PathwayForte |build| |docs| |coverage| |zenodo|
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:
… [1] Mubeen, S., et al (2019). The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling <https://doi.org/10.3389/fgene.2019.01203>_. Front. Genet., 10:1203.
Installation |pypi_version| |python_versions| |pypi_license|
pathway_forte can be installed from PyPI <https://pypi.org/project/pathway-forte>_
with the following command in your terminal:
… code-block:: sh
$ python3 -m pip install pathway_forte
The latest code can be installed from GitHub <https://github.com/pathwayforte/pathway-forte>_
with:
… code-block:: sh
$ python3 -m pip install git+https://github.com/pathwayforte/pathway-forte.git
For developers, the code can be installed with:
… code-block:: sh
$ 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 <https://github.com/ostrokach/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 [2]_) 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 <https://scikit-learn.org/>_ (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
- export. Export GMT files with current gene sets for the pathway databases included in ComPath [3]_.
- datasets. Lists the TCGA data sets [4]_ that are ready to run in
pathway_forte.
References
… [2] Lim, S., et al. (2018). Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data <https://doi.org/10.1093/bib/bby097>. Briefings in bioinformatics, bby125.
… [3] Domingo-Fernández, D., et al. (2018). ComPath: An ecosystem for exploring, analyzing, and curating mappings across pathway databases <https://doi.org/10.1038/s41540-018-0078-8>. npj Syst Biol Appl., 4(1):43.
… [4] Weinstein, J. N., et al. (2013). The cancer genome atlas pan-cancer analysis project <https://doi.org/10.1038/ng.2764>_. Nature genetics, 45(10), 1113.
License
The Pathway Forte logo is derived from "Muscle Fat" <https://game-icons.net/1x1/lorc/muscle-fat.html>_ by Lorc, used under CC BY 3.0.
Disclaimer
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.
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