OmniPath table ingestion¶

omnipath_client.to_annnet builds a graph from an OmniPath-style interaction table. AnnNet reaches no knowledge base of its own: the client for a knowledge base is what returns AnnNet objects. This notebook uses a local table so it is deterministic.

In [1]:

import annnet as an
import omnipath_client as oc

an.info()

import annnet as an import omnipath_client as oc an.info()

[2026-08-07 02:29:57] [DEBUG] [pkg_infra.session        ] ▸ Using app logger: default

[2026-08-07 02:29:57] [DEBUG] [pkg_infra.session        ] ▸ Session time initialized: utc=2026-08-07 00:29:57.380209+00:00, local=2026-08-07 02:29:57.380210+02:00, timezone=CEST

[2026-08-07 02:29:57] [DEBUG] [pkg_infra.session        ] ▸ Location lookup skipped

[2026-08-07 02:29:57] [INFO ] [pkg_infra.session        ] ▸ Creating new session...

[2026-08-07 02:29:57] [INFO ] [pkg_infra.session        ] ▸ Session has been created

Out[1]:

annnetv0.2.0·graph: networkx·plot: graphviz·I/O: 10/12

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
Installable bundles	backendsplotiostorageall

Build from a local OmniPath-style table¶

In [2]:

import polars as pl

interactions = pl.DataFrame(
    {
        'source': ['EGF', 'EGFR', 'EGFR', 'EGFR', 'RAS', 'MEK'],
        'target': ['EGFR', 'RAS', 'RAS', 'GRB2', 'MEK', 'ERK'],
        'interaction_id': [
            'EGF_EGFR',
            'EGFR_RAS_primary',
            'EGFR_RAS_secondary',
            'EGFR_GRB2_complex',
            'RAS_MEK',
            'MEK_ERK',
        ],
        'is_directed': [True, True, True, False, True, True],
        'curation_score': [0.95, 0.88, 0.63, 0.76, 0.82, 0.79],
        'consensus_direction': [1, 1, 1, 0, 1, 1],
        'source_database': [
            'omnipath',
            'omnipath',
            'literature',
            'complexportal',
            'pathwayextra',
            'kinaseextra',
        ],
    }
)

G = oc.to_annnet(
    interactions,
    source_col='source',
    target_col='target',
    edge_id_col='interaction_id',
    directed_col='is_directed',
    weight_col='curation_score',
    edge_attr_cols=['consensus_direction', 'source_database'],
)

print('shape:', G.shape)
G.views.edges().select(
    ['edge_id', 'source', 'target', 'effective_weight', 'source_database']
)

import polars as pl interactions = pl.DataFrame( { 'source': ['EGF', 'EGFR', 'EGFR', 'EGFR', 'RAS', 'MEK'], 'target': ['EGFR', 'RAS', 'RAS', 'GRB2', 'MEK', 'ERK'], 'interaction_id': [ 'EGF_EGFR', 'EGFR_RAS_primary', 'EGFR_RAS_secondary', 'EGFR_GRB2_complex', 'RAS_MEK', 'MEK_ERK', ], 'is_directed': [True, True, True, False, True, True], 'curation_score': [0.95, 0.88, 0.63, 0.76, 0.82, 0.79], 'consensus_direction': [1, 1, 1, 0, 1, 1], 'source_database': [ 'omnipath', 'omnipath', 'literature', 'complexportal', 'pathwayextra', 'kinaseextra', ], } ) G = oc.to_annnet( interactions, source_col='source', target_col='target', edge_id_col='interaction_id', directed_col='is_directed', weight_col='curation_score', edge_attr_cols=['consensus_direction', 'source_database'], ) print('shape:', G.shape) G.views.edges().select( ['edge_id', 'source', 'target', 'effective_weight', 'source_database'] )

shape: (6, 6)

Out[2]:

shape: (6, 5)

edge_id	source	target	effective_weight	source_database
str	str	str	f64	str
"EGF_EGFR"	"EGF"	"EGFR"	0.95	"omnipath"
"EGFR_RAS_primary"	"EGFR"	"RAS"	0.88	"omnipath"
"EGFR_RAS_secondary"	"EGFR"	"RAS"	0.63	"literature"
"EGFR_GRB2_complex"	"EGFR"	"GRB2"	0.76	"complexportal"
"RAS_MEK"	"RAS"	"MEK"	0.82	"pathwayextra"
"MEK_ERK"	"MEK"	"ERK"	0.79	"kinaseextra"

Add analysis context as slices¶

In [3]:

rows = list(G.views.edges().iter_rows(named=True))
edge_label = {
    row['edge_id']: f"{row['source']} -> {row['target']}"
    for row in rows
}
high_confidence = [
    row['edge_id']
    for row in rows
    if row['effective_weight'] >= 0.85
]
G.slices.add('high_confidence')
G.slices.add_edges('high_confidence', high_confidence)

print(
    'high-confidence interactions:',
    [edge_label[eid] for eid in sorted(G.slices.edges('high_confidence'))],
)

rows = list(G.views.edges().iter_rows(named=True)) edge_label = { row['edge_id']: f"{row['source']} -> {row['target']}" for row in rows } high_confidence = [ row['edge_id'] for row in rows if row['effective_weight'] >= 0.85 ] G.slices.add('high_confidence') G.slices.add_edges('high_confidence', high_confidence) print( 'high-confidence interactions:', [edge_label[eid] for eid in sorted(G.slices.edges('high_confidence'))], )

high-confidence interactions: ['EGFR -> RAS', 'EGF -> EGFR']

Draw the prior network¶

In [4]:

from annnet.utils import plotting

plotting.plot(
    G,
    backend='graphviz',
    show_edge_labels=True,
    edge_label_keys=['source_database'],
)

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

Out[4]:

The client hands back a graph that keeps confidence, provenance and every downstream context in one object. Where the table came from is the concern of the client, and what the graph does with it is the concern of AnnNet.