MicroRNA classification

This project can pull microRNA expression profiles from The Cancer Genome Atlas, store it in a JSON file, preprocess and build a classifier for tumour tissue from it. Different classifiers can be used. For each classifier the cross validated accuracy is calculated.

Inspired by the paper "MicroRNA based Pan-Cancer Diagnosis and Treatment Recommendation" by Cheerla and Gevaert and done (just for fun and profit) as part of the Hot-Topics in Bioinformatics Seminar at KIT.

Workflow

(See the --help pages of the commands for more information on how to use them.)

1. Pull the data from the TCGA into a database (calling its API)
   • you can use the database from here with the data of all female participants (1490 cases and 1612 samples)
   • or use python3 classifier.py update_db [case id 1] […]
   • or use python3 classifier.py update_db_from_dict [file]
   • → creates a database
2. Impute the missing normal tissue data → creates a new database
   • use python3 classifier.py impute
   • might fail for databases that are to large
   • and might not lead to better results
3. Reduce the feature space via a principal component analysis → reduced JSON
   • use python3 classifier.py learn pca
   • only the resulting file is usable for the next step
4. Create some classifiers and cross validate them
   • use python3 classifier.py learn classify [svc, …]

Example workflow: (assumes that the mentioned prepared database is used and stored in a file called women_db)

python3 classifier.py --db women_db impute women_db_imputed
python3 classifier.py --db women_db_imputed learn pca --output_file reduced_women_db
python3 classifier.py learn classify --file reduced_women_db mlp

Why?

Because it was a nice way to learn a bit machine learning and to better understand a paper.

Dependencies

• python3 (3.4 or later)
• scipy
• scikit-learn
• click
• keras and tensorflow (for keras_mpl classifier)
• hyperopt and hyperopt-sklearn for the parameter optimization

License

MIT