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GattacaNet2

Predictive Genomics leveraging Multi-Task learning and LINA

Requirements

  • Python 3.6
    • Tensoflow v2.6.2
    • pandas_plink v2.2.9
    • pandas v1.4.2
    • numpy v1.19.5
    • scikit-learn v1.1.0

How to use it

This github contains the base code to run our experimentations. Each module represent a step of analysis.

  • Data_Preproccessing
    • phenotype_extraction_code.ipynb:Contain the code for the parralel phenotypes extraction. Produce 2 CSV file containing cancers and non-cancer diseases
  • Models
    • model_STL.py:Contain the code to train one the STL model. Require the phenotype index number as argument
    • Launch_trainings_STL.sh and train_stl.sbatch: Scripts to submit 1 job per phenotype to train STL models using SLURM.
    • model_pan_disease.ipynb: Contains the code to train the pan-disease model
    • model_pan_cancer.ipynb :Contains the code to train the pan-cancer model
    • model_pan_cancer_interpretation_decoy.ipynb: Contains the code to train the pan-cancer model with decoy SNPs and to interpret it.
  • Analyses:
    • important_snps_analysis.ipynb:Compute the important snps at different thresholds of FDR
    • important_snps_manhattan_venn_inter_corr_union.ipynb:compute the different metrics on the important SNPs and venn diagrams

For any inquiry please contact adrien.f.badre-1@ou.edu

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