A Python package for comparing the effect of pathway database choice in functional enrichment and classification methods
A Python package for benchmarking pathway databases with functional enrichment and prediction methods tasks.
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.
|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.
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 ) 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.
|||Lim, S., et al. (2018). Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data. Briefings in bioinformatics, bby125.|
|||Domingo-Fernández, D., et al. (2018). ComPath: An ecosystem for exploring, analyzing, and curating mappings across pathway databases. npj Syst Biol Appl., 4(1):43.|
|||Weinstein, J. N., et al. (2013). The cancer genome atlas pan-cancer analysis project. Nature genetics, 45(10), 1113.|
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