WNetAlign

WNetAlign is a tool for MS (Mass Spectrometry) or NMR (Nuclear Magnetic Resonance) spectral alignment using Truncated Wasserstein Metric-based methods. It offers algorithms and utilities for comparing and mapping spectral features between datasets, leveraging advanced optimal transport techniques and spectral topology.

Features

  • Truncated Wasserstein metric-based spectral alignment
  • Algorithms for spectral feature comparison and mapping
  • Utilities for preprocessing and visualization

Installation

pip install wnetalign

Usage

import numpy as np
from wnetalign import Spectrum, WNetAligner
from wnet.distances import DistanceMetric

# positions shape: (dim, num_peaks); intensities shape: (num_peaks,)
S1 = Spectrum(positions=np.array([[1.0, 2.5, 4.0]]), intensities=np.array([1.0, 3.0, 2.0]))
S2 = Spectrum(positions=np.array([[1.1, 2.4, 4.2]]), intensities=np.array([1.0, 2.5, 2.5]))

S1 = S1.normalized()
S2 = S2.normalized()

aligner = WNetAligner(
    empirical_spectrum=S1,
    theoretical_spectra=[S2],
    distance=DistanceMetric.L2,
    max_distance=0.5,
    trash_cost=0.9,
)
aligner.set_point([1])

print("Total cost:", aligner.total_cost())
flows = aligner.flows()[0]        # Flow(empirical_peak_idx, theoretical_peak_idx, flow)
emp_ids, theo_ids = aligner.consensus()  # greedy 1-to-1 pairs

For more complete examples see the tutorials directory.

Tutorials

Tutorials showing how to apply wnetalign to NMR data can be found in tutorials/nmr, while an example of LC-MS alignment can be found in tutorials/lcms.

Results from publication

To reproduce the results and figures from the paper "WNetAlign: Fast and Accurate Spectra Alignment Using Truncated Wasserstein Distance and Network Simplex" use the scripts and notebooks provided in the publication/nmr folder for NMR data; for an LC-MS alignment example see tutorials/lcms.

2D NMR data

The 2D 15N-1H HSQC spectra are available in publication/nmr/2D/15N_HSQC_GB1_reduced folder.

  • NMR_2D_original.ipynb - plotting data distribution, chain alignment of the series of 15N-1H HSQC spectra measured in different temperatures
  • NMR_2D_simulated.ipynb - simulating synthetic temperature series, validation of alignment procedure using standard and extended metrics, heatmaps
  • NMR_2D_seeds.ipynb - performing alignments for the synthetic temperature series with different random seeds

4D NMR data

The 4D CCNOESY spectrum of 2LX7 pdb structure can be found in the publication/nmr/4D/2LX7.

  • NMR_4D.ipynb - simulating the replicate of the 4D 2LX7 CCNOESY spectrum and validating the alignment between the original spectrum and the replicate with standard and extended performance metrics

7D NMR data

The hypothetical 7D NMR spectra can be found in publication/nmr/7D/NMR_7D.

  • NMR_7D.ipynb - simulating the replicates of the 17 hypothetical 7D NMR spectra and validating the alignments between the original hypothetical spectra and the replicates with extended performance metrics

Citation

If you use this software, please cite:

Król J, Bochenek M, Jopa S, Kazimierczuk K, Gambin A, Startek MP (2026). WNetAlign: fast and accurate spectra alignment using truncated Wasserstein distance and network simplex. Briefings in Bioinformatics, 27(3), bbag247. https://doi.org/10.1093/bib/bbag247

@article{krol2026wnetalign,
  title   = {WNetAlign: fast and accurate spectra alignment using truncated Wasserstein distance and network simplex},
  author  = {Kr{\'o}l, Justyna and Bochenek, Maria and Jopa, Sylwia and Kazimierczuk, Krzysztof and Gambin, Anna and Startek, Micha{\l} Piotr},
  journal = {Briefings in Bioinformatics},
  volume  = {27},
  number  = {3},
  pages   = {bbag247},
  year    = {2026},
  doi     = {10.1093/bib/bbag247}
}

License

This project is licensed under the MIT License.

Project links

Data verified by PyPI on Aug 14, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Latest release

1 maintainer

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Avatar for michalsta from gravatar.com michalsta

Credits

Author/Maintainer: Michał Startek

GitHub Statistics

Data verified by PyPI on Aug 14, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.9

Provides Extra

extras pytest

Classifiers

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