Oncological TranscripTomE Recognition

v 1.0.0

This repository contains code to train and run OTTER v1.0.0, an ensemble of CNN for transcriptome-based classification of tumour subtypes. For details on how it works and its aims, please see the publication at the end of this file.

To run the pre-trained classifier on your expression samples for inference please visit our OTTER web app

Please make sure to read our licence before using the files contained in this repository.

How it works

There are two main files, otter_train.py allows you to train a single CNN model at a time, otter_predict.py allows you to run inference with an ensemble of trained models.

python otter_train.py --data to_train.h5 
	--labels input_labels.h5 
	--hparam hyperparameters.json 
	--epochs 50 
	--batchsize 64 
	--patience 3 
	--split .2 
	--lowvar .99
	--output output_folder 

python otter_predict.py --data to_predict.h5 
	--models models_folder
	--output output_folder 

-h, --help will summon a help message with details on the available options.

The output is a pandas dataframe with the prediction probabilities. If the target classes were produced with RACCOON and a final_tree.json file containing information on the hierarchy is available, the probabilities can be further adjustes with postprocess.py. This script also allows you to recenter the probabilities midpoint according to the Youden indices if these has been calculated and saved to a pickle calibration_weights.pkl.

python postprocess.py --pred predictions.h5
	--nodes final_tree.json
	--calibration calibration_weights.pkl
	--output output_folder 

Dependencies

The classifiers were originally built and trained with the following libraries. More recent versions can be used, but beware you will need to update the code accordingly.

- scikit-learn	== 0.22.1
- tensorflow	== 1.12.0
- keras		== 2.2.4
- hyperopt	== 0.1.1

Citation

When using these files or the associated web app, please cite

F. Comitani, J. O. Nash, S. Cohen-Gogo, A. Chang, T. T. Wen, A. Maheshwari, B. Goyal, E. S. L. Tio, K. Tabatabaei, R. Zhao, L. Brunga, J. E. G. Lawrence, P. Balogh, A. Flanagan, S. Teichmann, B. Ho, A. Huang, V. Ramaswamy, J. Hitzler, J. Wasserman, R. A. Gladdy, B. C. Dickson, U. Tabori, M. J. Cowley, S. Behjati, D. Malkin, A. Villani, M. S. Irwin and A. Shlien, "Multi-scale transcriptional clustering and heterogeneity analysis reveal diagnostic classes of childhood cancer" (under review).