//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. Hugo en