STRND: Graph reconstruction via Node2Vec (pecanpy) + UMAP
Project description
STRND
STRND = Spatio Topological Recovery by Network Discovery
- Feed an edge list (pandas DataFrame, NX, PyG, list of edges)
- Get back node coordinates in 2D or 3D (pandas DataFrame)
Install
pip install strnd
Minimal use
from strnd import get_strnd_positions_df
coords_df = get_strnd_positions_df(edge_df, dim=2)
# coords_df columns: node_ID, x, y
edge_df must have columns source, target (and optionally weight).
Example with Graph Generation + Quality Metrics
import numpy as np
from proxigraph.config import GraphConfig
from proxigraph.core import ProximityGraph
from PointQuality import QualityMetrics
from strnd import get_strnd_positions_df
np.random.seed(42)
# 1) create a graph
config = GraphConfig(dim=2, num_points=1000, L=1, point_mode="circle", proximity_mode="delaunay_corrected")
pg = ProximityGraph(config=config)
positions = pg.generate_positions()
edge_df = pg.get_edge_list(as_dataframe=True)
# 2) run STRND
rec_positions = get_strnd_positions_df(edge_df, dim=2)
# 3) quality
rec_positions_ordered = rec_positions.sort_values("node_ID")[["x","y"]].to_numpy()
qm = QualityMetrics(positions, rec_positions_ordered)
print(qm.evaluate_metrics(compute_distortion=False))
# 4) print head
print(rec_positions.head())
dependencies
- numpy
- pandas
- matplotlib
- pecanpy (node2vec)
- umap-learn
- (optional) proxigraph for the demo
Citation
If you use STRND in your work, please cite:
Core STRND paper
Fernández Bonet, D., & Hoffecker, I. T. (2023). Image recovery from unknown network mechanisms for DNA sequencing-based microscopy. Nanoscale, 15, 8153–8157. https://doi.org/10.1039/D2NR05435C
Related work
Dahlberg, S. K., Fernández Bonet, D., & Hoffecker, I. T. (2025). Hidden network preserved in Slide-tags data allows reference-free spatial reconstruction. Nature Communications. https://doi.org/10.1038/s41467-025-65295-w
Fernández Bonet, D., Blumenthal, J. I., Lang, S., Dahlberg, S. K., & Hoffecker, I. T. (2024). Spatial coherence of DNA barcode networks. bioRxiv. https://doi.org/10.1101/2024.05.12.593725
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