analyze_readii_outputs

Code for analyzing outputs from the READII package or the readii-orcestra pipeline.

Workflow Contents

• shell script for setting up the directory structure
• Python Jupyter Notebook for pre-processing the clinical and image features data and setting up the pre-existing radiomic signatures
• R notebook for performing feature selection and CPH modeling

Data Organization

Raw data (currently what would be the deconstructed output from ORCESTRA object)

└──  rawdata
    └──  {DATASETNAME}
        ├──  clinical
        ├──  fmcib_outputs
        └──  readii_outputs
        {DATASETNAME}_READII-RADIOMICS_MAE.RDS

Processed data = filtered clinical, radiomic, and deep learning features, possibly split into training and test sets

└──  procdata
    └──  {DATASETNAME}
        └──  clinical
             ├──  cleaned_filtered_clinical_{DATASETNAME}.csv
             └──  [OPTIONAL] train_test_labelled_clinical_{DATASETNAME}.csv
        └──  radiomics
             ├──  clinical
                  └──  merged_clinical_{DATASETNAME}.csv
             ├──  features
                  ├──  merged_radiomicfeatures_{image_type}_{DATASETNAME}.csv
                  └──  labelled_radiomicfeatures_only_{image_type}_{DATASETNAME}.csv
             └──  [OPTIONAL] train_test_split
                  ├──  clinical
                       └──  train_merged_clinical_{DATASETNAME}.csv
                       └──  test_merged_clinical_{DATASETNAME}.csv
                  ├──  train_features
                       └──  train_labelled_radiomicfeatures_only_{image_type}_{DATASETNAME}.csv
                  └──  test_features
                       └──  test_labelled_radiomicfeatures_only_{image_type}_{DATASETNAME}.csv
        └──  deep_learning
             ├──  clinical
                  └──  merged_clinical_{DATASETNAME}.csv
             ├──  features
                  └──  merged_fmcibfeatures_{image_type}_{DATASETNAME}.csv
                  └──  labelled_fmcibfeatures_only_{image_type}_{DATASETNAME}.csv
             └──  [OPTIONAL] train_test_split
                  ├──  clinical
                       └──  train_merged_clinical_{DATASETNAME}.csv
                       └──  test_merged_clinical_{DATASETNAME}.csv
                  ├──  train_features
                       └──  train_labelled_fmcibfeatures_only_{image_type}_{DATASETNAME}.csv
                  └──  test_features
                       └──  test_labelled_fmcibfeatures_only_{image_type}_{DATASETNAME}.csv           

TODO:

• Implement logger
• Implement ORCESTRA download
• Implement MAE deconstructor
  • Unpack clinical data --> save to csv
  • Unpack radiomic features --> save each experiment to csv
  • Unpack deep learning features --> save each experiment to csv
  • Get list of experiments, specifically the negative controls
• Make this into a snakemake pipeline
• Implement config file creation if one is not present
• Move data_setup_for_modelling from scripts into notebooks
• Finish implementing survival time and event setup as functions