A Python package for benchmarking pathway databases with functional enrichment and prediction methods tasks.

If you find pathway_forte useful for your work, please consider citing:

pathway_forte can be installed from PyPI with the following command in your terminal:

$ python3 -m pip install pathway_forte

The latest code can be installed from GitHub with:

$ python3 -m pip install git+https://github.com/pathwayforte/pathway-forte.git

For developers, the code can be installed with:

$ git clone https://github.com/pathwayforte/pathway-forte.git
$ cd pathway-forte
$ python3 -m pip install -e .

The table below lists the main commands of PathwayForte.

• ora. Lists Over-Representation Analyses (e.g., one-tailed hyper-geometric test).
• fcs. Lists Functional Class Score Analyses such as GSEA and ssGSEA using GSEAPy.

pathway_forte enables three classification methods (i.e., binary classification, training SVMs for multi-classification tasks, or survival analysis) using individualized pathway activity scores. The scores can be calculated from any pathway with a variety of tools (see¹) using any pathway database that enables to export its gene sets.

• binary. Trains an elastic net model for a binary classification task (e.g., tumor vs. normal patients). The training is conducted using a nested cross validation approach (the number of cross validation in both loops can be selected). The model used can be easily changed since most of the models in scikit-learn (the machine learning library used by this package) required the same input.
• subtype. Trains a SVM model for a multi-class classification task (e.g., predict tumor subtypes). The training is conducted using a nested cross validation approach (the number of cross validation in both loops can be selected). Similarly as the previous classification task, other models can quickly be implemented.
• survival. Trains a Cox's proportional hazard's model with elastic net penalty. The training is conducted using a nested cross validation approach with a grid search in the inner loop. This analysis requires pathway activity scores, patient classes and lifetime patient information.

• export. Export GMT files with current gene sets for the pathway databases included in ComPath².
• datasets. Lists the TCGA data sets³ that are ready to run in pathway_forte.

The Pathway Forte logo is derived from "Muscle Fat" by Lorc, used under CC BY 3.0.

PathForte is a scientific software that has been developed in an academic capacity, and thus comes with no warranty or guarantee of maintenance, support, or back-up of data.