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 twood@broadinstitute.org.

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