bosperrus 0.2.3

Boundary effects in spatial graphs: error modeling and untangling strategies

BOSPERRUS: BOundary effects in SPatial graphs: ERRor modeling and Untangling Strategies

Tutorial — Getting Started with Flow

BOSPERRUS detects and corrects boundary effects in spatial graphs. Nodes close to the border of a tissue sample or point cloud tend to have systematically lower centrality scores than nodes in the center — not because they are biologically different, but because they have fewer neighbours. BOSPERRUS models this distance-dependent bias and optionally corrects for it.

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Table of Contents

1. Installation
2. Key concepts
3. Quick-start
4. Step-by-step walkthrough
   • Step 1 – Create a toy dataset
   • Step 2 – Initialise Flow
   • Step 3 – Run model fitting and correction
5. Reading the results
6. Choosing a graph type
7. Choosing a distance function
8. Visualising the correction (optional)
9. API reference summary

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Installation

Within a conda environment, run

pip install git+https://github.com/bionetslab/bosperrus-package.git

Pip dependencies (scikit-learn, scipy, numpy, pandas) are installed automatically. Within the environment, you also need to

conda install graph-tool -c conda-forge

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Key concepts

Entry points / input types:

Flow enables multiple entry points depending on what you already have:
🟣 Given coordinates, a distance function, a graph type (Delaunay, k-nearest neighbor, or radius nearest neighbor), and centrality measures to compute.
🩷 Given coordinates, a distance function, and a pre-built edge list.
🟡 Given coordinates, a distance function, and pre-computed centrality scores.
☘️ Given pre-computed distances and scores only (no coordinates needed).

Distance functions:

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Quick-start

import numpy as np
from bosperrus import Flow
from bosperrus.distances import distance_to_convex_hull

rng = np.random.default_rng(42)
coords = rng.uniform(0, 100, size=(500, 2))   # 500 random 2-D points

flow = Flow.from_coords(
    coordinates=coords,
    distance_fn=distance_to_convex_hull,
    measures=["degree", "closeness", "betweenness", "clustering", "pagerank"],
    graph_type="delaunay",
)
flow.flow()

# Inspect results
print(flow.fit_quality)
print(flow.observations.head())   # contains raw + corrected centrality columns

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Step-by-step walkthrough

Step 1 – Create a toy dataset

We simulate a disc-shaped point cloud to produce an obvious boundary effect: nodes at the perimeter will always have fewer neighbours than nodes at the centre.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from bosperrus import Flow
from bosperrus.distances import distance_to_convex_hull

rng = np.random.default_rng(0)

# Sample uniformly inside a disc of radius 50
n = 600
angles = rng.uniform(0, 2 * np.pi, n)
radii  = 50 * np.sqrt(rng.uniform(0, 1, n))   # sqrt for uniform area sampling
coords = np.stack([radii * np.cos(angles),
                   radii * np.sin(angles)], axis=1)

Step 2 – Initialise Flow

Flow is initialised via one of four factory classmethods depending on what you already have. For a full pipeline from coordinates:

measures = ["degree", "closeness", "betweenness", "harmonic", "clustering", "pagerank"]

flow = Flow.from_coords(
    coordinates=coords,
    distance_fn=distance_to_convex_hull,
    measures=measures,
    graph_type="delaunay",   # or "knn" / "rnn", see below
)

If you already have an edge list, use Flow.from_coords_and_edgelist. If you already have centrality scores, use Flow.from_coords_and_scores. If you have neither coordinates nor a graph, use Flow.from_distances_and_scores.

All factory methods populate flow.observations: a DataFrame with one row per node containing the centrality scores and the distance column.

Step 3 – Run model fitting and correction

from bosperrus.fit import ConstantFit, PiecewiseLinearFit, ExponentialSaturationFit, MichaelisMentenFit

flow.flow(
    measures=measures,
    fits=[ConstantFit, PiecewiseLinearFit, ExponentialSaturationFit, MichaelisMentenFit],
    calculate_rel_ll_to_baseline=ConstantFit,   # ConstantFit = "no boundary effect" null model
)

flow() selects the best model per measure by AIC and writes corrected values back into flow.observations. All arguments are optional — omitting them runs all four models on all measures with ConstantFit as baseline.

After this step:

• flow.fit_quality — a DataFrame with one row per measure summarising the best model, AIC-based statistics, effect strength, and half-life.
• flow.observations — now also contains columns named "BOSPERRUS corrected <measure>" for each centrality.

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Reading the results

# Overview table
print(flow.fit_quality.T[[
    "best_fit_type",
    "observed_effect_strength",   # signed relative drop from border to centre
    "observed_half_life",         # distance at which ~50% of the effect has decayed
    "affected samples",           # fraction of nodes still in the boundary-affected zone
]])

observed_effect_strength is defined as (c_border − c_centre) / (c_border + c_centre). A value close to 0 means negligible boundary bias; a value near ±1 means strong bias.

best_fit_type tells you which model won the AIC comparison:

Model	Interpretation
Constant Fit	No detectable boundary effect
Piecewise Linear Fit	Linear drop up to a breakpoint, then flat plateau
Exponential Saturation Fit	Smooth exponential approach to a plateau
Michaelis-Menten Fit	Hyperbolic saturation (analogous to enzyme kinetics)

To access the corrected values:

# All corrected columns at once
corrected_cols = [c for c in flow.observations.columns if c.startswith("BOSPERRUS corrected")]
print(flow.observations[corrected_cols].head())

# Single measure
flow.observations[["degree", "BOSPERRUS corrected degree"]].head()

To access fit parameters directly:

degree_fit = flow.best_fits["degree"]
print(degree_fit.name)      # e.g. "Exponential Saturation Fit"
print(degree_fit.params)    # fitted parameter dict
print(degree_fit.AIC)

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Choosing a graph type

Graph type	Key parameter	Notes
"delaunay"	none	Default choice; produces a natural triangulation without isolated nodes
"knn"	k (int)	Directed, asymmetric — pass via graph_kwargs={"k": 6}
"rnn"	r (float, same units as coords)	Undirected — pass via graph_kwargs={"r": 15.0}

# k-nearest-neighbour example
flow = Flow.from_coords(
    coordinates=coords,
    distance_fn=distance_to_convex_hull,
    measures=measures,
    graph_type="knn",
    graph_kwargs={"k": 6},
)

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Choosing a distance function

Function	Use case
distance_to_convex_hull(coords)	General default; works for any convex or near-convex tissue shape (2-D and 3-D)
distance_to_rectangular_border(coords)	Rectangular imaging window / biopsy (2-D only)
distance_to_pointset(coords, pointset)	Border defined by a set of explicit landmark coordinates
distance_to_mask(coords, mask)	Binary image mask defining the tissue boundary

All functions are importable from bosperrus.distances and return a named pd.Series with one value per node.

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Visualising the correction (optional)

import matplotlib.pyplot as plt

fig, axes = plt.subplots(1, 2, figsize=(12, 5))

sc = axes[0].scatter(coords[:, 0], coords[:, 1],
                     c=flow.observations["degree"], cmap="viridis", s=8)
axes[0].set_title("Raw degree centrality")
plt.colorbar(sc, ax=axes[0])

sc2 = axes[1].scatter(coords[:, 0], coords[:, 1],
                      c=flow.observations["BOSPERRUS corrected degree"], cmap="viridis", s=8)
axes[1].set_title("BOSPERRUS-corrected degree centrality")
plt.colorbar(sc2, ax=axes[1])

plt.tight_layout()
plt.savefig("bosperrus_correction.png", dpi=150)
plt.show()

Nodes near the border should appear more uniform after correction.

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API reference summary

Flow — construction

Factory method	When to use
Flow.from_coords(coordinates, distance_fn, measures, graph_type, distance_kwargs=None, graph_kwargs=None)	Full pipeline from scratch
Flow.from_coords_and_edgelist(coordinates, distance_fn, measures, edge_list, distance_kwargs=None)	Skip graph construction
Flow.from_coords_and_scores(coordinates, distance_fn, scores, distance_kwargs=None)	Pre-computed centralities
Flow.from_distances_and_scores(distances, scores)	No coords needed

Flow — methods

Method	Signature	Description
flow	(measures=None, fits=None, calculate_rel_ll_to_baseline=None)	Fit all models, select best by AIC, write corrected columns to observations

Key attributes after a full run

Attribute	Type	Content
flow.observations	pd.DataFrame	All node-level data: centralities, distance column, corrected values
flow.fit_quality	pd.DataFrame	Per-measure model selection summary (columns = measures)
flow.best_fits	dict[str, Fit]	Best Fit object per measure
flow._edge_list	set	Set of edges built during construction (available for Paths 1 and 2)