Key objective 1

Subsections of Key objective 1

Part 1: Construction of the preliminary AOP table for AOP networks

The AOP project ► Key objective 1

Author: Shakira Agata

This Jupyter Notebook describes the steps needed to create the preliminary table that is needed for construction of an AOP network that focuses on inflammatory processes in human organ systems. This notebook focuses on the organ systems: brain, liver, kidney and lung due to research interests of the author. The preliminary table will contain the following information: AOP (adverse outcome pathway), AOP title, KE name (key event name), AO (adverse outcome), AO title, KER (key event relationship), KER ID and title of the organ system. To achieve this result, three SPARQLqueries will be executed against AOP-Wiki RDF to extract the AOPs that are related to inflammatory processes along with their respective upstreamKEs, downstreamKEs and KERs. The detailed steps are outlined in the following eight sections:

  • Section 1: System preparation for generation of AOP-Wiki RDF SPARQL queries
  • Section 2: Execution of the first SPARQL query
  • Section 3: Merging of two datasets
  • Section 4: Filtering of results
  • Section 5: Execution of second SPARQL query
  • Section 6: Execution of third SPARQL query
  • Section 7: Merging results from section 4-6
  • Section 8: Metadata

Section 1: System preparation for generation of AOP-Wiki RDF SPARQL queries

In section 1 and section 2, the system requirements for this notebook will be fulfilled followed by the generation of the first SPARQL query. This query is run against AOP-Wiki RDF.

Step 1: Install SPARQLWRAPPER which is the Python wrapper you need to be able to run the query.

pip install sparqlwrapper
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Note: you may need to restart the kernel to use updated packages.

Step 2: Import sys, sparqlwrapper and pandas which are packages that allow you to interact with variables and functions and manipulate data. For the usage of Pandas, the maximum column width is set to ´None´ as Pandas version 2.2.2 does not allow for non-negative integer to be set.

import sys

!{sys.executable} -m pip install watermark
from SPARQLWrapper import SPARQLWrapper, JSON


import pandas as pd

pd.set_option('display.max_colwidth', None)
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Step 3: Create the variable: AOPWikiSPARQL for the SPARQL wrapper and set the endpoint to JSON to ensure results are displayed in human-readable format.

AOPWikiSPARQL = SPARQLWrapper("https://aopwiki.rdf.bigcat-bioinformatics.org/sparql/")
AOPWikiSPARQL.setReturnFormat(JSON)

Step 4: Define the triple, coretypes, ontologies and identifiers which are used to codify the semantic data in AOP-Wiki.

triple = ['subject','predicate','object']
coretypes = ['aopo:AdverseOutcomePathway','aopo:KeyEvent','aopo:KeyEventRelationship','ncbitaxon:131567','go:0008150','pato:0001241','pato:0000001','aopo:CellTypeContext','aopo:OrganContext','nci:C54571','cheminf:000000'] 
ontologies = ['http://aopkb.org/aop_ontology#','http://edamontology.org/','http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#','http://purl.bioontology.org/ontology/NCBITAXON/','http://purl.obolibrary.org/obo/MMO','http://purl.obolibrary.org/obo/CL_','http://purl.obolibrary.org/obo/UBERON_','http://purl.obolibrary.org/obo/MI_','http://purl.obolibrary.org/obo/MP_','http://purl.org/commons/record/mesh/','http://purl.obolibrary.org/obo/HP_','http://purl.obolibrary.org/obo/PCO_','http://purl.obolibrary.org/obo/NBO_','http://purl.obolibrary.org/obo/VT_','http://purl.obolibrary.org/obo/PR_','http://purl.obolibrary.org/obo/CHEBI_','http://purl.org/sig/ont/fma/fma','http://xmlns.com/foaf/0.1/','http://www.w3.org/2004/02/skos/core#','http://www.w3.org/2000/01/rdf-schema#','http://www.w3.org/1999/02/22-rdf-syntax-ns#','http://semanticscience.org/resource/CHEMINF_','http://purl.obolibrary.org/obo/GO_','http://purl.org/dc/terms/','http://purl.org/dc/elements/1.1/','http://purl.obolibrary.org/obo/PATO_']
identifiers = []

Section 2: Execution of the first SPARQL query

Step 5: Define and run the SPARQL query to extract the inflammatory-related AOPs. Subsequently, convert the results to a pandas dataframe (df1) to display the results.

first_sparqlquery= '''SELECT DISTINCT ?AOP ?AOPtitle ?KE ?KEname
WHERE {
?KE a aopo:KeyEvent ;
dc:identifier ?KElookup ;
dc:title ?KEname .
?AOP a aopo:AdverseOutcomePathway ;
aopo:has_key_event ?KElookup ;
dc:title ?AOPtitle .

FILTER regex(?KEname, "inflammation|inflammatory", "i")
}
ORDER BY DESC(?AOP)'''

AOPWikiSPARQL.setQuery(first_sparqlquery)
first_results = AOPWikiSPARQL.query().convert()

first_data= first_results["results"]["bindings"]

columns = [{
    "AOP": item["AOP"]["value"],
    "AOPtitle": item["AOPtitle"]["value"],
    "KE": item["KE"]["value"],
    "KEname": item["KEname"]["value"]
    
} for item in first_data]

df1= pd.DataFrame(columns)
display(df1)

AOP AOPtitle KE KEname
0 https://identifiers.org/aop/62 AKT2 activation leading to hepatic steatosis https://identifiers.org/aop.events/486 systemic inflammation leading to hepatic steatosis
1 https://identifiers.org/aop/544 Inhibition of neuropathy target esterase leading to delayed neuropathy via increased inflammation https://identifiers.org/aop.events/149 Increase, Inflammation
2 https://identifiers.org/aop/535 Binding and activation of GPER leading to learning and memory impairments https://identifiers.org/aop.events/188 Neuroinflammation
3 https://identifiers.org/aop/511 The AOP framework on ROS-mediated oxidative stress induced vascular disrupting effects https://identifiers.org/aop.events/2009 Activation of inflammation pathway
4 https://identifiers.org/aop/507 Nrf2 inhibition leading to vascular disrupting effects via inflammation pathway https://identifiers.org/aop.events/2009 Activation of inflammation pathway
... ... ... ... ...
67 https://identifiers.org/aop/144 Endocytic lysosomal uptake leading to liver fibrosis https://identifiers.org/aop.events/1493 Increased Pro-inflammatory mediators
68 https://identifiers.org/aop/14 Glucocorticoid Receptor Activation Leading to Increased Disease Susceptibility https://identifiers.org/aop.events/152 Suppression, Inflammatory cytokines
69 https://identifiers.org/aop/12 Chronic binding of antagonist to N-methyl-D-aspartate receptors (NMDARs) during brain development leads to neurodegeneration with impairment in learning and memory in aging https://identifiers.org/aop.events/188 Neuroinflammation
70 https://identifiers.org/aop/115 Epithelial cytotoxicity leading to forestomach tumors (in mouse and rat) https://identifiers.org/aop.events/149 Increase, Inflammation
71 https://identifiers.org/aop/114 HPPD inhibition leading to corneal papillomas and carcinomas (in rat) https://identifiers.org/aop.events/777 Increase, Inflammation (corneal cells)

72 rows × 4 columns

Section 3: Merging of datasets

In this section, you will merge two datafiles: df1 which contains the previous SPARQL query results for inflammatory processes-related AOPs and table2 which is a snorql-csv file that contains additional AOPs that was created by Marvin Martens (supervisor of Shakira Agata). The snorql-csv file has five columns: AOP, AOPName, ao, aotitle and organ which were merged with df1.

Step 6: You first read and convert the snorql-csv file into JSON format.

table2= pd.read_excel('snorql-csv-1679052766.894.xlsx', sheet_name='snorql-csv-1679052766.894')
json_table2= table2.to_json()

Step 7: This is followed by converting the JSON formatted file into a pandas dataframe and verifying the result.

df2= pd.read_json("C:/Users/shaki/Downloads/csvjson.json")

Step 8: Next, you merge the two dataframes based on the shared ‘AOP’ column.

mergedJSONtable= pd.merge(df1,df2, on='AOP')

Section 4: Filtering of results

In this section, you filter the result of section 3 by removing AOPs that are not organ-system based i.e belonging to brain, kidney, liver or lung.

Step 9: The rows in column:‘organ’ that contain ‘other’ are filtered so that you only retain AOPs in the defined four organ systems (brain, liver, kidney and lung). This is done by using boolean function:’!=’ to only include the rows where organ is NOT defined as ‘Other’.

filtered_mergedJSONtable = mergedJSONtable[mergedJSONtable['organ'] != 'Other']

Section 5: Execution of second SPARQL query

In this section, you will run the second SPARQL query to retrieve/define the upstreamKEs and downstreamKEs from the AOPs retrieved in section 2.

Step 10: In preparation for the second SPARQL query, you first retrieve the uri’s for the AOPs so that the execution of the query takes less time and can be automated. This will be done with the following function where we select the ‘AOP’ column from the filtered_mergedJSONtable that contains the uri’s in the filtered_mergedJSONtable and join them (all 54).

values_AOPs = " ".join(f"<{AOP}>" for AOP in filtered_mergedJSONtable['AOP'])
print(values_AOPs)
<https://identifiers.org/aop/62> <https://identifiers.org/aop/48> <https://identifiers.org/aop/452> <https://identifiers.org/aop/452> <https://identifiers.org/aop/451> <https://identifiers.org/aop/451> <https://identifiers.org/aop/447> <https://identifiers.org/aop/429> <https://identifiers.org/aop/409> <https://identifiers.org/aop/409> <https://identifiers.org/aop/382> <https://identifiers.org/aop/38> <https://identifiers.org/aop/374> <https://identifiers.org/aop/362> <https://identifiers.org/aop/320> <https://identifiers.org/aop/320> <https://identifiers.org/aop/319> <https://identifiers.org/aop/303> <https://identifiers.org/aop/3> <https://identifiers.org/aop/280> <https://identifiers.org/aop/278> <https://identifiers.org/aop/27> <https://identifiers.org/aop/206> <https://identifiers.org/aop/173> <https://identifiers.org/aop/173> <https://identifiers.org/aop/171> <https://identifiers.org/aop/17> <https://identifiers.org/aop/17> <https://identifiers.org/aop/144> <https://identifiers.org/aop/12>

Step 11: Now you run the second SPARQL query to retrieve the KE, KEID and KEtitle for the respective AOPs. To do this, you need to use one curly bracket for values_AOPs as that marks the position for uri insertion and two surrounding curly brackets for values_AOPs to prevent identification of the curly brackets as placeholders (escaping).

second_sparqlsquery = f'''
SELECT DISTINCT ?AOP ?KE ?KEID ?KEtitle
WHERE {{
VALUES ?AOP {{ {values_AOPs} }}
?AOP a aopo:AdverseOutcomePathway ;
dc:title ?AOPName ;
aopo:has_key_event ?KE .
?KE a aopo:KeyEvent ; rdfs:label ?KEID ; dc:title ?KEtitle .
}}
'''

AOPWikiSPARQL.setQuery(second_sparqlsquery)
second_results = AOPWikiSPARQL.query().convert()

second_data= second_results["results"]["bindings"]

columns = [{
    
    "AOP": item["AOP"]["value"],
    "KE": item["KE"]["value"],
    "KEID": item["KEID"]["value"],
    "KEtitle": item["KEtitle"]["value"] 
    
} for item in second_data]

df3= pd.DataFrame(columns)
display(df3)

AOP KE KEID KEtitle
0 https://identifiers.org/aop/447 https://identifiers.org/aop.events/105 KE 105 Inhibition, Mitochondrial Electron Transport Chain Complexes
1 https://identifiers.org/aop/27 https://identifiers.org/aop.events/1115 KE 1115 Increase, Reactive oxygen species
2 https://identifiers.org/aop/303 https://identifiers.org/aop.events/1115 KE 1115 Increase, Reactive oxygen species
3 https://identifiers.org/aop/319 https://identifiers.org/aop.events/1115 KE 1115 Increase, Reactive oxygen species
4 https://identifiers.org/aop/382 https://identifiers.org/aop.events/1115 KE 1115 Increase, Reactive oxygen species
... ... ... ... ...
185 https://identifiers.org/aop/62 https://identifiers.org/aop.events/459 KE 459 Increased, Liver Steatosis
186 https://identifiers.org/aop/62 https://identifiers.org/aop.events/484 KE 484 Activation, AKT2
187 https://identifiers.org/aop/62 https://identifiers.org/aop.events/486 KE 486 systemic inflammation leading to hepatic steatosis
188 https://identifiers.org/aop/447 https://identifiers.org/aop.events/759 KE 759 Increased, Kidney Failure
189 https://identifiers.org/aop/451 https://identifiers.org/aop.events/780 KE 780 Increase, Cytotoxicity (epithelial cells)

190 rows × 4 columns

Section 6: Execution of third SPARQL query

In this section, you will run the third SPARQL query to retrieve/define the KERs for the upstreamKEs and downstreamKEs of the AOPs. This approach allows for easier integration of the output into the nodetable and edgetable of the AOP network (1).

Step 12: You run the following query to retrieve the upstreamKEs, downstreamKEs and KERs.

third_sparqlsquery = f'''
SELECT DISTINCT ?AOP ?upstreamKE ?upstreamKEtitle ?downstreamKE ?downstreamKEtitle ?KER ?KERID
WHERE {{
VALUES ?AOP {{ {values_AOPs} }}
?AOP a aopo:AdverseOutcomePathway ;
dc:title ?AOPName ;
aopo:has_key_event_relationship ?KER .
?KER a aopo:KeyEventRelationship ;
rdfs:label ?KERID .

?KER aopo:has_upstream_key_event ?upstreamKE .
?upstreamKE dc:title ?upstreamKEtitle .

?KER aopo:has_downstream_key_event ?downstreamKE .
?downstreamKE dc:title ?downstreamKEtitle .
}}
'''
AOPWikiSPARQL.setQuery(third_sparqlsquery)
third_results = AOPWikiSPARQL.query().convert()

third_data= third_results["results"]["bindings"]

columns = [{
    "AOP": item["AOP"]["value"],
    "upstreamKE": item["upstreamKE"]["value"],
    "downstreamKE": item["downstreamKE"]["value"],
    "KER": item["KER"]["value"],
    "KERID": item["KERID"]["value"],
    "upstreamKEtitle": item["upstreamKEtitle"]["value"],
    "downstreamKEtitle": item["downstreamKEtitle"]["value"]
    
    
} for item in third_data]

df4= pd.DataFrame(columns)

Section 7: Merging results from section 4-6

In this section, you will merge the dataframes: filtered_mergedJSONtable (result of section 4), df3 (result of section 5) and df4 (result of section 6).

Step 13: The three dataframes are merged to get the final table that will be used to define the nodes and edges of the network in Py4Cytoscape. The final table will be the merged version of:

  • filtered_mergedJSONtable (AOP SPARQL query)
  • df3 (KE SPARQL query)
  • df4(KER/KERID SPARQL query).
first_merge = pd.merge(df3, df4)
final_result= pd.merge(filtered_mergedJSONtable,first_merge)

Step 14: Lastly the final_result table is stored in JSON-format in your laptop in preparation for the next Jupyter Notebook.

final_result.to_json('final_result.json')

Section 8: Metadata

Step 15: At last, the metadata belonging to this Jupyter Notebook is displayed which contains the version numbers of packages and system-set-up for interested users. This requires the usage of packages:Watermark and print_versions.

%load_ext watermark
!pip install print-versions
Requirement already satisfied: print-versions in c:\users\shaki\anaconda3\lib\site-packages (0.1.0)
%watermark
Last updated: 2025-04-26T20:54:28.526670+02:00

Python implementation: CPython
Python version       : 3.12.3
IPython version      : 8.25.0

Compiler    : MSC v.1938 64 bit (AMD64)
OS          : Windows
Release     : 11
Machine     : AMD64
Processor   : Intel64 Family 6 Model 140 Stepping 1, GenuineIntel
CPU cores   : 8
Architecture: 64bit
from print_versions import print_versions
print_versions(globals())
json==2.0.9
ipykernel==6.28.0
pandas==2.2.2
SPARQLWrapper==2.0.0

References:

  1. Martens, M., Evelo, C. T., & Willighagen, E. L. (2022). Providing Adverse Outcome Pathways from the AOP-Wiki in a Semantic Web Format to Increase Usability and Accessibility of the Content. Applied In Vitro Toxicology. https://github.com/marvinm2/AOPWikiRDF
  2. Martens M, Evelo CT, Willighagen EL. Providing Adverse Outcome Pathways from the AOP-Wiki in a Semantic Web Format to Increase Usability and Accessibility of the Content. Appl In Vitro Toxicol. 2022 Mar 1;8(1):2-13. doi: 10.1089/aivt.2021.0010. Epub 2022 Mar 17. PMID: 35388368; PMCID: PMC8978481.
  3. msx. Package for listing version of packages used in a Jupyter Notebook \[Internet\]. Stack Overflow. 2016. Available from: https://stackoverflow.com/questions/40428931/package-for-listing-version-of-packages-used-in-a-jupyter-notebook

Part 2: Construction of an human inflammatory stress response AOP network

The AOP project ► Key objective 1

Author: Shakira Agata

This Jupyter notebook describes the steps needed to construct an AOP network that focuses on inflammatory processes in human organ systems AOP network. Briefly the nodetable and edgetable were defined followed by construction of the AOP network and adaptation of the visual style with py4cytoscape. Py4cytoscape is a Python package that collaborates with Cytoscape for visualization of molecular networks and biological pathways. The detailed steps are outlined in the following eight sections:

  • Section 1: System preparation
  • Section 2: Data cleaning and preparation
  • Section 3: Data manipulation for node table
  • Section 4: Definition of the node table
  • Section 5: Definition of the edge table
  • Section 6: Construction of the AOP network
  • Section 7: Adaptation of stylistic aspects of the AOP network
  • Section 8: Metadata

Section 1: System preparation

In this section, you import pandas and py4cytoscape which are required for execution of this notebook.

Step 1: We import pandas and py4cytoscape. For py4cytoscape, you must first open Cytoscape on your laptop prior to running the code. If not, an error message will be shown.

import pandas as pd
import py4cytoscape as p4c
p4c.cytoscape_ping()
p4c.cytoscape_version_info()
You are connected to Cytoscape!





{'apiVersion': 'v1',
 'cytoscapeVersion': '3.10.1',
 'automationAPIVersion': '1.9.0',
 'py4cytoscapeVersion': '1.9.0'}

Section 2: Data cleaning and preparation

In this section, the result of Jupyter notebook: ‘Agata,Shakira-The AOP project-Part 1’ is imported and cleaned in preparation for definition of the nodetable and edgetable. The result of the first Jupyter Notebook was saved as ‘final_result.json’.

Step 2: You read the final_result table of the first Jupyter notebook into a new dataframe.

final_result= pd.read_json("C:/Users/shaki/Downloads/final_result.json")

Step 3: Now, you remove the redundant column: ‘AOPName’ as it contains the same output as the column:‘AOPtitle’.

final_result1= final_result.drop(columns='AOPName', axis=1)

Step 4: This is followed by the removal of duplicates in the KER column to prevent the visualization of duplicate identical interactions in the AOP network.

final_result2= final_result1.drop_duplicates(subset='KER', keep='first')

Step 5: Create a weight column and set the value to 1. This is a prerequisite for both the construction of the edgetable and adaptation of the visual style of the future AOP network. This will be done by using the assign function and copying the dataframe to prevent a slice dataframe.

final_result2 = final_result2.copy()
final_result2 = final_result2.assign(weight='1')

Step 6: You remove the columns: KE, KEID, KEname and KEtitle from the final_result2 dataframe to clean up the table. This columns were generated during the second SPARQL query, but can be removed from the final table as the definition of the nodetable and edgetable only requires the upstream key events, downstream key events and adverse outcomes (AO) columns.

Adaptedfinal_result2= final_result2.drop(['KE', 'KEname', 'KEID', 'KEtitle'], axis=1)

Step 7: You lastly change the column names: ‘upstreamKE’ and ‘downstreamKE’ to upstreamKEID and downstreamKEID for cohesiveness of the dataframe.

secondfinal_result2= Adaptedfinal_result2.rename(columns= {'upstreamKE': 'upstreamKEID', 'downstreamKE':'downstreamKEID'})

Section 3: Data manipulation for node table

In this section, functions will be constructed that allow for differentiation between molecular initiating events (MIE), Key events (KE) and Adverse outcomes (AO) in the AOP network. This is important for the correct establishment of directionality of the AOP network as MIEs precede KEs and KEs precede AOs.

Step 8: You will identify the most upstreamKEs as MIE using the function below. This is manually done as SPARQL queries cannot assign MIEs in the respective AOPs. The strategy behind these functions is to define the MIEs as the upstreamKEs that are not present in the downstreamKE column. This is logical as the most upstreamKE should not be present in the downstreamKE column. You first set the mostupstreamKE variable and then search our upstreamKEID column of the secondfinal_result2 for the MIEs that are present in the mostupstreamKE variable. Those that are not present in the mostupstreamKE get the type: ‘KE’. This is followed by searching the secondfinal_result2 table for the most downstreamKE as those get the assignment: ‘AO’. The downstreamKEs that are not AOs get the type: ‘KE’.

mostupstreamKE = set(secondfinal_result2['upstreamKEID']) - set(secondfinal_result2['downstreamKEID'])
secondfinal_result2['type'] = 'KE'

secondfinal_result2.loc[secondfinal_result2['upstreamKEID'].isin(mostupstreamKE), 'type'] = 'MIE'

secondfinal_result2.loc[secondfinal_result2['downstreamKEID'].isin(secondfinal_result2['ao']), 'type'] = 'AO'

Step 9: You can optionally save the finaltable to Excel and verify output. I included this line of code to verify my results with AOP-Wiki as an intermediate check.

secondfinal_result2.to_excel('checksecondfinal_result.xlsx')

Section 4: Definition of the node table

In this section, you will define your node table which will include: ’name’, ‘ID’ and ’type’.

Step 10: You will construct the node table by first making three dataframes for the nodes and then joining them together. The three dataframes are:

  1. MIE_nodes: These include the most upstreamKE from the upstreamKEs. The results of section 3 will be employed for this by selecting the values ‘MIE’ from the type column of the secondfinal_result2 table.

  2. KE_nodes: These include the downstreamKEs and upstreamKEs that were not MIE. The results of section 3 will be employed for this by selecting the values ‘KE’ from the type column of the secondfinal_result2 table.

  3. AO_node: These include your adverse outcomes. The results of section 3 will be employed for this by selecting the values ‘AO’ from the type column of the secondfinal_result2 table.

MIE_nodes = secondfinal_result2[secondfinal_result2['type'] == 'MIE'][['upstreamKEID', 'upstreamKEtitle', 'organ', 'type']].rename(columns={'upstreamKEID': 'id', 'upstreamKEtitle': 'name', 'organ': 'group'})

KE_nodes = secondfinal_result2[secondfinal_result2['type'] == 'KE'][['downstreamKEID', 'downstreamKEtitle', 'organ', 'type']].copy()
KE_nodes.rename(columns={'downstreamKEID': 'id', 'downstreamKEtitle': 'name', 'organ': 'group'}, inplace=True)

upstreamKE_nodes = secondfinal_result2[(secondfinal_result2['type'] != 'MIE') & (secondfinal_result2['type'] != 'AO')][['upstreamKEID', 'upstreamKEtitle', 'organ', 'type']]
upstreamKE_nodes.rename(columns={'upstreamKEID': 'id', 'upstreamKEtitle': 'name', 'organ': 'group'}, inplace=True)
KE_nodes = pd.concat([KE_nodes, upstreamKE_nodes], ignore_index=True)

AO_nodes = secondfinal_result2[secondfinal_result2['type'] == 'AO'][['ao', 'aotitle', 'organ', 'type']].rename(columns={'ao': 'id', 'aotitle': 'name', 'organ': 'group'})

nodetable = pd.concat([MIE_nodes, KE_nodes, AO_nodes], ignore_index=True)

nodetable

id name group type
0 https://identifiers.org/aop.events/486 systemic inflammation leading to hepatic steat... Liver MIE
1 https://identifiers.org/aop.events/875 Binding of agonist, Ionotropic glutamate recep... Brain MIE
2 https://identifiers.org/aop.events/2007 Non-coding RNA expression profile alteration Lung MIE
3 https://identifiers.org/aop.events/2007 Non-coding RNA expression profile alteration Lung MIE
4 https://identifiers.org/aop.events/2007 Non-coding RNA expression profile alteration Lung MIE
... ... ... ... ...
275 https://identifiers.org/aop.events/1276 Lung fibrosis Lung AO
276 https://identifiers.org/aop.events/1458 Pulmonary fibrosis Lung AO
277 https://identifiers.org/aop.events/1090 Increased, mesotheliomas Lung AO
278 https://identifiers.org/aop.events/341 Impairment, Learning and memory Brain AO
279 https://identifiers.org/aop.events/352 N/A, Neurodegeneration Brain AO

280 rows × 4 columns

Step 11: You remove the duplicates so that only the unique nodes remain in the list. With the following function, you remove duplicate nodes, but retain the nodes that have the same id, but different group. These nodes are valuable as those represent the KEs which are present in multiple organ systems.

finalnodetable= nodetable.drop_duplicates(['id', 'group'], keep='first')
finalnodetable

id name group type
0 https://identifiers.org/aop.events/486 systemic inflammation leading to hepatic steat... Liver MIE
1 https://identifiers.org/aop.events/875 Binding of agonist, Ionotropic glutamate recep... Brain MIE
2 https://identifiers.org/aop.events/2007 Non-coding RNA expression profile alteration Lung MIE
8 https://identifiers.org/aop.events/1495 Substance interaction with the lung resident c... Lung MIE
11 https://identifiers.org/aop.events/105 Inhibition, Mitochondrial Electron Transport C... Kidney MIE
... ... ... ... ...
269 https://identifiers.org/aop.events/896 Parkinsonian motor deficits Brain AO
270 https://identifiers.org/aop.events/1588 Bronchiolitis obliterans Lung AO
271 https://identifiers.org/aop.events/1549 Liver Injury Liver AO
272 https://identifiers.org/aop.events/357 Cholestasis, Pathology Liver AO
276 https://identifiers.org/aop.events/1458 Pulmonary fibrosis Lung AO

137 rows × 4 columns

Step 12: This list can also be saved to Excel to use for assignment of molecular pathways to the AOP network nodes present in this table. More details can be found in Jupyter Notebook: ‘Agata,Shakira-The AOP project-Part 3’.

finalnodetable.to_excel('finalnodetable.xlsx')

Section 5: Definition of the edge table

In this section, the edge table will be defined which includes: ‘source’, ’target’, ‘interaction’, ‘weight’, ‘group’ and ‘ID’.

Step 13: You will define these columns and select the columns from the final_result2 table. There are different methods to do so, but you will use the transpose() function in this case. Afterwards, you rename the columns to: source, target, interaction, weight and group respectively so the table can be read by Cytoscape.

preliminaryedgetable= pd.DataFrame([secondfinal_result2.upstreamKEID, secondfinal_result2.downstreamKEID, secondfinal_result2.KER, secondfinal_result2.KERID, secondfinal_result2.weight, secondfinal_result2.organ]).transpose()

edgetable= preliminaryedgetable.rename(columns= {'upstreamKEID': 'source', 'downstreamKEID':'target', 'KER':'interaction',  'KERID':'id', 'organ':'group'})

edgetable.to_excel('edgetable.xlsx')

Section 6: Construction of the AOP network

In this section, the AOP network will constructed using predefined py4cytoscape functions.

Step 14: You will first define the nodes, edges and title of the network.

p4c.create_network_from_data_frames(nodes=finalnodetable, edges=edgetable, title='Agata,S-The inflammatory-process related AOP network')
Applying default style...
Applying preferred layout





17292

Section 7: Adaptation of stylistic aspects

In this section, the style of your AOP network will be adapted and displayed.

Step 15: Prior to adapting the style of the network, you first need to import the stylistic mappings in py4cytoscape.

from py4cytoscape import get_node_color
from py4cytoscape import set_node_color_mapping
from py4cytoscape import gen_node_color_map
from py4cytoscape import set_edge_color_default
from py4cytoscape import set_node_color_default
from py4cytoscape import set_edge_source_arrow_shape_default
from py4cytoscape import set_edge_target_arrow_shape_default
from py4cytoscape import set_edge_target_arrow_color_default
from py4cytoscape import get_arrow_shapes
from py4cytoscape import get_edge_target_arrow_shape
from py4cytoscape import set_edge_target_arrow_shape_mapping
from py4cytoscape import gen_edge_arrow_map
from py4cytoscape import select_nodes
from py4cytoscape import get_table_value
from py4cytoscape import get_network_suid
from py4cytoscape import clear_selection
from py4cytoscape import set_node_color_bypass

Step 16: The style of the network can be changed with the following commands. In this Jupyter Notebook, the color of the nodes are changed according to the ’type’ assignment and edges are adapted to be arrow-shaped. This requires retrieval of the Cytoscape columns.

style_name = "default"
defaults = {'NODE_SHAPE': "ELLIPSE", 'NODE_SIZE': 50, 'EDGE_TRANSPARENCY': 140, 'NODE_LABEL_POSITION': "C,C,c,0.00,0.00"}
nodeLabels = p4c.map_visual_property('node label', 'name', 'p') 
edgeWidth = p4c.map_visual_property('edge width', 'weight', 'p') 
arrowShapes = p4c.map_visual_property('Edge Target Arrow Shape','interaction', 'd')
p4c.create_visual_style(style_name, defaults, [nodeLabels, edgeWidth])
p4c.set_visual_style(style_name)
{'message': 'Visual Style applied.'}
set_edge_target_arrow_shape_default('ARROW', style_name='default')
set_edge_target_arrow_color_default('#000000', style_name='default')
''
Newtable= p4c.get_table_columns()
Newtable

SUID shared name id group type degree.layout name selected
17665 17665 https://identifiers.org/aop.events/1276 https://identifiers.org/aop.events/1276 Lung AO NaN Lung fibrosis False
17410 17410 https://identifiers.org/aop.events/2010 https://identifiers.org/aop.events/2010 Lung KE NaN Pulmonary inflammation False
17668 17668 https://identifiers.org/aop.events/344 https://identifiers.org/aop.events/344 Liver AO NaN N/A, Liver fibrosis False
17413 17413 https://identifiers.org/aop.events/2013 https://identifiers.org/aop.events/2013 Lung KE NaN Airway remodeling False
17671 17671 https://identifiers.org/aop.events/1841 https://identifiers.org/aop.events/1841 Brain AO NaN Encephalitis False
... ... ... ... ... ... ... ... ...
17401 17401 https://identifiers.org/aop.events/389 https://identifiers.org/aop.events/389 Brain KE NaN Increased, Intracellular Calcium overload False
17659 17659 https://identifiers.org/aop.events/1941 https://identifiers.org/aop.events/1941 Brain AO NaN Memory Loss False
17404 17404 https://identifiers.org/aop.events/177 https://identifiers.org/aop.events/177 Liver KE NaN Mitochondrial dysfunction False
17662 17662 https://identifiers.org/aop.events/1090 https://identifiers.org/aop.events/1090 Lung AO NaN Increased, mesotheliomas False
17407 17407 https://identifiers.org/aop.events/2011 https://identifiers.org/aop.events/2011 Lung KE NaN Emphysema False

122 rows × 8 columns

Step 17: The color of the nodes are changed by using the ‘select and color’ method I developed. With this, you first select the nodes, convert them to lists and change the color for the entire length of the lists with py4cytoscape’s bypass function. With this approach, a datatype must be a list as dataframes are determined to be ‘ambigous’. This approach also allows you to see the highlighted nodes in Cytoscape as verification while running the code.

Step 17a: Change appearance of MIE nodes to green.

MIEnodes_df = Newtable[Newtable['type'] == 'MIE']
MIEnodeslist = MIEnodes_df['name'].tolist()
p4c.select_nodes(MIEnodeslist,by_col='SUID')
{}
color = '#09d63a' 
new_colors = [color] * len(MIEnodeslist)
p4c.set_node_color_bypass(node_names=MIEnodeslist, new_colors=new_colors)
''

Step 17b: Change appearance of KE nodes to yellow

KEnodes_df = Newtable[Newtable['type'] == 'KE']
KEnodeslist = KEnodes_df['name'].tolist()
p4c.select_nodes(KEnodeslist, by_col='SUID')
{'nodes': [17596], 'edges': []}
color = '#f7ff00' 
new_colors = [color] * len(KEnodeslist)
p4c.set_node_color_bypass(node_names=KEnodeslist, new_colors=new_colors)
''

Step 17c: Changing appearance of the AO nodes to pink.

AO_nodes_df = Newtable[Newtable['type'] == 'AO']
AOnodeslist = AO_nodes_df['name'].tolist()
p4c.select_nodes(AOnodeslist, by_col='SUID')
{'nodes': [17596], 'edges': []}
color = '#ff28d5' 
new_colors = [color] * len(AOnodeslist)
p4c.set_node_color_bypass(node_names=AOnodeslist, new_colors=new_colors)

Section 8: Metadata

Step 18: At last, the metadata belonging to this Jupyter Notebook is displayed which contains the version numbers of packages and system-set-up for interested users. This requires the usage of packages:Watermark and print_versions.

%load_ext watermark
!pip install print-versions
Requirement already satisfied: print-versions in c:\users\shaki\anaconda3\lib\site-packages (0.1.0)
%watermark
Last updated: 2025-06-02T13:18:59.342286+02:00

Python implementation: CPython
Python version       : 3.12.3
IPython version      : 8.25.0

Compiler    : MSC v.1938 64 bit (AMD64)
OS          : Windows
Release     : 11
Machine     : AMD64
Processor   : Intel64 Family 6 Model 140 Stepping 1, GenuineIntel
CPU cores   : 8
Architecture: 64bit
from print_versions import print_versions
print_versions(globals())
json==2.0.9
ipykernel==6.28.0
pandas==2.2.2
py4cytoscape==1.9.0

References:

  1. CyTargetLinker app update: A flexible solution for network extension in Cytoscape. Martina Kutmon, Friederike Ehrhart, Egon L Willighagen, Chris T. Evelo, Susan L. Coort F1000Research 2018, 7:743 doi: 10.12688/f1000research.14613.1
  2. Kutmon,M. 2024 . cyTargetLinker-automation.Maastricht:Github; \[accessed 2024 December 18\].https://github.com/CyTargetLinker/cytargetlinker-automation

Part 3: Enrichment of the human inflammatory stress response AOP network

The AOP project ► Key objective 1

Author: Shakira Agata

This Jupyter notebook describes the steps needed to enrich your AOP network by addition of molecular pathways from WikiPatwhays and genes through CyTargetLinker. This notebook is dependent on the output: ´Agata,completenodetable.xlsx´ which contains the KE-WP mapping results. This notebook is subdivided into the following ten sections:

  • Section 1: KE-WP mapping
  • Section 2: Importing the tables needed to construct the node table and edge table
  • Section 3: Loading the node table and edge table
  • Section 4: Defining the node table and edge table
  • Section 5: Construction of the molecular AOP network
  • Section 6: Adaptation of stylistic aspects of the molecular AOP network
  • Section 7: Extension of the molecular AOP network using Cytargetlinker and WikiPathways linkset
  • Section 8: Changing the visual style of CytargetLinker-extended molecular AOP ntwork
  • Section 9: Saving results
  • Section 10: Metadata

Section 1: System preparation

In this section, you will install the necessary packages for this notebook.

Step 1: You will import pandas and py4cytoscape by running the following code below.

import pandas as pd
import py4cytoscape as p4c
p4c.cytoscape_ping()
p4c.cytoscape_version_info()
You are connected to Cytoscape!





{'apiVersion': 'v1',
 'cytoscapeVersion': '3.10.1',
 'automationAPIVersion': '1.9.0',
 'py4cytoscapeVersion': '1.9.0'}

Section 2: KE-WP mapping

In this section, you will execute KE-WP mapping. This is a manual process that requires the assignment of molecular pathways to the MIEs, KEs and AOs that are present in the human inflammatory stress response AOP network. This requires usage of the decision tree method available at: https://github.com/marvinm2/KE-WP-mapping. Afterwards, your results should be tabulated in the following order to use in the next sections of the Jupyternotebook: ID (KEID), Name (KEtitle), Group, Type, KE level of organisation, Cell/organ term, WPID, WPtitle, Confidence score and Causative/responsive.

  • An example is provided at section 3.

Section 3: Loading the node table and edge table

In this section, the node table and edge table will be loaded and reorganised in preparation for section 4.

Step 2: You will now import the node table which contains the molecular pathway information. You will rename the columns: ‘Type’ to ’type’ and ‘Causative/responsive’ to ‘association type’ for coherence of the table.

nodetable0 = pd.read_excel('Agata,completenodetable.xlsx')
nodetable0.rename(columns={'Type': 'type', 'Causative/responsive': 'Association'}, inplace=True
Note

For KE-WP mapping, it is advised to use: https://github.com/marvinm2/KE-WP-mapping to ensure smoother documentation of the KE-WP matches.

Section 4: Defining the nodetable and edgetable

In this section you will load your node table and edgetable.

Step 3: First, the nodes of the network are defined as MIE, KE, AO and molecular pathway. You will do this by creating a new dataframe: ‘molecularpathwaytable’ for the molecular pathway information and adding a type: ‘Molecular pathway’ column to it. This ensures easier integration in the final node table using the ‘pd.concat’ function and manipulation of the future molecular AOP network.

preliminarymolecularpathway_table= pd.DataFrame([nodetable0.WPID, nodetable0.WPtitle, nodetable0.Group, nodetable0.Association]).transpose()
molecularpathwaytable= preliminarymolecularpathway_table.rename(columns= {'WPID': 'id', 'WPtitle':'name', 'Group':'group', 'Association':'Association type'})
molecularpathwaytable

id name group Association type
0 WP5095 Overview of proinflammatory and profibrotic me... Liver Responsive
1 WP5083 Neuroinflammation and glutamatergic signaling Brain Other
2 WP1545 miRNAs involved in DNA damage response Lung Causative
3 WP1530 miRNA regulation of DNA damage response Lung Other
4 WP4655 Cytosolic DNA-sensing pathway Lung Responsive
... ... ... ... ...
287 WP408 Oxidative stress response Brain Causative
288 WP1772 Apoptosis modulation and signaling Brain Causative
289 WP254 Apoptosis Brain Causative
290 WP3676 BDNF-TrkB signaling Brain Causative
291 WP5477 Molecular pathway for oxidative stress Brain Causative

292 rows × 4 columns

molecularpathwaytable['type'] = 'Molecular pathway'
molecularpathwaytable.dropna(inplace=True)
molecularpathwaytable

id name group Association type type
0 WP5095 Overview of proinflammatory and profibrotic me... Liver Responsive Molecular pathway
1 WP5083 Neuroinflammation and glutamatergic signaling Brain Other Molecular pathway
2 WP1545 miRNAs involved in DNA damage response Lung Causative Molecular pathway
3 WP1530 miRNA regulation of DNA damage response Lung Other Molecular pathway
4 WP4655 Cytosolic DNA-sensing pathway Lung Responsive Molecular pathway
... ... ... ... ... ...
287 WP408 Oxidative stress response Brain Causative Molecular pathway
288 WP1772 Apoptosis modulation and signaling Brain Causative Molecular pathway
289 WP254 Apoptosis Brain Causative Molecular pathway
290 WP3676 BDNF-TrkB signaling Brain Causative Molecular pathway
291 WP5477 Molecular pathway for oxidative stress Brain Causative Molecular pathway

275 rows × 5 columns

Step 4: The nodes derived from the node table will be joined with the formed molecularpathway table to form the final nodetable. This table organises the nodes in a way that you can easily select node type with organ-system location. For the molecular pathways, this table also allows you to see the association type for the respective molecular pathways.

MIE_nodes = nodetable0[nodetable0['type'] == 'MIE'][['ID (KEID)', 'Name (KEtitle)', 'Group', 'type']].rename(columns={'ID (KEID)': 'id', 'Name (KEtitle)': 'name', 'Group': 'group'})

KE_nodes = nodetable0[nodetable0['type'] == 'KE'][['ID (KEID)', 'Name (KEtitle)', 'Group', 'type']].copy()
KE_nodes.rename(columns={'ID (KEID)': 'id', 'Name (KEtitle)': 'name', 'Group': 'group'}, inplace=True)

AO_nodes = nodetable0[nodetable0['type'] == 'AO'][['ID (KEID)', 'Name (KEtitle)', 'Group', 'type']].rename(columns={'ID (KEID)': 'id', 'Name (KEtitle)': 'name', 'Group': 'group'})

nodetable = pd.concat([MIE_nodes, KE_nodes, AO_nodes, molecularpathwaytable], ignore_index=True)

nodetable

id name group type Association type
0 https://identifiers.org/aop.events/486 systemic inflammation leading to hepatic steat... Liver MIE NaN
1 https://identifiers.org/aop.events/875 Binding of agonist, Ionotropic glutamate recep... Brain MIE NaN
2 https://identifiers.org/aop.events/2007 Non-coding RNA expression profile alteration Lung MIE NaN
3 https://identifiers.org/aop.events/2007 Non-coding RNA expression profile alteration Lung MIE NaN
4 https://identifiers.org/aop.events/1495 Substance interaction with the lung resident c... Lung MIE NaN
... ... ... ... ... ...
562 WP408 Oxidative stress response Brain Molecular pathway Causative
563 WP1772 Apoptosis modulation and signaling Brain Molecular pathway Causative
564 WP254 Apoptosis Brain Molecular pathway Causative
565 WP3676 BDNF-TrkB signaling Brain Molecular pathway Causative
566 WP5477 Molecular pathway for oxidative stress Brain Molecular pathway Causative

567 rows × 5 columns

Step 5: You will load the edge table in the dataframe: ’edgetable1’

edgetable1= pd.read_excel('edgetable.xlsx')

Step 6: Now edgetable2 will be created which will contain the confidence score and association type for the molecular pathways. This is needed as edgetable1 was made before assignment of molecular pathways to the inflammatory-related AOP network in the previous notebook. Secondly, you have to establish the interactions between the molecular pathways and the MIEs,KEs and AOs so that they are present in the network with the following function.

edgetable2 = pd.DataFrame({
    'source': nodetable0['ID (KEID)'], 
    'target': nodetable0['WPID'], 
    'interaction': ['interacts'] * len(nodetable0), 
    'group' : nodetable0['Group'],
    'confidence score': nodetable0['Confidence score'],
    'Association type': nodetable0['Association']
})

edgetable2

source target interaction group confidence score Association type
0 https://identifiers.org/aop.events/486 WP5095 interacts Liver Medium Responsive
1 https://identifiers.org/aop.events/875 WP5083 interacts Brain Medium Other
2 https://identifiers.org/aop.events/2007 WP1545 interacts Lung Medium Causative
3 https://identifiers.org/aop.events/2007 WP1530 interacts Lung Medium Other
4 https://identifiers.org/aop.events/1495 WP4655 interacts Lung High Responsive
... ... ... ... ... ... ...
287 https://identifiers.org/aop.events/352 WP408 interacts Brain High Causative
288 https://identifiers.org/aop.events/352 WP1772 interacts Brain High Causative
289 https://identifiers.org/aop.events/352 WP254 interacts Brain High Causative
290 https://identifiers.org/aop.events/352 WP3676 interacts Brain High Causative
291 https://identifiers.org/aop.events/352 WP5477 interacts Brain High Causative

292 rows × 6 columns

Step 7: For edgetable 2, you also need to identify the valid edges.

edgetable_2 = edgetable2[edgetable2['source'].isin(nodetable['id']) & edgetable2['target'].isin(nodetable['id'])]
edgetable_2

source target interaction group confidence score Association type
0 https://identifiers.org/aop.events/486 WP5095 interacts Liver Medium Responsive
1 https://identifiers.org/aop.events/875 WP5083 interacts Brain Medium Other
2 https://identifiers.org/aop.events/2007 WP1545 interacts Lung Medium Causative
3 https://identifiers.org/aop.events/2007 WP1530 interacts Lung Medium Other
4 https://identifiers.org/aop.events/1495 WP4655 interacts Lung High Responsive
... ... ... ... ... ... ...
287 https://identifiers.org/aop.events/352 WP408 interacts Brain High Causative
288 https://identifiers.org/aop.events/352 WP1772 interacts Brain High Causative
289 https://identifiers.org/aop.events/352 WP254 interacts Brain High Causative
290 https://identifiers.org/aop.events/352 WP3676 interacts Brain High Causative
291 https://identifiers.org/aop.events/352 WP5477 interacts Brain High Causative

275 rows × 6 columns

Step 8: You can now make the final edgetable by merging edgetable1 with the present edges of the edgetable2.

edgetable = pd.concat([edgetable1, edgetable_2], ignore_index=True)

Step 9: Lastly the additional spaces present in the node table and edge table will be removed. This is done to prevent parsing errors when constructing the network.

Step 9a: You first remove the additional spaces in the nodetable.

nodetable['id'] = nodetable['id'].str.strip()

Step 9b: You first remove the additional spaces for the source and target columns in the edgetable.

edgetable['source'] = edgetable['source'].str.strip()
edgetable['target'] = edgetable['target'].str.strip()

Section 5: Construction of the molecular AOP network

In this section, the molecular AOP network will be constructed.

Step 10: You can construct the molecular AOP network and display the output.

p4c.create_network_from_data_frames(nodes=nodetable, edges=edgetable, title='Agata,S.-Molecular inflammatory-process related AOP network')
Applying default style...
Applying preferred layout





51035

Section 6: Adaptation of stylistic aspects of the molecular AOP network

In this section you can change the stylistic aspects of your molecular AOP network.

Step 11: You first have to import the necessary functions.

from py4cytoscape import get_node_color
from py4cytoscape import set_node_color_mapping
from py4cytoscape import gen_node_color_map
from py4cytoscape import set_edge_color_default
from py4cytoscape import set_node_color_default
from py4cytoscape import set_edge_source_arrow_shape_default
from py4cytoscape import set_edge_target_arrow_shape_default
from py4cytoscape import get_arrow_shapes
from py4cytoscape import get_edge_target_arrow_shape
from py4cytoscape import set_edge_target_arrow_shape_mapping
from py4cytoscape import gen_edge_arrow_map
from py4cytoscape import select_nodes
from py4cytoscape import get_table_value
from py4cytoscape import get_network_suid
from py4cytoscape import clear_selection
from py4cytoscape import set_node_color_bypass
from py4cytoscape import set_edge_color_bypass
from py4cytoscape import set_edge_target_arrow_color_default

Step 12: Next, you can define the style for your network and set the nodelabels and edgewidth.

style_name = "default"
defaults = {'NODE_SHAPE': "ELLIPSE", 'NODE_SIZE': 50, 'EDGE_TRANSPARENCY': 140, 'NODE_LABEL_POSITION': "C,C,c,0.00,0.00"}
nodeLabels = p4c.map_visual_property('node label', 'name', 'p') 
edgeWidth = p4c.map_visual_property('edge width', 'weight', 'p') 
arrowShapes = p4c.map_visual_property('Edge Target Arrow Shape','interaction', 'd')
p4c.create_visual_style(style_name, defaults, [nodeLabels, edgeWidth])
p4c.set_visual_style(style_name)
{'message': 'Visual Style applied.'}

Step 13: You can change the edge color and target shape to show the directionality of your network.

set_edge_target_arrow_shape_default('ARROW', style_name='default')
set_edge_target_arrow_color_default('#ffffff', style_name='default')
''

Step 14: In preparation for step 15, you will retrieve the columns of the nodetable.

table_columns = p4c.get_table_columns()
table_columns

SUID shared name id group type Association type name selected
51201 51201 https://identifiers.org/aop.events/1943 https://identifiers.org/aop.events/1943 Brain KE None Hyperphosphorylation of Tau False
51204 51204 https://identifiers.org/aop.events/386 https://identifiers.org/aop.events/386 Brain KE None Decrease of neuronal network function False
51207 51207 https://identifiers.org/aop.events/1944 https://identifiers.org/aop.events/1944 Brain KE None Synaptic dysfunction False
51210 51210 https://identifiers.org/aop.events/1582 https://identifiers.org/aop.events/1582 Brain KE None Impaired axonial transport False
51213 51213 https://identifiers.org/aop.events/1942 https://identifiers.org/aop.events/1942 Brain KE None Accumulation, Cytosolic toxic Tau oligomers False
... ... ... ... ... ... ... ... ...
51705 51705 WP5485 WP5485 Brain Molecular pathway Causative Post-COVID neuroinflammation False
51195 51195 https://identifiers.org/aop.events/1392 https://identifiers.org/aop.events/1392 Brain KE None Oxidative Stress False
51708 51708 WP2371 WP2371 Brain Molecular pathway Other Parkinson's disease pathway False
51198 51198 https://identifiers.org/aop.events/1815 https://identifiers.org/aop.events/1815 Liver KE None Activation of ER stress False
51711 51711 WP4197 WP4197 Liver Molecular pathway Causative Immune response to tuberculosis False

213 rows × 8 columns

Step 15: You can change the color of the nodes by selection of the node type followed by list conversion and color change with the bypass function.

Step 15a: Changing appearance of the MIE nodes to green.

table_columns_df = table_columns[table_columns['type'] == 'MIE']
MIEnodeslist = table_columns_df['name'].tolist()
p4c.select_nodes(MIEnodeslist)
{}
color = '#09d63a' 
new_colors = [color] * len(MIEnodeslist)
p4c.set_node_color_bypass(node_names=MIEnodeslist, new_colors=new_colors, network='current')
''

Step 15b: Changing appearance of the KE nodes to yellow.

table_columns_df2 = table_columns[table_columns['type'] == 'KE']
KEnodeslist = table_columns_df2['name'].tolist()
p4c.select_nodes(KEnodeslist, by_col='SUID')
{'nodes': [51519, 51342], 'edges': []}
color = '#f7ff00' 
new_colors = [color] * len(KEnodeslist)
p4c.set_node_color_bypass(node_names=KEnodeslist, new_colors=new_colors, network='current')
''

Step 15c: Changing appearance of the AO nodes to pink.

table_columns_df3 = table_columns[table_columns['type'] == 'AO']
AOnodeslist = table_columns_df3['name'].tolist()
p4c.select_nodes(AOnodeslist)
{'nodes': [51519, 51342], 'edges': []}
color = '#ff28d5' 
new_colors = [color] * len(AOnodeslist)
p4c.set_node_color_bypass(node_names=AOnodeslist, new_colors=new_colors, network='current')
''

Step 15d: Changing appearance of the molecular pathway nodes to grey.

table_columns_df4 = table_columns[table_columns['type'] == 'Molecular pathway']
molecularpathwaynodeslist = table_columns_df4['name'].tolist()
p4c.select_nodes(molecularpathwaynodeslist)
{'nodes': [51519, 51342], 'edges': []}
color = '#414a4c' 
new_colors = [color] * len(molecularpathwaynodeslist)
p4c.set_node_color_bypass(node_names=molecularpathwaynodeslist, new_colors=new_colors, network='current')
''
set_node_color_default('#a7a5a5',style_name='default')
''

Section 7: Extension of the molecular AOP network using Cytargetlinker and WikiPathways linkset

In this section, the AOP network will be enriched with genes associated to the molecular pathways you matched to your MIEs, KEs and AOs. This will be done using CyTargetLinker.

Step 16: You first install the CyTargetLinker app and check its functionality and status.

p4c.install_app('CyTargetLinker')
{}





{}
p4c.get_app_information('CyTargetLinker')
{'app': 'CyTargetLinker',
 'descriptionName': 'Flexible network extension app',
 'version': '4.1.0'}
p4c.get_app_status('CyTargetLinker')
{'appName': 'CyTargetLinker', 'status': 'Installed'}
p4c.commands_get('cytargetlinker extend')
["Available arguments for 'cytargetlinker extend':",
 'direction',
 'idAttribute',
 'linkSetDirectory',
 'linkSetFiles',
 'network']

Step 17: You import the operating system (os) package in preparation for step 17. Os allows the user to interact with the operating system of Cytoscape.

import os

Step 18: Next you define the filepath by which you retrieve the WikiPathways linkset file in your laptop.

file_path = os.path.join(os.getcwd(), "wikipathways_hsa_20240410 (1).xgmml")
linkset = "linkSetFiles="+file_path
print(linkset)
linkSetFiles=C:\Users\shaki\Downloads\wikipathways_hsa_20240410 (1).xgmml

Step 19: You construct the string for the network extension including: LinkSetFiles, idAttribute and direction.

cmd_cytargetlinker = ['cytargetlinker','extend', linkset, 'idAttribute="id"', 'direction="BOTH"']
cmd_ctl = " ".join(cmd_cytargetlinker)
print(cmd_ctl)
cytargetlinker extend linkSetFiles=C:\Users\shaki\Downloads\wikipathways_hsa_20240410 (1).xgmml idAttribute="id" direction="BOTH"

Step 20: You lastly run the command for the network extension and display the output. You have to use commands.commands_get as this function converts the command string of step 18 into a CyREST query.

p4c.commands.commands_get(cmd_ctl)
['Extension step: 1',
 'Linkset: WikiPathways-20240410_Homo sapiens_20240410',
 'Added edges: 5758',
 'Added nodes: 2739']

Section 8: Changing the visual style of CytargetLinker-extended molecular AOP network

In this section, you will apply the Cytoscape’s preferred layout to the AOP network.

Step 21: You will apply the layout to your extended network which uses Cytoscape’s preferred layout for organising the network.

cmd_cytargetlinker = ['cytargetlinker','applyLayout', 'network="current"']
cmd_ctl = " ".join(cmd_cytargetlinker)
p4c.commands.commands_get(cmd_ctl)
[]

Step 22: You can now apply the previous visual style to our extended network and view the network in Cytoscape.

cmd_cytargetlinker = ['cytargetlinker','applyVisualstyle', 'network="current"']
cmd_ctl = " ".join(cmd_cytargetlinker)
p4c.commands.commands_get(cmd_ctl)
[]
style_name = "default"
defaults = {'NODE_SHAPE': "ELLIPSE", 'NODE_SIZE': 50, 'EDGE_TRANSPARENCY': 140, 'NODE_LABEL_POSITION': "C,C,c,0.00,0.00"}
nodeLabels = p4c.map_visual_property('node label', 'name', 'p') 
edgeWidth = p4c.map_visual_property('edge width', 'weight', 'p') 
arrowShapes = p4c.map_visual_property('Edge Target Arrow Shape','interaction', 'd')
p4c.create_visual_style(style_name, defaults, [nodeLabels, edgeWidth])
p4c.set_visual_style(style_name)
{'message': 'Visual Style applied.'}

Section 9: Saving results

In this section, the Cytoscape network along with the style settings and bypass settings are saved for the next Jupyternotebook:‘Agata,Shakira-The AOP project-Part 4’.

Step 23: You will save your molecular AOP network as a Cytoscape in preparation for the next part.

p4c.save_session('Agata,S.-Part4-Complete Molecular inflammation-process related AOP network')
This file has been overwritten.





{}

Section 10: Metadata

At last, the metadata belonging to this jupyternotebook is displayed which contains the version numbers of packages and system-set-up for interested users. This requires the usage of packages:Watermark and print_versions.

Step 24: At last, the metadata belonging to this jupyternotebook is displayed which contains the version numbers of packages and system-set-up for interested users. This requires the usage of packages:Watermark and print_versions.

%load_ext watermark
!pip install print-versions
Requirement already satisfied: print-versions in c:\users\shaki\anaconda3\lib\site-packages (0.1.0)
%watermark
Last updated: 2025-06-02T18:23:43.809510+02:00

Python implementation: CPython
Python version       : 3.12.3
IPython version      : 8.25.0

Compiler    : MSC v.1938 64 bit (AMD64)
OS          : Windows
Release     : 11
Machine     : AMD64
Processor   : Intel64 Family 6 Model 140 Stepping 1, GenuineIntel
CPU cores   : 8
Architecture: 64bit
from print_versions import print_versions
print_versions(globals())
json==2.0.9
ipykernel==6.28.0
numpy==1.26.4
pandas==2.2.2
ipywidgets==8.0.3
xarray==2023.6.0
py4cytoscape==1.9.0

References:

  1. CyTargetLinker app update: A flexible solution for network extension in Cytoscape. Martina Kutmon, Friederike Ehrhart, Egon L Willighagen, Chris T. Evelo, Susan L. Coort F1000Research 2018, 7:743 doi: 10.12688/f1000research.14613.1
  2. Kutmon,M. 2024 . cyTargetLinker-automation.Maastricht:Github; \[accessed 2024 December 18\].https://github.com/CyTargetLinker/cytargetlinker-automation