Dawnn model selection and hyperparmeter optimisation
This project is a collection of files to allow users to reproduce the model development and benchmarking in "Dawnn: single-cell differential abundance with neural networks" (Hall and Castellano, under review). Dawnn is a tool for detecting differential abundance in single-cell RNAseq datasets. It is available as an R package here. Please contact us if you are unable to reproduce any of the analysis in our paper.
The files in this collection contain to the code to reproduce the model selection and hyperparameter optimisation along with the results.
- nn_model_choice.py Python code to select architecture of neural networks and training hyperparameters (learning rate and whether dropout is employed). Uses Keras and Tensorflow. Outputs are stored in model_evaluations_structure_all_nn_results.txt.
- model_evaluations_structure_all_nn_results.txt Results from neural network model selection and hyperparameter optimisation in nn_model_choice.py.
- eval_rf_svm.py Python code to evaluate the performance of support vector machines and random forests for the same task as neural networks. Results are stored in svm_model_evaluations.txt and rf_model_evaluations.txt, respectively.
- svm_model_evaluations.txt Results from evaluation of support vector machines in eval_rf_svm.py.
- rf_model_evaluations.txt Results from evaluation of random forests in eval_rf_svm.py.