wnetdeconv
Spectral deconvolution via Wasserstein optimal transport.
Given an empirical spectrum and a library of theoretical component spectra,
wnetdeconv finds the mixture proportions that minimise the total Wasserstein
transport cost between the empirical signal and the weighted sum of components.
The inner problem at each set of proportions is solved exactly as a min-cost
flow (via pylmcf / LEMON), giving an
exact piecewise-linear objective with exact gradients — suitable for gradient-
based outer optimisation with scipy.
Supports 1-D spectra (NMR chemical shift, m/z) and higher-dimensional data (e.g. m/z + retention time).
Installation
pip install wnetdeconv
Dependencies: pylmcf, wnet, numpy, scipy.
Optional: pyopenms for loading featureXML files.
Concepts
Spectra as distributions
A spectrum is a set of (position, intensity) pairs. In 1-D (NMR chemical
shift, m/z) use Spectrum_1D; for higher-dimensional data (m/z + retention
time) use Spectrum with a (d, n) positions array.
from wnetdeconv import Spectrum_1D
empirical = Spectrum_1D([1.0, 2.0, 3.0], [10.0, 25.0, 15.0])
component = Spectrum_1D([1.0, 2.0, 3.0], [1.0, 2.0, 1.0])
Transport cost
Matching a unit of intensity from an empirical peak at position p to a
theoretical peak at position q costs distance(p, q). Peaks that cannot
be matched cheaply are instead routed to a trash node at a fixed penalty.
max_distance caps the farthest match considered; anything farther is cheaper
to trash. trash_cost (or the asymmetric pair
experimental_trash_cost / theoretical_trash_cost) sets that penalty.
Scaling
Internally all intensities and costs are scaled to integers for the MCF solver.
Intensities are quantised automatically by a p95-quantile policy
(wnet.scaling.WNetDeconvScaler); cost quantisation is chosen by the network
itself against its integer budget. Construction raises ValueError if
quantisation would silently discard more than 20% of any spectrum's total
intensity (see allow_intensity_loss). Pass scale_factor to override the
automatic scaling explicitly. The scipy ftol stop criterion is derived from
the actual scale factors, so the outer loop stops as soon as further improvement
is below the resolution the integer network can deliver.
The former precision parameter is deprecated and has no effect; passing a
non-default value raises a DeprecationWarning.
Solvers
DeconvSolver — unconstrained baseline
Solves the network at a given point and exposes total_cost() and
gradient(). Optimisation (via optimize(), L-BFGS-B) minimises cost with
only non-negativity bounds.
from wnetdeconv import DeconvSolver, Spectrum_1D
from wnet.distances import DistanceMetric
emp = Spectrum_1D([1.0, 100.0], [10.0, 30.0])
t1 = Spectrum_1D([1.0], [2.0]) # optimal proportion: 5
t2 = Spectrum_1D([100.0], [3.0]) # optimal proportion: 10
solver = DeconvSolver(
empirical_spectrum=emp,
theoretical_spectra=[t1, t2],
distance=DistanceMetric.LINF,
max_distance=10.0,
trash_cost=100.0,
)
result = solver.optimize()
print(result.x) # [5. 10.]
You can also drive the solver manually — useful when embedding it in your own optimisation loop:
solver.set_point([5.0, 10.0])
print(solver.total_cost()) # 0.0
print(solver.gradient()) # [0. 0.] (at the optimum)
ConstrainedSolver — total-mass equality
Adds the constraint Σ wₛ · Iₛ = I_emp so that the mixture exactly accounts
for all empirical intensity. Uses SLSQP. Drop-in replacement for
DeconvSolver; call optimize() the same way.
from wnetdeconv import ConstrainedSolver
solver = ConstrainedSolver(
empirical_spectrum=emp,
theoretical_spectra=[t1, t2],
distance=DistanceMetric.LINF,
max_distance=10.0,
trash_cost=100.0,
)
result = solver.optimize()
Key parameters
| Parameter | Applies to | Description |
|---|---|---|
max_distance |
all | Maximum peak-to-peak match distance. Also sets the sparsity of the internal network in 1-D. |
trash_cost |
all | Symmetric penalty for unmatched peaks. |
experimental_trash_cost |
DeconvSolver |
Per-unit penalty for discarding empirical mass. |
theoretical_trash_cost |
DeconvSolver |
Per-unit penalty for discarding theoretical mass. |
precision |
all | Deprecated, no effect; non-default values raise DeprecationWarning. |
scale_factor |
all | Override automatic intensity/cost quantisation. |
Distance metrics
From wnet.distances.DistanceMetric:
L1— sum of absolute coordinate differences (Manhattan / taxicab)L2— Euclidean distanceLINF— maximum absolute coordinate difference (Chebyshev); dual of the W₁ earth-mover distance used by masserstein
Loading MS data (featureXML)
from wnetdeconv import Spectrum
emp = Spectrum.FromFeatureXML("sample.featureXML") # requires pyopenms
Architecture
wnetdeconv
├── Spectrum / Spectrum_1D — data containers (extend wnet.Distribution)
├── DeconvSolver — core: builds WassersteinNetwork, exposes cost + gradient
└── ConstrainedSolver — adds total-mass equality, uses SLSQP
The underlying min-cost flow is provided by wnet (network construction) and pylmcf (LEMON-based MCF algorithms, including warm-restart Network Simplex).
Related work
Sister paper: WNetAlign applies the same truncated Wasserstein / network simplex machinery to MS and NMR spectral alignment:
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
License
MIT
1 maintainer
Metadata
1 maintainer
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