Network Analysis

In [1]:

import sys

import matplotlib.pyplot as plt
import networkx as nx
from loguru import logger
from pyeed import Pyeed
from pyeed.analysis.network_analysis import NetworkAnalysis
from pyeed.analysis.sequence_alignment import PairwiseAligner

logger.remove()
level = logger.add(sys.stderr, level="WARNING")

import sys import matplotlib.pyplot as plt import networkx as nx from loguru import logger from pyeed import Pyeed from pyeed.analysis.network_analysis import NetworkAnalysis from pyeed.analysis.sequence_alignment import PairwiseAligner logger.remove() level = logger.add(sys.stderr, level="WARNING")

In [2]:

uri = "bolt://127.0.0.1:7688"
user = "neo4j"
password = "12345678"

eedb = Pyeed(uri, user=user, password=password)

uri = "bolt://127.0.0.1:7688" user = "neo4j" password = "12345678" eedb = Pyeed(uri, user=user, password=password)

Pyeed Graph Object Mapping constraints not defined. Use _install_labels() to set up model constraints.
📡 Connected to database.

In [5]:

# load NCBI accession ids from a file
ids = []
with open("ids.txt", "r") as f:
    for line in f:
        ids.append(line.strip())


# now fecth all of the proteins from the database
eedb.fetch_from_primary_db(ids, db="ncbi_protein")

# load NCBI accession ids from a file ids = [] with open("ids.txt", "r") as f: for line in f: ids.append(line.strip()) # now fecth all of the proteins from the database eedb.fetch_from_primary_db(ids, db="ncbi_protein")

In [7]:

# perform the sequence alignment
pa = PairwiseAligner()
alignments = pa.align_multipairwise(ids=ids, db=eedb.db)

# perform the sequence alignment pa = PairwiseAligner() alignments = pa.align_multipairwise(ids=ids, db=eedb.db)

Output()

In [8]:

attribute = "similarity"
scale = 1
threshold = 0.15
mode = "HIDE_UNDER_THRESHOLD"
type_relationship = "PAIRWISE_ALIGNED"

attribute = "similarity" scale = 1 threshold = 0.15 mode = "HIDE_UNDER_THRESHOLD" type_relationship = "PAIRWISE_ALIGNED"

In [11]:

na = NetworkAnalysis(db=eedb.db)
network = na.create_graph(ids=ids, nodes=["Protein"])

na = NetworkAnalysis(db=eedb.db) network = na.create_graph(ids=ids, nodes=["Protein"])

In [12]:

filtered_graph, pos = na.calculate_positions_2d(
    attribute=attribute,
    scale=scale,
    threshold=threshold,
    mode=mode,
    type_relationship=type_relationship,
)

filtered_graph, pos = na.calculate_positions_2d( attribute=attribute, scale=scale, threshold=threshold, mode=mode, type_relationship=type_relationship, )

In [16]:

node_labels = {
    n: data["properties"].get("accession_id", n)
    for n, data in filtered_graph.nodes(data=True)
}

edge_weights = nx.get_edge_attributes(filtered_graph, attribute) if attribute else {}


# Draw edge labels if attribute is provided
if attribute:
    nx.draw_networkx_edge_labels(filtered_graph, pos, edge_labels=edge_weights)

nx.draw(
    filtered_graph,
    pos,
    with_labels=True,
    node_size=550,
    alpha=0.8,
    font_size=8,
    edge_color="black",
    labels=node_labels,
)

plt.title("Example network of pairwise aligned proteins")

plt.show()

node_labels = { n: data["properties"].get("accession_id", n) for n, data in filtered_graph.nodes(data=True) } edge_weights = nx.get_edge_attributes(filtered_graph, attribute) if attribute else {} # Draw edge labels if attribute is provided if attribute: nx.draw_networkx_edge_labels(filtered_graph, pos, edge_labels=edge_weights) nx.draw( filtered_graph, pos, with_labels=True, node_size=550, alpha=0.8, font_size=8, edge_color="black", labels=node_labels, ) plt.title("Example network of pairwise aligned proteins") plt.show()