SNF2

Authors: Michael Tran, James Bannon

Contact: bhklab.michaeltran@gmail.com

Description: SNF2 is a modern Python implementation of Similarity Network Fusion.

---

Usage

import numpy as np

from snf2 import fuse, make_affinity

modality_a = np.array(
    [[0.0, 1.0], [0.2, 0.8], [1.0, 0.1], [0.9, 0.2]],
)
modality_b = np.array(
    [[1.0, 0.0], [0.8, 0.1], [0.1, 1.0], [0.2, 0.9]],
)

affinities = [
    make_affinity(modality_a, n_neighbors=2),
    make_affinity(modality_b, n_neighbors=2),
]
fused_network = fuse(affinities, n_neighbors=2)

Rows are samples and columns are features. SNF2 does not standardize or align inputs: callers must preprocess each modality and ensure identical sample ordering before constructing affinities.

Affinity construction defaults to squared Euclidean distance and accepts every named metric supported by scipy.spatial.distance.pdist. Use metric_kwargs for metric-specific arguments:

correlation_network = make_affinity(
    modality_a,
    metric="correlation",
    n_neighbors=2,
)

minkowski_network = make_affinity(
    modality_a,
    metric="minkowski",
    metric_kwargs={"p": 3.5},
    n_neighbors=2,
)

Metric-specific data requirements follow SciPy. SNF2 raises an error if a metric produces non-finite or negative pairwise distances for the supplied data.

SNF2 currently provides the two core algorithm stages: constructing an affinity matrix from one feature matrix and fusing affinity matrices across modalities.

Usage

import numpy as np

from snf2 import fuse, make_affinity

modality_a = np.array(
    [[0.0, 1.0], [0.2, 0.8], [1.0, 0.1], [0.9, 0.2]],
)
modality_b = np.array(
    [[1.0, 0.0], [0.8, 0.1], [0.1, 1.0], [0.2, 0.9]],
)

affinities = [
    make_affinity(modality_a, n_neighbors=2),
    make_affinity(modality_b, n_neighbors=2),
]
fused_network = fuse(affinities, n_neighbors=2)

Rows are samples and columns are features. SNF2 does not standardize or align inputs: callers must preprocess each modality and ensure identical sample ordering before constructing affinities.

Affinity construction defaults to squared Euclidean distance and accepts every named metric supported by scipy.spatial.distance.pdist. Use metric_kwargs for metric-specific arguments:

correlation_network = make_affinity(
    modality_a,
    metric="correlation",
    n_neighbors=2,
)

minkowski_network = make_affinity(
    modality_a,
    metric="minkowski",
    metric_kwargs={"p": 3.5},
    n_neighbors=2,
)

Metric-specific data requirements follow SciPy. SNF2 raises an error if a metric produces non-finite or negative pairwise distances for the supplied data.

Development setup

Pixi manages the development environments and lock file.

pixi install
pixi run -e dev check

Build the documentation locally with:

pixi run -e docs docs-build

The published documentation is available at bhklab.github.io/snf2.

License

SNF2 is licensed under the MIT License.