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Processing & Filtering

Much of this project was done on the cloud via Terra.bio (Firecloud v2) workflows or Google VMs. As such, if you need access to the Terra workspaces, please email [email protected].

User Warning: Many of the bash scripts used to train and evaluate the models use nohup commands - if your CPU is not able to tolerate the amount of jobs, consider modifying your local version to serially run the models.

Terra Workflow Links

Main pipeline (Mutect, Mutsig2CV, Strelka, & others)

CGA HG38A WES Pipeline, split into pair sets :

NCI_A: https://app.terra.bio/#workspaces/shipp-dfci/hg38_tumorOnly_test_workspace/job_history/b0649a72-3c73-412f-b895-3d7779d43b88

NCI_B: https://app.terra.bio/#workspaces/shipp-dfci/hg38_tumorOnly_test_workspace/job_history/35b939df-447e-469f-8c62-e6829b366ab4

NCI_C: https://app.terra.bio/#workspaces/shipp-dfci/hg38_tumorOnly_test_workspace/job_history/a9d0751e-4be1-4d42-9b32-ada9637874a4

NCI_D: https://app.terra.bio/#workspaces/shipp-dfci/hg38_tumorOnly_test_workspace/job_history/8e504071-f828-4360-be1f-2aed5d3fca6d

NCI_E: https://app.terra.bio/#workspaces/shipp-dfci/hg38_tumorOnly_test_workspace/job_history/ddf231d9-5c95-48a6-afcc-673ad5b48649

Standalone BLAT filter and beyond (call-cached jobs, in order)

https://app.terra.bio/#workspaces/shipp-dfci/DLBCL_Staudt_TumorOnly_2021_v2/job_history

Classifier Preprocessing Reproducibility Steps (optional, files already included)

  1. Remap the labels from the consensus nmf job above: src_python/remap_labels.py
  2. Compute q-values per gene: src_python/fisher_5x2_parallel.py
  3. Generate baseline probabilities: src_python/generate_baseline_probabilities.py
  4. Create gene footprint table: src_python/calculate_driver_footprint.py

Before training Classifier:

Create an environment

conda create --name Classifier

conda activate Classifier

Install Pre-requisites

conda install pytorch torchvision -c pytorch

conda install pandas

conda install matplotlib

conda install scikit-learn

Model Reproducibility steps

  1. Run model training bash scripts (warning: this will launch many jobs, do not launch at all once)

    • run_all_experiments_step1.sh

    • run_all_experiments_step2A.sh

    • run_all_experiments_step2B.sh

    • run_all_experiments_step2C.sh

    • run_all_experiments_step2T.sh

    • run_sens_spec_experiments.sh

  2. Evaluate all trained models: src_python/evaluate_validation_ensembles.py

  3. Combine training history: src_python/combine_model_training_history.py

Plotting Results

Most plots are generated via R

  1. Step 1: src_R/plot_step1.R
  2. Step 2A: src_R/plot_step2A.R
  3. Step 2B: src_R/plot_step2B.R
  4. Step 2C: src_R/plot_step2C.R
  5. Step 2T: src_R/plot_step2T.R
  6. Sens/Spec experiments: src_R/plot_sensitivity_specificity_experiment.R
  7. Model training history: src_R/plot_training_history.R