setton_hadi_choo_2023

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UID: 11270

Description: Description from GitHub:

"This repository contains code and data to generate the figures presented in Setton, Hadi, Choo et al. Nature 2023.

Most figures (aside from those which rely on random forest training) are found in ./notebooks/figures.ipynb. The remaining figures (Figure 5, Extended Data Figure 8, and Extended Data Figure 9) are found in ./notebooks/random_forest.ipynb.

We have also made available our OnenessTwoness random forest classifier, which predicts whether a tumor sample is HR-proficient, BRCA1-deficient, and BRCA2-deficient. This is saved in the file ./models/stash.retrained.model.rds. We provided the code for training the model and an example of the input data for making predictions in the notebook ./notebooks/oneness_twoness_training_and_examples.ipynb."

Subject(s)

Access via GitHub
Access Restrictions: Free to All
Access Instructions: Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the relevant conditions. Read more about access policy on GitHub.
Associated Publications: Setton J, Hadi K, Choo Z, Kuchin K, Tian H, Da Cruz Paula A, Rosiene J, Selenica P, Behr J, Yao X, Deshpande A, Sigouros M, Manohar J, Nauseef J, Mosquera J, Elemento O, Weigelt B, Riaz N, Reis-Filho J, Powell S, Imieliński M. Long-molecule scars of backup DNA repair in BRCA1- and BRCA2-deficient cancers. Nature. 2023; 621(7977):129-137.
Dataset Format(s): RDS, IPYNB

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