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