Oncodrive3D: Fast and accurate detection of structural clusters of somatic mutations under positive selection

This repository includes notebooks to reproduce the analysis performed for the publication of Oncodrive3D.

Notebooks

0. genetables
   • Data preprocessing
1. method_explaination
   • Toy example to explain the method: F1
   • Rescaling the 3D clustering score and calculating p-values: S7
2. enrichment
   • CGC and Fish genes enrichment analysis: F2, S17
3. detected_genes_and_complementarity
   • Number of detected genes: F2, S17, S26
   • Complementarity analysis: F4, S20, S21, S26, S27
4. resources_analysis
   • Resources utilization analysis: F3, S19
5. landscape_and_distributions
   • Landscape of cancer driver genes: F5, S23, S24
   • Tables: T4, T6
   • Scores and features distributions: S22
   • Landscape of CH genes: S27
6. recurrence
   • Recurrence of clusters in cancer: F6
   • Recurrence of clusters in CH: S27
7. contact_probability_calculation
   • Survey on calculation of contact probability: S1
8. score_and_calibration
   • Correction of the 3D clustering score: S2, S3, S4, S5, S6
   • Distribution of p-values: S11, S12
   • QQ-plots: S18
9. simulations_and_ranking
   • Rank-based calculation of p-values: S8, S9, S10
10. alphafold_contribution
    • The contribution of AlphaFold models to Oncodrive3D discovery: S13
11. distance_within_clusters
    • Distance between clusters: S14
12. newly_identified_genes
    • Genes newly identified by Oncodrive3D with literature support: S15
13. data
    • Data visualization: S16
14. effect_of_hotspots
    • Effect of hotspots on detected clusters and genes