BoostDM-CH manuscript analyses

Content

This repo contains the source code to reproduce the analysis and figures of the paper:

Identification of Clonal Hematopoiesis Driver Mutations through In Silico Saturation Mutagenesis Santiago Demajo, Joan Enric Ramis-Zaldivar, Ferran Muinos, Miguel L Grau, Maria Andrianova, Nuria Lopez-Bigas, Abel Gonzalez-Perez; DOI: https://doi.org/10.1158/2159-8290.CD-23-1416

Repository organisation:

- BoostDM_ch_analyses
	| 1-Observed_mutations 
	| 2-Blueprints  
	| 3-Discovery_index
	| 4-Benchmark
	|   | - Other_cohort
   	| 5-Experimental_assays
	|   | - DNMT3A
	|   | - TP53
    	| 6-BoostDM_cancer_comparison
	|   | - scripts
    	| 7-Expert_curated_rules_comparison
    	| 8-UKB_analyses
	|   | 0_Clinical_phenotypes
	|   | 1_Post_processing_calling
	|   |   | - polN
	|   | 2_Mutation_overview
	|   | 3_Clinical_associations
	|   | 4_BoostDM_scores_tiers
	|   | 5_Rules_comparison
	|   | 6-Fitness_analysis
	|   | 7-New_exclusive_boostDM
-UKB_variant_calling
	| - crams
	| - part1
	| - part2
	| - refs
	| - U2AF1
- Paper_data
	| - BoostDM-cancer
	| - BoostDM-CH
	|   | - discovery	
	|   | - evaluation/CH
    |   | - model_selection
	|   | - prediction
	|   | - splitcv_meta/CH
	|   | - training_meta/CH
	|   | - Results_crossvalidation_50_iterations
	| - Experimental_data
	|   | - DNMT3A
	|   | - TP53
	| - Expert_curated_rules
	| - Intogen-Cancer
	| - Intogen-CH
	| - pfam

Folders descriptions and allocation of jupyter-notebooks and figures:

• BoostDM_ch_analyses, Contains all code to reproduce all analyses and figures
  • 1-Observed_mutations, Observed mutations from the three training cohorts
    • Observed_mutations.ipynb
      • Figure 1B
      • Supplementary Figure S3
      • Supplementary Figure S8B
  • 2-Blueprints,
    • Blueprint_models.ipynb
      • Figure 2A-B
      • Figure 2C
      • Figure 1C
  • 3-Discovery_index,
    • Discovery_index.ipynb
      • Supplementary Figure S1
  • 4-Benchmark, Cross-validations...
    • Cross-validation.ipynb
      • Figure 1C
      • Supplemental Figure S1G
    • Fscore50_plots.ipynb
      • Figure 1D
      • Figure 1B
      • Supplemental Figure S2A
    • Precision-recall.ipynb
      • Figure 1E
      • Supplemental Figure S2B
    • Other_cohort, Benchmarking with other cohorts
      • GaoExclusive_benchmark.ipynb
        • Supplemental Figure S3C
      • JapaneseBiobank_benchmark.ipynb
        • Supplemental Figure S3C
    • Results_crossvalidation_50_iterations,
  • 5-Experimental_assays, Benchmarking with experimental assays
    • DNMT3A,
      • DNMT3A_training_cohort.ipynb
        • Supplemental Figure S3A (left)
        • Figure 1F
      • DNMT3A_JapaneseBiobank.ipynb - Supplemental Figure S3A (right)
    • TP53,
      • TP53_Giacomelli_JapaneseBiobank.ipynb
        • Supplemental Figure S3B
      • TP53_Kato_Giacomelli_Training_cohort.ipynb
        • Supplemental Figure S3B
      • TP53_Kato_JapaneseBiobank.ipynb
        • Supplemental Figure S3B
      • TP53_Kotler_JapaneseBiobank.ipynb
        • Supplemental Figure S3B
      • TP53_Kotler_Training_cohort.ipynb
        • Supplemental Figure S3B
      • TP53_Ursu_JapaneseBiobank.ipynb
        • Supplemental Figure S3B
      • TP53_Ursu_Training_cohort.ipynb
        • Supplemental Figure S3B
  • 6-BoostDM_cancer_comparison,
    • BoostDM-CH_cancer_comparison.ipynb
      • Figure 3
      • Supplemental Figure S7
    • scripts, Contain dependencies and functions needed for the plots in the folder
  • 7-Expert_curated_rules_comparison,
    • Expert_curated_rules.ipynb
      • Supplemental Figure S7
      • Supplemental Figure S20C
    • False_positive.ipynb, False positive mutations from Bick et. al.
  • 8-UKB_analyses,
    • 0_Clinical_phenotypes,
      • cancerdata_manually.ipynb
      • cancerdata_variables.ipynb
      • covariates.ipynb
      • cvddata_variables.ipynb
      • Infectiondata_variables.ipynb
    • 1_Post_processing_calling,
      • Postprocessing.ipynb
      • Postprocessing_U2AF1.ipynb
      • Generate_final_files.ipynb
      • FINAL_merge.ipynb
      • polN, Generate pool of normals and excluding list. - poN.ipynb - poN_U2AF1.ipynb
    • 2_Mutation_overview,
      • Mut_overview.ipynb
        • Figure 4B
        • Supplemental Figure S8A
      • MutSig.ipynb
        • Supplemental Figure S8B
    • 3_Clinical_associations,
      • Mut_overview.ipynb
        • Figure 4C-D
        • Figure 5
        • Supplemental Figure S12
        • Supplemental Figure S13A-E
        • Supplemental Figure S14
        • Supplemental Figure S15
        • Supplemental Figure S16
      • Clinical_associations_non_observed.ipynb
        • Supplemental Figure S10A-B left
      • Clinical_associations_one_observed.ipynb
        • Supplemental Figure S10A-B right
      • Ethnicity_associations.ipynb
        • Supplemental Figure S10C
      • Age_association_SNV.ipynb, Age associaton per SNV CH mutation
    • 4_BoostDM_scores_tiers,
      • BoostDM_score_tiers.ipynb
        • Supplemental Figure S9
        • Supplemental Figure S13F
    • 5_Rules_comparison,
      • BoostDM_Rules_comparisons.ipynb
        • Supplemental Figure S18
      • UKB_mut_BoostDM_Rules.ipynb
        • Supplemental Figure S17
        • Supplemental Figure S21A
    • 6-Fitness_analysis,
      • 1_variants_distribution.ipynb, Get limit of detection of the cohort per gene
      • 2_ML_fitness_R882H.ipynb, DNMT3A R882H hotspot fitness calculation
      • 3_All_mutations_fitness.ipynb
        • Supplemental Figure S11
    • 7-New_exclusive_boostDM,
      • BoostDM_Rules_comparisons_overview.ipynb
        • Supplemental Figure S19A-C
        • Supplemental Figure S20A
        • Figure 6A
      • Methods_analysis_exclusive.ipynb
        • Supplemental Figure S19D
        • Supplemental Figure S20E
        • Figure 6E
      • Methods_analysis_intersect_rules.ipynb
        • Supplemental Figure S20B, D, F
        • Figure 6B-D
      • BoostDM-CH_ALL_CHEK2_tests_VAF.ipynb
        • Supplemental Figure S21B
      • BoostDM-CH_ALL_CHEK2_tests.ipynb
        • Supplemental Figure S21c-D

Complementary content

You can access to boostDM-CH source code and documentation in the boostDM-CH pipeline repository.
You can explore and download the main outputs of boostDM-CH in the boostDM-CH website.