This repo includes the code for the analyses presented in the paper
A. Busia and J. Listgarten. MBE: Model-based enrichment estimation and prediction for differential sequencing data. Genome Biology, 2023.
which quantify high-throughput selection experiments using model-based enrichment (MBE)---a density ratio estimation (DRE) approach for estimating and/or predicting log-enrichment from sequencing data.
Key components include: MBE is implemented using linear, fully-connected neural network, and convolutional neural network model architectures defined in modeling.py, which are trained and evaluted using run_models.py and evaluate_models.py. See the MBE package for a more general implementation. Additional analyses of negative selection simulations are implemented in negative_selection.py and the scripts in plotting generate the visuals presented in the paper's main text and supplementary information. Simulated libraries and sequencing datasets were generated using the simulate_(...).py scripts, simlord_from_counts.py, and add_random_noise.py.