Dawnn benchmarking dataset: Simulated discrete clusters processing and label simulation

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 correspond to the benchmarking dataset based on simulated discrete clusters. <br> FILES: Data processing code <strong>adapted_discrete_clusters_sim_milo_paper.R</strong> Lightly adapted code from Dann <em>et al.</em> to simulate single-cell RNAseq datasets that form discrete clusters . <strong>generate_test_data_discrete_clusters_sim_milo_paper.R</strong> R code to assign simulated labels to datatsets generated from <em>adapted_discrete_clusters_sim_milo_paper.R</em>. Seurat objects saved as <em>cells_sim_discerete_clusters_gex_seed_*.rds</em>. Simulated labels saved as <em>benchmark_dataset_sim_discrete_clusters.csv</em>. Resulting datasets <strong>cells_sim_discerete_clusters_gex_seed_*.rds</strong> Seurat objects generated by <em>generate_test_data_discrete_clusters_sim_milo_paper.R</em>. <strong>benchmark_dataset_sim_discrete_clusters.csv </strong>Cell labels generated by <em>generate_test_data_discrete_clusters_sim_milo_paper.R</em>.