PyG HeteroData export¶

to_pyg exports an AnnNet graph to PyTorch Geometric HeteroData for graph neural network workflows.

In [1]:

import annnet as an

an.info()

import annnet as an an.info()

Out[1]:

annnetv0.2.0·graph: networkx·plot: graphviz·I/O: 12/13

Show environment details

Version	v0.2.0
License	BSD-3-Clause
Authors	Youssef Zerta ✉, Daniele Bottazzi ✉, Denes Turei ✉
Repository	https://github.com/saezlab/annnet
Documentation	https://saezlab.github.io/annnet/reference/
Default adapter backend	networkx
Default plot backend	graphviz
Graph backends	✓networkx✓igraph✗graph-tool✓pyg
Plot backends	✓graphviz✗pydot✓matplotlib
Tabular data backends	✓polars✓pandas✓pyarrow
I/O modules	✓annnet✓json/ndjson✓dataframes✓csv✓excel✓graphml/gexf✓sif✓cx2✓parquet✓zarr✗sbml✓scverse✓omnipath
Installable bundles	backendsplotiostorageall

Build a heterogeneous graph¶

In [2]:

G = an.AnnNet(directed=True)
G.add_vertices('p1', kind='protein', activity=1.2, abundance=4.0)
G.add_vertices('p2', kind='protein', activity=-0.4, abundance=2.5)
G.add_vertices('g1', kind='gene', expression=8.0, length=1200.0)
G.add_vertices('drug_a', kind='drug', dosage=10.0, approved=1.0)

G.add_edges('p1', 'g1', edge_id='regulates', weight=0.8)
G.add_edges('p2', 'g1', edge_id='represses', weight=-0.6)
G.add_edges('drug_a', 'p1', edge_id='targets', weight=0.5)
G.add_edges('drug_a', 'p2', edge_id='off_target', weight=0.25)
G.slices.add('train')
G.slices.add_edges('train', ['regulates', 'represses'])

G.views.vertices()

G = an.AnnNet(directed=True) G.add_vertices('p1', kind='protein', activity=1.2, abundance=4.0) G.add_vertices('p2', kind='protein', activity=-0.4, abundance=2.5) G.add_vertices('g1', kind='gene', expression=8.0, length=1200.0) G.add_vertices('drug_a', kind='drug', dosage=10.0, approved=1.0) G.add_edges('p1', 'g1', edge_id='regulates', weight=0.8) G.add_edges('p2', 'g1', edge_id='represses', weight=-0.6) G.add_edges('drug_a', 'p1', edge_id='targets', weight=0.5) G.add_edges('drug_a', 'p2', edge_id='off_target', weight=0.25) G.slices.add('train') G.slices.add_edges('train', ['regulates', 'represses']) G.views.vertices()

Out[2]:

shape: (4, 8)

vertex_id	kind	activity	abundance	expression	length	dosage	approved
str	str	f64	f64	f64	f64	f64	f64
"p1"	"protein"	1.2	4.0	null	null	null	null
"p2"	"protein"	-0.4	2.5	null	null	null	null
"g1"	"gene"	null	null	8.0	1200.0	null	null
"drug_a"	"drug"	null	null	null	null	10.0	1.0

Draw the source graph¶

In [3]:

from annnet.utils import plotting

plotting.plot(G, backend='graphviz', show_edge_labels=True)

from annnet.utils import plotting plotting.plot(G, backend='graphviz', show_edge_labels=True)

Out[3]:

Export to PyG and run a tensor operation¶

In [4]:

import torch

data = an.to_pyg(
    G,
    node_features={
        'protein': ['activity', 'abundance'],
        'gene': ['expression', 'length'],
        'drug': ['dosage', 'approved'],
    },
    slice_id='train',
    hyperedge_mode='skip',
)
data.validate(raise_on_error=True)

protein_activity = data['protein'].x[:, 0]
activity_probability = torch.sigmoid(protein_activity)
homogeneous = data.to_homogeneous()

print(data)
print('protein activity:', protein_activity.tolist())
print('sigmoid(activity):', activity_probability.tolist())
print('homogeneous edge_index shape:', tuple(homogeneous.edge_index.shape))

import torch data = an.to_pyg( G, node_features={ 'protein': ['activity', 'abundance'], 'gene': ['expression', 'length'], 'drug': ['dosage', 'approved'], }, slice_id='train', hyperedge_mode='skip', ) data.validate(raise_on_error=True) protein_activity = data['protein'].x[:, 0] activity_probability = torch.sigmoid(protein_activity) homogeneous = data.to_homogeneous() print(data) print('protein activity:', protein_activity.tolist()) print('sigmoid(activity):', activity_probability.tolist()) print('homogeneous edge_index shape:', tuple(homogeneous.edge_index.shape))

HeteroData(
  manifest={
    node_index={
      protein={
        p1=0,
        p2=1,
      },
      gene={ g1=0 },
      drug={ drug_a=0 },
    },
    edge_index={},
  },
  protein={
    x=[2, 2],
    train_mask=[2],
  },
  gene={
    x=[1, 2],
    train_mask=[1],
  },
  drug={
    x=[1, 2],
    train_mask=[1],
  },
  (protein, edge, gene)={
    edge_index=[2, 2],
    edge_weight=[2],
  },
  (drug, edge, protein)={
    edge_index=[2, 2],
    edge_weight=[2],
  }
)
protein activity: [1.2000000476837158, -0.4000000059604645]
sigmoid(activity): [0.7685248255729675, 0.40131235122680664]
homogeneous edge_index shape: (2, 4)

AnnNet remains useful before tensorization: it stores names, metadata, slices, and graph semantics. to_pyg is the boundary where selected numeric attributes become tensors.