Quickstart¶

Build a small signaling graph, inspect its edges, draw it, and save it in the native AnnNet format.

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

Create a graph¶

In [2]:

G = an.AnnNet(directed=True)
G.add_vertices(['EGF', 'EGFR', 'GRB2', 'SOS1', 'RAS', 'ERK'])
G.add_edges('EGF', 'EGFR', edge_id='ligand_binding', weight=1.0)
G.add_edges('EGFR', 'RAS', edge_id='canonical_signal', weight=0.9)
G.add_edges('EGFR', 'RAS', edge_id='adapter_signal', weight=0.7)
G.add_edges('GRB2', 'SOS1', edge_id='adapter_complex', directed=False, weight=0.55)
G.add_edges(
    src=['EGFR', 'GRB2', 'SOS1'],
    tgt=['RAS'],
    edge_id='signalosome',
    directed=True,
    weight=1.2,
)
G.add_edges('RAS', 'ERK', edge_id='mapk_signal', weight=0.75)

print('shape:', G.shape)
print('vertices:', sorted(G.vertices()))

G = an.AnnNet(directed=True) G.add_vertices(['EGF', 'EGFR', 'GRB2', 'SOS1', 'RAS', 'ERK']) G.add_edges('EGF', 'EGFR', edge_id='ligand_binding', weight=1.0) G.add_edges('EGFR', 'RAS', edge_id='canonical_signal', weight=0.9) G.add_edges('EGFR', 'RAS', edge_id='adapter_signal', weight=0.7) G.add_edges('GRB2', 'SOS1', edge_id='adapter_complex', directed=False, weight=0.55) G.add_edges( src=['EGFR', 'GRB2', 'SOS1'], tgt=['RAS'], edge_id='signalosome', directed=True, weight=1.2, ) G.add_edges('RAS', 'ERK', edge_id='mapk_signal', weight=0.75) print('shape:', G.shape) print('vertices:', sorted(G.vertices()))

shape: (6, 6)
vertices: ['EGF', 'EGFR', 'ERK', 'GRB2', 'RAS', 'SOS1']

Inspect edges¶

In [3]:

G.views.edges().select(
    ['edge_id', 'kind', 'source', 'target', 'head', 'tail', 'directed', 'effective_weight']
)

G.views.edges().select( ['edge_id', 'kind', 'source', 'target', 'head', 'tail', 'directed', 'effective_weight'] )

Out[3]:

shape: (6, 8)

edge_id	kind	source	target	head	tail	directed	effective_weight
str	str	str	str	list[str]	list[str]	bool	f64
"ligand_binding"	"binary"	"EGF"	"EGFR"	null	null	true	1.0
"canonical_signal"	"binary"	"EGFR"	"RAS"	null	null	true	0.9
"adapter_signal"	"binary"	"EGFR"	"RAS"	null	null	true	0.7
"adapter_complex"	"binary"	"GRB2"	"SOS1"	null	null	false	0.55
"signalosome"	"hyper"	"EGFR|GRB2|SOS1"	"RAS"	["EGFR", "GRB2", "SOS1"]	["RAS"]	true	1.2
"mapk_signal"	"binary"	"RAS"	"ERK"	null	null	true	0.75

Draw the topology¶

AnnNet stores the graph; Graphviz is just a rendering backend. In a notebook, returning the Graphviz object displays the graph directly.

In [4]:

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[4]:

Save and reload¶

In [5]:

import os
import tempfile

tmp = tempfile.mkdtemp(prefix='annnet_quickstart_')
path = os.path.join(tmp, 'signaling.annnet')
an.write(G, path)
reloaded = an.read(path)

print('written:', os.path.basename(path))
print('reloaded shape:', reloaded.shape)

import os import tempfile tmp = tempfile.mkdtemp(prefix='annnet_quickstart_') path = os.path.join(tmp, 'signaling.annnet') an.write(G, path) reloaded = an.read(path) print('written:', os.path.basename(path)) print('reloaded shape:', reloaded.shape)

written: signaling.annnet
reloaded shape: (6, 6)

The native .annnet file preserves the parallel edges, the undirected adapter complex, and the signalosome hyperedge instead of flattening them into a plain edge list.