Skip to content

Commit

Permalink
formatted imports
Browse files Browse the repository at this point in the history
  • Loading branch information
@haeussma
haeussma committed Apr 20, 2024
1 parent 9fc104e commit 8fcd586
Show file tree
Hide file tree
Showing 5 changed files with 134 additions and 107 deletions.
2 changes: 1 addition & 1 deletion pyeed/core/alignment.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,8 @@
from IPython.display import clear_output
from joblib import Parallel, cpu_count, delayed
from pydantic import Field, validator
from rich.status import Status
from rich.console import Console
from rich.status import Status
from sdRDM.base.listplus import ListPlus
from sdRDM.base.utils import forge_signature
from tqdm import tqdm
Expand Down
4 changes: 1 addition & 3 deletions pyeed/core/proteininfo.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -7,13 +7,11 @@
from Bio.Blast import NCBIXML
from IPython.display import clear_output
from pydantic import Field
from rich.status import Status
from rich.console import Console
from rich.status import Status
from sdRDM.base.listplus import ListPlus
from sdRDM.base.utils import forge_signature



from pyeed.container.abstract_container import Blastp
from pyeed.core.abstractsequence import AbstractSequence
from pyeed.core.citation import Citation
Expand Down
3 changes: 1 addition & 2 deletions pyeed/core/site.py
View file
Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
from typing import Dict, List, Optional
from uuid import uuid4

import validators
from lxml.etree import _Element
Expand All @@ -9,8 +10,6 @@

from .abstractannotation import AbstractAnnotation

from uuid import uuid4


class Site(
AbstractAnnotation,
Expand Down
228 changes: 128 additions & 100 deletions pyeed/network/network.py
View file
Original file line number Diff line number Diff line change
@@ -1,23 +1,17 @@
import pandas as pd
from tqdm import tqdm
from itertools import combinations
from typing import List, Optional

import networkx as nx
import py4cytoscape as p4c
import plotly.express as px
import plotly.graph_objects as go
from typing import List, Optional
from itertools import combinations
import py4cytoscape as p4c
from joblib import Parallel, cpu_count, delayed
from pydantic import BaseModel, Field
from Bio.Align import Alignment as BioAlignment
from joblib import Parallel, delayed, cpu_count
from typing import List, Optional, Union, Tuple, TYPE_CHECKING
from concurrent.futures import ThreadPoolExecutor, as_completed
from py4cytoscape import gen_node_color_map, gen_node_size_map, scheme_c_number_continuous
from tqdm import tqdm

from pyeed.core.sequence import Sequence
from pyeed.aligners.pairwise import PairwiseAligner
from pyeed.core.abstractsequence import AbstractSequence
from pyeed.core.pairwisealignment import PairwiseAlignment
from pyeed.core.proteininfo import ProteinInfo
from pyeed.aligners.pairwise import PairwiseAligner


class SequenceNetwork(BaseModel):
Expand Down Expand Up @@ -46,7 +40,6 @@ class Config:
nx.Graph: lambda g: nx.node_link_data(g),
}


sequences: Optional[List[AbstractSequence]] = Field(
default=[],
description="List of sequences to be compared",
Expand Down Expand Up @@ -87,8 +80,15 @@ class Config:
description="Network graph with networkx",
)


def __init__(self, sequences: List[AbstractSequence], weight: str = "identity", color: str = "name", threshold: float = None, label: str = "name", dimensions: int = 3):
def __init__(
self,
sequences: List[AbstractSequence],
weight: str = "identity",
color: str = "name",
threshold: float = None,
label: str = "name",
dimensions: int = 3,
):
super().__init__()
self.sequences = sequences
self.threshold = threshold
Expand All @@ -97,7 +97,9 @@ def add_target(self, target: AbstractSequence):
if target.source_id not in self.targets:
self.targets.append(target.source_id)

def _create_pairwise_alignments(self, input_sequences, aligner: "PairwiseAligner", **kwargs):
def _create_pairwise_alignments(
self, input_sequences, aligner: "PairwiseAligner", **kwargs
):
"""
Creates pairwise alignments between sequences.
Expand All @@ -117,7 +119,6 @@ def _create_pairwise_alignments(self, input_sequences, aligner: "PairwiseAligner

# Pairwise alignment
if len(input_sequences) == 2:

pairwise_aligner = aligner(
sequences=[
input_sequences[0].sequence,
Expand Down Expand Up @@ -151,51 +152,56 @@ def _create_pairwise_alignments(self, input_sequences, aligner: "PairwiseAligner
)

return alignments, pairs, aligners[0].mode


else:
raise ValueError(
f"Alignment Error. Recieved {len(input_sequences)} sequences. Expected 2."
)

def _process_pair(self, alignment, pair):
shorter_seq = min(pair, key=lambda x: len(x.sequence))

identities = alignment.counts().identities
identity = identities / len(shorter_seq.sequence)
return (
pair[0].source_id, pair[1].source_id,
{
'identity': identity,
'gaps': 1 / (alignment.counts().gaps + 1),
'mismatches': 1 / (alignment.counts().mismatches + 1),
'score': alignment.score
}
)


def _process_pair(self, alignment, pair):
shorter_seq = min(pair, key=lambda x: len(x.sequence))

identities = alignment.counts().identities
identity = identities / len(shorter_seq.sequence)
return (
pair[0].source_id,
pair[1].source_id,
{
"identity": identity,
"gaps": 1 / (alignment.counts().gaps + 1),
"mismatches": 1 / (alignment.counts().mismatches + 1),
"score": alignment.score,
},
)

def _process_sequence(self, sequence):
return (sequence.source_id, {
'name': sequence.name,
'sequence': sequence.sequence,
'family_name': sequence.family_name,
'domain': sequence.organism.domain,
'kingdom': sequence.organism.kingdom,
'phylum': sequence.organism.phylum,
'tax_class': sequence.organism.tax_class,
'order': sequence.organism.order,
'family': sequence.organism.family,
'genus': sequence.organism.genus,
'species': sequence.organism.species,
'ec_number': sequence.ec_number,
'mol_weight': sequence.mol_weight,
'taxonomy_id': sequence.organism.taxonomy_id,
})
return (
sequence.source_id,
{
"name": sequence.name,
"sequence": sequence.sequence,
"family_name": sequence.family_name,
"domain": sequence.organism.domain,
"kingdom": sequence.organism.kingdom,
"phylum": sequence.organism.phylum,
"tax_class": sequence.organism.tax_class,
"order": sequence.organism.order,
"family": sequence.organism.family,
"genus": sequence.organism.genus,
"species": sequence.organism.species,
"ec_number": sequence.ec_number,
"mol_weight": sequence.mol_weight,
"taxonomy_id": sequence.organism.taxonomy_id,
},
)

def create_networkx_graph(self):
# TODO: check if such a graph already exists
self.network = nx.Graph()
# create the alignments
alignments, pairs, mode = self._create_pairwise_alignments(self.sequences, PairwiseAligner, mode="global")
alignments, pairs, mode = self._create_pairwise_alignments(
self.sequences, PairwiseAligner, mode="global"
)
# Add nodes and assign node attributes
if all([isinstance(sequence, ProteinInfo) for sequence in self.sequences]):
# the node data list
Expand Down Expand Up @@ -232,37 +238,56 @@ def create_networkx_graph(self):

self.network.add_edges_from(edge_data)




def create_cytoscope_graph(self, collection: str, title: str, threshold: float = 0.8):
def create_cytoscope_graph(
self, collection: str, title: str, threshold: float = 0.8
):
# assert that the cytoscope API is running and cytoscope is running in the background
assert p4c.cytoscape_ping(), "Cytoscape is not running in the background"
assert p4c.cytoscape_version_info(), "Cytoscape API is not running"
# TODO fix that the title an dcollection is set unique in order to avoid confuing when using the software
p4c.create_network_from_networkx(self.graph, collection=collection, title=title+'_'+str(threshold))

p4c.create_network_from_networkx(
self.graph, collection=collection, title=title + "_" + str(threshold)
)

def set_layout(self, layout_name: str="force-directed", properties_dict: dict={"defaultSpringCoefficient": 4e-5, "defaultSpringLength": 100, "defaultNodeMass": 3, "numIterations": 50,}):
def set_layout(
self,
layout_name: str = "force-directed",
properties_dict: dict = {
"defaultSpringCoefficient": 4e-5,
"defaultSpringLength": 100,
"defaultNodeMass": 3,
"numIterations": 50,
},
):
p4c.layout_network(layout_name)
# ['numIterations', 'defaultSpringCoefficient', 'defaultSpringLength', 'defaultNodeMass', 'isDeterministic', 'singlePartition']
p4c.set_layout_properties(layout_name=layout_name, properties_dict=properties_dict)
p4c.set_layout_properties(
layout_name=layout_name, properties_dict=properties_dict
)

p4c.scale_layout(axis='Both Axis', scale_factor=1.0)
p4c.scale_layout(axis="Both Axis", scale_factor=1.0)
p4c.layout_network(layout_name)

def filter_cytoscope_edges_by_parameter(self, name: str, parameter: str, min_val: float, max_val: float):
def filter_cytoscope_edges_by_parameter(
self, name: str, parameter: str, min_val: float, max_val: float
):
# this is a filter for the network in cytoscope
# here the nodes and edges not relevant are filtered out
p4c.create_column_filter(name, parameter, [min_val, max_val] , "BETWEEN", type='edges', apply=True, hide=True)
p4c.create_column_filter(
name,
parameter,
[min_val, max_val],
"BETWEEN",
type="edges",
apply=True,
hide=True,
)

def calculate_degree(self):
# Calculate degree of nodes with filtering
g = p4c.create_networkx_from_network()
nx.set_node_attributes(g, dict(nx.degree(self.graph)), "degree")
p4c.create_network_from_networkx(g, collection='tet', title='hfjakd')


p4c.create_network_from_networkx(g, collection="tet", title="hfjakd")

@property
def graph(self) -> nx.Graph:
Expand All @@ -283,40 +308,43 @@ def graph(self) -> nx.Graph:
- The graph can be visualized in 2D or 3D using the visualize() method.
"""
graph = nx.Graph()
alignments, pairs, mode = self._create_pairwise_alignments(self.sequences, PairwiseAligner, mode="global")
alignments, pairs, mode = self._create_pairwise_alignments(
self.sequences, PairwiseAligner, mode="global"
)

# Add nodes and assign node attributes
# TODO die Aminosäuren sequenz soll jetzt auch noch da rein
if all([isinstance(sequence, ProteinInfo) for sequence in self.sequences]):

node_data = []

def process_sequence(sequence):
return (sequence.source_id, {
'name': sequence.name,
'family_name': sequence.family_name,
'domain': sequence.organism.domain,
'kingdom': sequence.organism.kingdom,
'phylum': sequence.organism.phylum,
'tax_class': sequence.organism.tax_class,
'order': sequence.organism.order,
'family': sequence.organism.family,
'genus': sequence.organism.genus,
'species': sequence.organism.species,
'ec_number': sequence.ec_number,
'mol_weight': sequence.mol_weight,
'taxonomy_id': sequence.organism.taxonomy_id,
})

print('Processing Sequences in for loop')
return (
sequence.source_id,
{
"name": sequence.name,
"family_name": sequence.family_name,
"domain": sequence.organism.domain,
"kingdom": sequence.organism.kingdom,
"phylum": sequence.organism.phylum,
"tax_class": sequence.organism.tax_class,
"order": sequence.organism.order,
"family": sequence.organism.family,
"genus": sequence.organism.genus,
"species": sequence.organism.species,
"ec_number": sequence.ec_number,
"mol_weight": sequence.mol_weight,
"taxonomy_id": sequence.organism.taxonomy_id,
},
)

print("Processing Sequences in for loop")

for sequence in self.sequences:
node_data.append(process_sequence(sequence))
print('Adding in networx')
print("Adding in networx")

graph.add_nodes_from(node_data)
print('nodes added ')

print("nodes added ")

else:
for sequence in self.sequences:
Expand All @@ -334,36 +362,36 @@ def process_sequence(sequence):
taxonomy_id=sequence.organism.taxonomy_id,
)


# create a datafarme for the egdes
edge_data = []

def process_pair(alignment, pair):
shorter_seq = min(pair, key=lambda x: len(x.sequence))

identities = alignment.counts().identities
identity = identities / len(shorter_seq.sequence)
if self.threshold is not None and identity < self.threshold:
return None
return (
pair[0].source_id, pair[1].source_id,
pair[0].source_id,
pair[1].source_id,
{
'identity': identity,
'gaps': 1 / (alignment.counts().gaps + 1),
'mismatches': 1 / (alignment.counts().mismatches + 1),
'score': alignment.score
}
"identity": identity,
"gaps": 1 / (alignment.counts().gaps + 1),
"mismatches": 1 / (alignment.counts().mismatches + 1),
"score": alignment.score,
},
)

print('Processing Pairs in for loop')
print("Processing Pairs in for loop")

for alignment, pair in zip(alignments, pairs):
edge = process_pair(alignment, pair)
if edge:
edge_data.append(edge)
print('Adding in networx')
print("Adding in networx")
graph.add_edges_from(edge_data)
print('edges added ')
print("edges added ")
# Calculate betweenness centrality
betweenness = nx.betweenness_centrality(graph)
for node, betw_value in betweenness.items():
Expand Down
Loading

0 comments on commit 8fcd586

Please sign in to comment.