https://github.com/bhklab/analyze_readii_outputs
Code for analyzing outputs from the READII package or the readii-orcestra pipeline.
https://github.com/bhklab/analyze_readii_outputs
Last synced: 3 months ago
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Code for analyzing outputs from the READII package or the readii-orcestra pipeline.
- Host: GitHub
- URL: https://github.com/bhklab/analyze_readii_outputs
- Owner: bhklab
- License: mit
- Created: 2024-10-01T13:46:22.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-12-03T02:50:22.000Z (6 months ago)
- Last Synced: 2024-12-30T03:20:50.499Z (5 months ago)
- Language: Jupyter Notebook
- Size: 5.98 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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)
```raw
└── 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
```raw
└── 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
- [x] Supports MRMR training over k folds
- [ ] Doesn't support MRMR training over 1 fold
- [ ] Doesn't support regular training over k folds
- [ ] Doesn't support loading model weights across k folds