In order to study the effects of phenotypic semantic similarity profiles we need to first characterize the difference. See also #4
Implementation
Develop a python function as part of a Jupyter notebook that takes as input two pandas dataframes with semantic similarity profiles and analyses how they differ from each other. The metrics to describe this difference need to be developed as a first pass we want to understand what is the average change of all phenotypic similarity values across pairs of ontology classes.
For example, consider SSP1, with N:1,N:2,0.9 and SSP2 with N:1,N:2,0.45 the percentage change is -100% (google it!)
Testing
Use the original HPHP mapping table from exomiser and compare it with the scrambled results from #4.
Motivation
In order to study the effects of phenotypic semantic similarity profiles we need to first characterize the difference. See also #4
Implementation
Develop a python function as part of a Jupyter notebook that takes as input two pandas dataframes with semantic similarity profiles and analyses how they differ from each other. The metrics to describe this difference need to be developed as a first pass we want to understand what is the average change of all phenotypic similarity values across pairs of ontology classes. For example, consider SSP1, with N:1,N:2,0.9 and SSP2 with N:1,N:2,0.45 the percentage change is -100% (google it!)
Testing
Use the original HPHP mapping table from exomiser and compare it with the scrambled results from #4.