Two Jupyter notebooks in this directory demonstrate the analyses that support Figure 3 and Supplementary Figure 2 in the PathCORE-T paper:
They must be run after ../ANALYSIS.sh. Additional Python dependencies
for the notebooks are specified in the ./requirements.txt file for this
directory.
An additional notebook is included as a supplemental example of the PathCORE-T analysis workflow:
In this notebook, we apply the PathCORE-T software to a FastICA model of the normalized P. aeruginosa gene compendium. We apply scikit-learn's FastICA implementation.
Similar to the case studies we describe in our manuscript, we set the number of features (ICA components) we construct to 300. A user interested in changing this parameter and examining the differences in the resulting network can do so in cell [4] of the notebook and re-run the analysis.