OmniPath table ingestion¶

from_omnipath can consume an OmniPath-style interaction table directly. This notebook uses a local table so it is deterministic.

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 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 = an.from_omnipath(
    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'],
    load_vertex_annotations=False,
)

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 = an.from_omnipath( 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'], load_vertex_annotations=False, ) print('shape:', G.shape) G.views.edges().select( ['edge_id', 'source', 'target', 'effective_weight', 'source_database'] )

[timing] fetch/receive df:     0.000s
[timing] column resolution:    0.0055s
         source='source'  target='target'  directed='is_directed'
         edge_attr_cols (2): ['consensus_direction', 'source_database']
[timing] AnnNet init:          0.005s  (pre-sized n=6 e=6)
[timing] to_rows setup:        0.000s  (6 rows, streaming=True)
[timing] bulk list build:      0.000s  (6 edges)
[timing] add_edges_bulk:       0.001s
         vertices=6  edges=6
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]:

AnnNet can use OmniPath-style tables as prior knowledge while keeping confidence, provenance, and downstream contexts in one graph object.