netan 1.0.3

Network builder for multi-omics matrices (Rodin-compatible).

Netan — Multilayer Network Builder for Rodin‑like Objects

Netan builds multilayer networks from omics matrices and gives you clean APIs to analyze, visualize, and export them. It supports Spearman, CLR (MI‑z), ExtraTrees‑RF, and Graphical Lasso; both samples and features node modes; stacked or multilayer graphs (with consensus edges); cross‑omics links; an interactive Plotly viewer; and Cytoscape‑ready CSV export.

Web App: netan.io

Works with any Rodin‑like object exposing:

• r.X: pandas.DataFrame (features × samples)
• r.samples: pandas.DataFrame (first column = sample IDs; order = r.X.columns)
• r.features: pandas.DataFrame (index = feature IDs)

• check https://github.com/BM-Boris/rodin

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Installation

pip install netan

Requires Python ≥ 3.10. Dependencies (installed automatically): rodin>=1.9.10, numpy, pandas, networkx, scikit-learn, joblib, tqdm, plotly.

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Quick Start (with Rodin)

Below is a ready‑to‑run .

import rodin
import netan

# 1) Create one or multiple Rodin objects from omics data + metadata
r1 = rodin.create( 'metabolomics.txt', 'meta.csv'
    )

r2 = rodin.create( 'transcriptomics.csv', 'meta.csv'
    )

# 2) Apply your preprocessing (Rodin handles normalization/log/scale etc.)
r1.transform()
r2.transform()

# 3) Build a multilayer network across shared samples
nt = netan.create([r1, r2])
nt.build(
    method='spearman',        # network inference method
    edge_threshold=0.75,       # threshold on method-specific weights
    layer_mode='multilayer',  # 'stack' or 'multilayer'
)

# 4) Interactive Plotly graph (FigureWidget)
fig = nt.plot(
    title='Netan: Samples × Multilayer (Spearman, threshold=0.75)',
    node_size=12,
    width=950,
    height=650
)

# 5) Export an edge table compatible with Cytoscape
nt.to_csv()

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What Netan Does

• Aligns samples across inputs.
• Infers networks per method:
  • spearman: absolute Spearman correlation, threshold ∈ [0,1].
  • clr: Context Likelihood of Relatedness (MI‑based symmetric Z). Typical thresholds ~2–5.
  • rf: ExtraTrees‑based symmetric importance; threshold on [0,1].
  • glasso: Graphical Lasso; threshold on |partial correlation| ∈ [0,1].
• Combines layers:
  • layer_mode='stack': single layer "Entire".
  • layer_mode='multilayer': per‑input graphs; edges carry a layers set (includes "Entire"; adds "consensus" if present in all inputs).
  • In features+multilayer mode: adds cross‑omics edges labeled cross_<method>.
• Layouts & communities: assigns 2D coordinates (x,y) and lightweight component labels for easy plotting.
• Interactive Plotly: legend‑driven node group toggles dynamically rebuild edge polylines; continuous color shows a colorbar.
• CSV export: source,target,weight,layer,layers (Cytoscape‑friendly; set List delimiter = "|").

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create(rodins, names=None) -> Netan

Builds a Netan container from one or multiple Rodin‑like objects by aligning them to shared samples. Prints concise pre/post stats.

• Parameters

  • rodins: a single object or a list of objects exposing .X and .samples (optionally .features, .uns).
  • names: optional list of human‑readable layer names (defaults to r.uns['file_name'] or layer{i}).

• Returns: Netan (with .G unset until you call .build).

Netan.build(method='rf', node_mode='samples', layer_mode='multilayer', edge_threshold=0.025, weights=True, layout='force-directed', combine='mean', n_jobs=-1, **kwargs) -> self

Constructs the network in self.G and stores a 2D layout on nodes.

• Common parameters

  • method: 'spearman' | 'clr' | 'rf' | 'glasso'.
  • node_mode: 'samples' | 'features' — whether nodes represent samples or features.
  • layer_mode: 'stack' | 'multilayer' — combine inputs into one layer or keep them separate with fusion.
  • edge_threshold: float — threshold applied to the method‑specific weight matrix.
  • weights: bool — store edge weights as G[u][v]['weight'].
  • layout: 'force-directed'|'spring'|'circular'|'kamada_kawai'|'random' — determines x,y.
  • combine: 'mean'|'median'|'max' — fusion rule in samples+multilayer mode.
  • n_jobs: int — parallelism for CLR/RF.

• Method‑specific **kwargs

  • clr: n_neighbors=int.
  • rf: n_estimators=int, max_depth=int|None (0/''/None ⇒ None).
  • glasso: alpha=float, max_iter=int, tol=float (default 1e‑4).

• Returns: self. After the call, self.G is a networkx.Graph with edge attributes weight, layer, layers; nodes have x,y,display_id,community (and in features mode: object,file,type,compound when metadata is available).

Netan.plot(color=None, shape=None, layer=None, hide_isolated=False, weight_min=None, weight_max=None, node_size=10, width=None, height=None, title='Network Plot', continuous_colorscale='Viridis') -> plotly.graph_objs.FigureWidget

Creates an interactive Plotly network figure.

• Color/shape

  • Categorical color/shape splits nodes into legend groups; hiding a group removes its incident edges on the fly.
  • Continuous color shows a colorbar (legend toggling disabled).

• Layer/weight filters

  • layer: keep an edge if that layer label is present in its layers set.
  • weight_min/max: numeric bounds for pruning edges by weight.

• Display: returns a FigureWidget suitable for notebooks/dashboards.

Netan.to_csv(path=None, sep=',', index=False, float_format=None) -> pandas.DataFrame

Exports a flat edge list. Columns: source, target, weight, layer, layers.

• In features node mode: adds source_compound, target_compound if known.
• Cytoscape tip: set Advanced → List delimiter = | so layers parses as list.

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Threshold Tips

• Spearman: 0.7–0.9 (use higher for sparser graphs).
• CLR: 2–5 (start at 3).
• RF (ExtraTrees): 0.02–0.10.
• Glasso: 0.1–0.3 for edge_threshold; increase alpha (0.1–0.2) if convergence is hard.

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Performance & Limits

• Soft density guard around ~10,000 edges (MAX_EDGES); warnings suggest raising thresholds or reducing nodes.
• Complexity:
  • spearman/CLR/RF: ~O(p²) in the number of nodes per layer.
  • glasso: ~O(p³); consider increasing alpha` or reducing dimensionality.
• Use n_jobs to parallelize CLR/RF.

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Troubleshooting

• Graph too dense → raise edge_threshold, use a stricter method (glasso), or reduce variables.
• GraphicalLasso failed → increase alpha (e.g., 0.1–0.2), relax tol, ensure proper scaling.
• Empty plot → check layer and weight_min/max filters and that inputs share sample IDs.
• Too many categories for shape → map values to fewer categories (limited symbol set).

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License

MIT (see LICENSE).