A Python package for comparing the effect of pathway database choice in functional enrichment and classification methods
Project description
A Python package for benchmarking pathway databases with functional enrichment and prediction methods tasks.
Installation
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 .
Main Commands
The table below lists the main commands of PathwayForte.
Command |
Action |
---|---|
datasets |
Lists of Cancer Datasets |
export |
Export Gene Sets using ComPath |
ora |
List of ORA Analyses |
fcs |
List of FCS Analyses |
prediction |
List of Prediction Methods |
Functional Enrichment Methods
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.
Prediction Methods
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 (see [1]) 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.
Other
References
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