2 entry_point

---

Features

• Sample harmonisation — TCGA barcode parsing, fuzzy ID matching, coverage reporting
• Per-modality normalisation — auto-detection of count/TPM/M-value formats
• Chainable API — dataset.align().normalize().filter_features()
• sklearn compatibility — use OmicsSyncTransformer in a Pipeline
• Multiple export formats — DataFrame, dict, MOFA2, PyTorch tensor, AnnData
• Open Targets integration — query target-disease associations via GraphQL
• Type hints throughout — fully typed public API

---

Supported Data Sources

Source	Loader	Notes
TCGA	load_tcga_files()	Local files; barcode auto-harmonisation
GEO	load_geo()	Via GEOparse; requires omicsync[geo]
CSV/TSV	load_csv()	Any tabular file
Open Targets	load_open_targets_targets()	GraphQL API v4

---

Supported Modalities

Modality	Class	Default Normalisation
RNA expression	RNAModality	detect_and_normalise() (log1p)
DNA methylation	MethylationModality	M→beta conversion + clip
Copy number	CNVModality	log2 ratio, clipped [-2, 2]
Somatic mutations	MutationModality	Binarise at threshold
Protein abundance	ProteinModality	Z-score per protein

---

Documentation

• Quickstart guide
• API reference
• Tutorial: TCGA BRCA
• Tutorial: Custom CSV data

---

Citation

If you use omicsync in your research, please cite:

Paterson V. (2026). omicsync: A Python library for multi-omics data harmonisation. GitHub: github.com/vi-c-ky/omicsync

---

Contributing

Contributions are welcome. Please open an issue or pull request on GitHub.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Write tests for new functionality
4. Run the test suite (pytest tests/)
5. Open a pull request

---

License

MIT — see LICENSE for details.