//localhost:4321/key-objective-2/index.html Hugo en //localhost:4321/key-objective-2/agatashakira-the-aop-project-part-4/index.html Mon, 01 Jan 0001 00:00:00 +0000 //localhost:4321/key-objective-2/agatashakira-the-aop-project-part-4/index.html The AOP project ► Key objective 2 Author: Shakira Agata This Jupyter Notebook describes the steps needed for the mapping of transcriptomics datasets: GSE109565, E-MEXP-2599 and E-MEXP-3583 in the enriched AOP network. This notebook is subdivided into the following seven sections: Section 1: System preparation Section 2: Retrieval of molecular inflammation-process related AOP network Section 3: Adaptation of gene node color of molecular inflammation-process related AOP network Section 4: Mapping of dataset:GSE109565 Section 4.1 PCB126 concentration 1 Section 4.2 PCB126 concentration 2 Section 4.3 PCB126 concentration 3 Section 4.4 Roundup Section 5: Mapping of dataset:E-MEXP-2599 Section 5.1 CdCl2 exposure time 1 Section 5.2 CdCl2 exposure time 2 Section 5.3 CsA exposure time 1 Section 5.4 CsA exposure time 2 Section 5.5 Diquat dibromide exposure time 1 Section 5.6 Diquat dibromide exposure time 2 Section 6: Mapping of dataset:E-MEXP-3583 Section 6.1 Ag+ exposure time 1 Section 6.2 Ag+ exposure time 2 Section 6.3 AgNP exposure time 1 Section 6.4 AgNP exposure time 2 Section 7: Metadata Section 1: System preparation In this section, you will import the required packages and tools you need for this Jupyternotebook. //localhost:4321/key-objective-2/agatashakira-the-aop-project-part5/index.html Mon, 01 Jan 0001 00:00:00 +0000 //localhost:4321/key-objective-2/agatashakira-the-aop-project-part5/index.html The AOP project ► Key objective 2 Author: Shakira Agata This Jupyter notebook describes the steps needed for the mapping of transcriptomics datasets in the constructed enriched AOP network. For this notebook, open license transcriptomics datasets were derived from ArrayExpress and Gene Expression Omnibus (GEO). These datasets were preprocessed followed by execution of statistical analysis to identify differential expression genes (DEG). The tabulation of differential gene expression data was subsequently mapped/integrated into the network. This notebook is subdivided into the following six sections: //localhost:4321/key-objective-2/agatashakira-the-aop-project-part-6/index.html Mon, 01 Jan 0001 00:00:00 +0000 //localhost:4321/key-objective-2/agatashakira-the-aop-project-part-6/index.html The AOP project ► Key objective 2 Author: Shakira Agata This Jupyter Notebook describes the steps for the execution of Overrepresentation analysis (ORA) from the GSEApy package on datasets:GSE109565, E-MEXP-3583, E-MEXP-2599, GSE44729 and E-GEOD-69851. This notebook is subdivided into the following seven sections: Section 1: System preparation Section 2: Overrepresentation analysis (ORA) for dataset:GSE109565 Section 2.1: Generation of background genelist Section 2.2: Generation of the genelists Section 2.3: Execution of ORA Section 2.4: Saving plots of ORA Section 3: Overrepresentation analysis (ORA) for dataset:E-MEXP-2583 Section 3.1: Generation of background genelist Section 3.2: Generation of the genelists Section 3.3: Execution of ORA Section 3.4: Saving plots of ORA Section 4 Overrepresentation analysis (ORA) for dataset:E-MEXP-2599 Section 4.1: Generation of background genelist Section 4.2: Generation of the genelists Section 4.3: Execution of ORA Section 4.4: Saving plots of ORA Section 5: Overrepresentation analysis (ORA) for dataset:GSE44729 Section 5.1: Generation of background genelist Section 5.2: Generation of the genelists Section 5.3: Execution of ORA Section 5.4: Saving plots of ORA Section 6: Overrepresentation analysis (ORA) for dataset:E-GEOD-69851 Section 6.1: Generation of background genelist Section 6.2: Generation of the genelists Section 6.3: Execution of ORA Section 6.4: Saving plots of ORA Section 7: Metadata Section 1: System preparation In this section, the necessary packages are imported. //localhost:4321/key-objective-2/agatashakira-the-aop-project-part-7/index.html Mon, 01 Jan 0001 00:00:00 +0000 //localhost:4321/key-objective-2/agatashakira-the-aop-project-part-7/index.html The AOP project ► Key objective 2 Author: Shakira Agata This Jupyter Notebook shows the steps for the execution of KE enrichment analysis and benchmarking to Overrepresentation Analysis(ORA) for dataset:E-MEXP-3583. This notebook is subdivided into nine sections: Section 1: Creation of dictKE dictionary Section 2: Creation of dictWP dictionary Section 3: Creation of KEgenes dictionary Section 4: Calculation of N variable Section 5: Comparison 1: Ag+ 24H Section 5.1: Calculation of n variable Section 5.2:Calculation of variable B and variable b Section 5.3: Calculation of enrichment score and hypergeometric p-value Section 5.4: Filtering results Section 5.5: Calculation of percent gene overlap Section 5.5.1 Creation of significant KE table Section 5.5.2 Significant ORA pathway table Section 5.5.3 Creation of for loop Section 5.5.4 Tabulation Section 5.5.5 Percent overlap calculation Section 6: Comparison 2: Ag+ 48H Section 6.1: Calculation of n variable Section 6.2:Calculation of variable B and variable b Section 6.3: Calculation of enrichment score and hypergeometric p-value Section 6.4: Filtering results Section 6.5: Calculation of percent gene overlap Section 6.5.1 Creation of significant KE table Section 6.5.2 Significant ORA pathway table Section 6.5.3 Creation of for loop Section 6.5.4 Tabulation Section 6.5.5 Percent overlap calculation Section 7: Comparison 3: AgNP 24H Section 7.1: Calculation of n variable Section 7.2:Calculation of variable B and variable b Section 7.3: Calculation of enrichment score and hypergeometric p-value Section 7.4: Filtering results Section 7.5: Calculation of percent gene overlap Section 7.5.1 Creation of significant KE table Section 7.5.2 Significant ORA pathway table Section 7.5.3 Creation of for loop Section 7.5.4 Tabulation Section 7.5.5 Percent overlap calculation Section 8: Comparison 4: AgNP 48H Section 8.1: Calculation of n variable Section 8.2:Calculation of variable B and variable b Section 8.3: Calculation of enrichment score and hypergeometric p-value Section 8.4: Filtering results Section 8.5: Calculation of percent gene overlap Section 8.5.1 Creation of significant KE table Section 8.5.2 Significant ORA pathway table Section 8.5.3 Creation of for loop Section 8.5.4 Tabulation Section 8.5.5 Percent overlap calculation Section 9: Metadata Section 1: Creation of dictKE dictionary In this section, the dictKE dictionary will be made which is used to retrieve the first neighbors of the key events present in the inflammatory stress response pathway AOP network. //localhost:4321/key-objective-2/agatashakira-the-aop-project-part-8/index.html Mon, 01 Jan 0001 00:00:00 +0000 //localhost:4321/key-objective-2/agatashakira-the-aop-project-part-8/index.html The AOP project ► Key objective 2 Author: Shakira Agata This Jupyter Notebook shows the steps for the execution of KE enrichment analysis and benchmarking to Overrepresentation Analysis(ORA) for dataset:E-MEXP-2599. This notebook is subdivided into eleven sections: Section 1: Creation of dictKE dictionary Section 2: Creation of dictWP dictionary Section 3: Creation of KEgenes dictionary Section 4: Calculation of N variable Section 5: Comparison 1: diquat dibromide timepoint 1 Section 5.1: Calculation of n variable Section 5.2: Calculation of variable B and variable b Section 5.3: Calculation of enrichment score & hypergeometric p-value Section 5.4: Filtering results Section 5.5: Calculation of percent gene overlap Section 5.5.1 Creation of significant KE table Section 5.5.2 Significant ORA pathway table Section 5.5.3 Creation of for loop Section 5.5.4 Tabulation Section 5.5.5 Percent overlap calculation Section 6: Comparison 2: diquat dibromide timepoint 2 Section 6.1: Calculation of n variable Section 6.2:Calculation of variable B and variable b Section 6.3: Calculation of enrichment score & hypergeometric p-value Section 6.4: Filtering results Section 6.5: Calculation of percent gene overlap Section 6.5.1 Creation of significant KE table Section 6.5.2 Significant ORA pathway table Section 6.5.3 Creation of for loop Section 6.5.4 Tabulation Section 6.5.5 Percent overlap calculation Section 7: Comparison 3: cadmium chloride timepoint 1 Section 7.1: Calculation of n variable Section 7.2:Calculation of variable B and variable b Section 7.3: Calculation of enrichment score & hypergeometric p-value Section 7.4: Filtering results Section 7.5: Calculation of percent gene overlap Section 7.5.1 Creation of significant KE table Section 7.5.2 Significant ORA pathway table Section 7.5.3 Creation of for loop Section 7.5.4 Tabulation Section 7.5.5 Percent overlap calculation Section 8: Comparison 4: cadmium chloride timepoint 2 Section 8.1: Calculation of n variable Section 8.2:Calculation of variable B and variable b Section 8.3: Calculation of enrichment score & hypergeometric p-value Section 8.4: Filtering results Section 8.5: Calculation of percent gene overlap Section 8.5.1 Creation of significant KE table Section 8.5.2 Significant ORA pathway table Section 8.5.3 Creation of for loop Section 8.5.4 Tabulation Section 8.5.5 Percent overlap calculation Section 9: Comparison 5: cyclosporine A timepoint 1 Section 9.1: Calculation of n variable Section 9.2:Calculation of variable B and variable b Section 9.3: Calculation of enrichment score & hypergeometric p-value Section 9.4: Filtering results Section 9.5: Calculation of percent gene overlap Section 9.5.1 Creation of significant KE table Section 9.5.2 Significant ORA pathway table Section 9.5.3 Creation of for loop Section 9.5.4 Tabulation Section 9.5.5 Percent overlap calculation Section 10: Comparison 6: cyclosporine A timepoint 2 Section 10.1: Calculation of n variable Section 10.2:Calculation of variable B and variable b Section 10.3: Calculation of enrichment score & hypergeometric p-value Section 10.4: Filtering results Section 10.5: Calculation of percent gene overlap Section 10.5.1 Creation of significant KE table Section 10.5.2 Significant ORA pathway table Section 10.5.3 Creation of for loop Section 10.5.4 Tabulation Section 10.5.5 Percent overlap calculation Section 11: Metadata Section 1: Creation of dictKE dictionary In this section, the dictKE dictionary will be made which is used to retrieve the first neighbors of the key events present in the inflammatory stress response pathway AOP network. //localhost:4321/key-objective-2/agatashakira-the-aop-project-part-9/index.html Mon, 01 Jan 0001 00:00:00 +0000 //localhost:4321/key-objective-2/agatashakira-the-aop-project-part-9/index.html The AOP project ► Key objective 2 Author: Shakira Agata This Jupyter Notebook shows the steps for the execution of KE enrichment analysis and benchmarking to Overrepresentation Analysis(ORA) for dataset:E-GEOD-69851. This notebook is subdivided into fourteen sections: Section 1: Creation of dictKE dictionary Section 2: Creation of dictWP dictionary Section 3: Creation of KEgenes dictionary Section 4: Calculation of N variable Section 5: Comparison 1: Bisphenol A 100uM Section 5.1: Calculation of n variable Section 5.2: Calculation of variable B and variable b Section 5.3: Calculation of enrichment score & hypergeometric p-value Section 5.4: Filtering results Section 5.5: Calculation of percent gene overlap Section 5.5.1 Creation of significant KE table Section 5.5.2 Significant ORA pathway table Section 5.5.3 Creation of for loop Section 5.5.4 Tabulation Section 5.5.5 Percent overlap calculation Section 6: Comparison 2: Bisphenol A 1uM Section 6.1: Calculation of n variable Section 6.2:Calculation of variable B and variable b Section 6.3: Calculation of enrichment score & hypergeometric p-value Section 6.4: Filtering results Section 6.5: Calculation of percent gene overlap Section 6.5.1 Creation of significant KE table Section 6.5.2 Significant ORA pathway table Section 6.5.3 Creation of for loop Section 6.5.4 Tabulation Section 6.5.5 Percent overlap calculation Section 7: Comparison 3: Farnesol 100uM Section 7.1: Calculation of n variable Section 7.2:Calculation of variable B and variable b Section 7.3: Calculation of enrichment score & hypergeometric p-value Section 7.4: Filtering results Section 7.5: Calculation of percent gene overlap Section 7.5.1 Creation of significant KE table Section 7.5.2 Significant ORA pathway table Section 7.5.3 Creation of for loop Section 7.5.4 Tabulation Section 7.5.5 Percent overlap calculation Section 8: Comparison 4: Tetrachlorodibenzo dioxin 100nM Section 8.1: Calculation of n variable Section 8.2:Calculation of variable B and variable b Section 8.3: Calculation of enrichment score & hypergeometric p-value Section 8.4: Filtering results Section 8.5: Calculation of percent gene overlap Section 8.5.1 Creation of significant KE table Section 8.5.2 Significant ORA pathway table Section 8.5.3 Creation of for loop Section 8.5.4 Tabulation Section 8.5.5 Percent overlap calculation Section 9: Comparison 5: Tetrachlorodibenzo dioxin 1nM Section 9.1: Calculation of n variable Section 9.2:Calculation of variable B and variable b Section 9.3: Calculation of enrichment score & hypergeometric p-value Section 9.4: Filtering results Section 9.5: Calculation of percent gene overlap Section 9.5.1 Creation of significant KE table Section 9.5.2 Significant ORA pathway table Section 9.5.3 Creation of for loop Section 9.5.4 Tabulation Section 9.5.5 Percent overlap calculation Section 10: Comparison 6: Troglitazone 100uM Section 10.1: Calculation of n variable Section 10.2:Calculation of variable B and variable b Section 10.3: Calculation of enrichment score & hypergeometric p-value Section 10.4: Filtering results Section 10.5: Calculation of percent gene overlap Section 10.5.1 Creation of significant KE table Section 10.5.2 Significant ORA pathway table Section 10.5.3 Creation of for loop Section 10.5.4 Tabulation Section 10.5.5 Percent overlap calculation Section 11: Comparison 7: Troglitazone 10uM Section 11.1: Calculation of n variable Section 11.2: Calculation of variable B and variable b Section 11.3: Calculation of enrichment score & hypergeometric p-value Section 11.4: Filtering results Section 11.5: Calculation of percent gene overlap Section 11.5.1 Creation of significant KE table Section 11.5.2 Significant ORA pathway table Section 11.5.3 Creation of for loop Section 11.5.4 Tabulation Section 11.5.5 Percent overlap calculation Section 12: Comparison 8: Troglitazone 1uM Section 12.1: Calculation of n variable Section 12.2: Calculation of variable B and variable b Section 12.3: Calculation of enrichment score & hypergeometric p-value Section 12.4: Filtering results Section 12.5: Calculation of percent gene overlap Section 12.5.1 Creation of significant KE table Section 12.5.2 Significant ORA pathway table Section 12.5.3 Creation of for loop Section 12.5.4 Tabulation Section 12.5.5 Percent overlap calculation Section 13: Comparison 9: Valproic acid 1mM Section 13.1: Calculation of n variable Section 13.2:Calculation of variable B and variable b Section 13.3: Calculation of enrichment score & hypergeometric p-value Section 13.4: Filtering results Section 13.5: Calculation of percent gene overlap Section 13.5.1 Creation of significant KE table Section 13.5.2 Significant ORA pathway table Section 13.5.3 Creation of for loop Section 13.5.4 Tabulation Section 13.5.5 Percent overlap calculation Section 14: Metadata Section 1: Creation of dictKE dictionary In this section, the dictKE dictionary will be made which is used to retrieve the first neighbors of the key events present in the inflammatory stress response pathway AOP network. //localhost:4321/key-objective-2/agatashakira-the-aop-project-part-10/index.html Mon, 01 Jan 0001 00:00:00 +0000 //localhost:4321/key-objective-2/agatashakira-the-aop-project-part-10/index.html The AOP project ► Key objective 2 Author: Shakira Agata This Jupyter Notebook shows the steps for the execution of KE enrichment analysis and benchmarking to Overrepresentation Analysis(ORA) for dataset: GSE44729 This notebook is subdivided into nine sections: Section 1: Creation of dictKE dictionary Section 2: Creation of dictWP dictionary Section 3: Creation of KEgenes dictionary Section 4: Calculation of N variable Section 5: Comparison 1: Acrolein timepoint 2 Section 5.1: Calculation of n variable Section 5.2:Calculation of variable B and variable b Section 5.3: Calculation of enrichment score & hypergeometric p-value Section 5.4: Filtering results Section 5.5: Calculation of percent gene overlap Section 5.5.1 Creation of significant KE table Section 5.5.2 Significant ORA pathway table Section 5.5.3 Creation of for loop Section 5.5.4 Tabulation Section 5.5.5 Percent overlap calculation Section 6: Comparison 2: Chloropicrin timepoint 1 Section 6.1: Calculation of n variable Section 6.2:Calculation of variable B and variable b Section 6.3: Calculation of enrichment score & hypergeometric p-value Section 6.4: Filtering results Section 6.5: Calculation of percent gene overlap Section 6.5.1 Creation of significant KE table Section 6.5.2 Significant ORA pathway table Section 6.5.3 Creation of for loop Section 6.5.4 Tabulation Section 6.5.5 Percent overlap calculation Section 7: Comparison 3: Chloropicrin timepoint 2 Section 7.1: Calculation of n variable Section 7.2:Calculation of variable B and variable b Section 7.3: Calculation of enrichment score & hypergeometric p-value Section 7.4: Filtering results Section 7.5: Calculation of percent gene overlap Section 7.5.1 Creation of significant KE table Section 7.5.2 Significant ORA pathway table Section 7.5.3 Creation of for loop Section 7.5.4 Tabulation Section 7.5.5 Percent overlap calculation Section 8: Comparison 4: Maleic anhydride timepoint 2 Section 8.1: Calculation of n variable Section 8.2:Calculation of variable B and variable b Section 8.3: Calculation of enrichment score & hypergeometric p-value Section 8.4: Filtering results Section 8.5: Calculation of percent gene overlap Section 8.5.1 Creation of significant KE table Section 8.5.2 Significant ORA pathway table Section 8.5.3 Creation of for loop Section 8.5.4 Tabulation Section 8.5.5 Percent overlap calculation Section 9: Metadata Section 1: Creation of dictKE dictionary In this section, the dictKE dictionary will be made which is used to retrieve the first neighbors of the key events present in the inflammatory stress response pathway AOP network. //localhost:4321/key-objective-2/agatashakira-the-aop-project-part-11/index.html Mon, 01 Jan 0001 00:00:00 +0000 //localhost:4321/key-objective-2/agatashakira-the-aop-project-part-11/index.html The AOP project ► Key objective 2 Author: Shakira Agata This Jupyter Notebook shows the steps for the execution of KE enrichment analysis and benchmarking to Overrepresentation Analysis(ORA) for dataset:GSE109565. This notebook is subdivided into eight sections: Section 1: Creation of dictKE dictionary Section 2: Creation of dictWP dictionary Section 3: Creation of KEgenes dictionary Section 4: Calculation of N variable Section 5: Comparison 1: PCB concentration 1 Section 5.1: Calculation of n variable Section 5.2:Calculation of variable B and variable b Section 5.3: Calculation of enrichment score and hypergeometric p-value Section 5.4: Filtering results Section 5.5: Calculation of percent gene overlap Section 5.5.1 Creation of significant KE table Section 5.5.2 Significant ORA pathway table Section 5.5.3 Creation of for loop Section 5.5.4 Tabulation Section 5.5.5 Percent overlap calculation Section 6: Comparison 2:PCB concentration 2 Section 6.1: Calculation of n variable Section 6.2:Calculation of variable B and variable b Section 6.3: Calculation of enrichment score and hypergeometric p-value Section 6.4: Filtering results Section 6.5: Calculation of percent gene overlap Section 6.5.1 Creation of significant KE table Section 6.5.2 Significant ORA pathway table Section 6.5.3 Creation of for loop Section 6.5.4 Tabulation Section 6.5.5 Percent overlap calculation Section 7: Comparison 3: PCB concentration 3 Section 7.1: Calculation of n variable Section 7.2: Calculation of variable B and variable b Section 7.3: Calculation of enrichment score and hypergeometric p-value Section 7.4: Filtering results Section 7.5: Calculation of percent gene overlap Section 7.5.1 Creation of significant KE table Section 7.5.2 Significant ORA pathway table Section 7.5.3 Creation of for loop Section 7.5.4 Tabulation Section 7.5.5 Percent overlap calculation Section 8: Metadata Section 1: Creation of dictKE dictionary In this section, the dictKE dictionary will be made which is used to retrieve the first neighbors of the key events present in the inflammatory stress response pathway AOP network.