Chao-PTEN-functionalization-2020

Manuscript link (July 2020 cover!): https://cancerres.aacrjournals.org/content/80/13/2775

The following files accompany the PTEN functionalization manuscript
Requirements: Python 3 with Jupyter Notebook

1. Fig. 3

• 1_plot_domain_ClinVar.ipynb:
      generates the domain organization and ClinVar significance heatmaps
• 2_plot_MCF10A_LOF_scores (heatmap).ipynb:
      generates the heatmap for visualizing LOF scores

2. Fig. 4

• 3_plot_MCF10A_stability.ipynb:
      generates the graph in Fig. 4A
• 4_MCF10A_stability_LR.ipynb:
      computes logistic regression on the mean normalized stability scores
      generates the graph in Fig. 4B
• 5_comapre_stability_HEK_SDM.ipynb:
      compares stability scores from MCF10A cells, HEK cells and SDM predictions
      generates the graphs in Fig. 4C, 4D and 4E

3. Fig. 5

• 6_compare_yeast_vs_MCF10A.ipynb:
      compares LOF scores from yeast (Mighell et al 2018 PMID:29706350)
      generates the graphs in Fig. 5A, 5B and 5C

4. Fig. 6

• 7_LOF_scores_SVM.ipynb:
      builds SVM model for classifying LOF scores into 3 classes
      generates the data for Fig. 6A
• 8_plot_MCF10A_SVM_classification.ipynb:
      generates the plot in Fig. 6B from SVM classification
• 9_plot_ROC_BINARY.ipynb:
      generates the ROC curves for binary classification in Fig. 6C
• 10_plot_ROC_multiclass.ipynb:
      generates the ROC curves for multiclass classification in Fig. 6D
• 11_compare_LOF_vs_predictions.ipynb:
      compares LOF scores to CADD, SNAP2, PolyPhen-2 and SIFT
      generates the graphs in Fig. 6E