Dawnn benchmarking dataset: Simulated discrete clusters processing and label simulation

<p>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 <a href="https://github.com/george-hall-ucl/dawnn" target="_blank">here</a>. Please contact us if you are unable to reproduce any of the analysis in our paper.</p> <p>The files in this collection correspond to the benchmarking dataset based on simulated discrete clusters.</p> <p><br></p> <p>FILES:</p> <p><u>Data processing code</u></p> <ul> <li><strong>adapted_discrete_clusters_sim_milo_paper.R</strong> Lightly adapted code from <a href="https://github.com/MarioniLab/milo_analysis_2020/blob/main/simulations/discrete_simulations.R" target="_blank">Dann <em>et al.</em></a> to simulate single-cell RNAseq datasets that form discrete clusters .</li> <li><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>.</li> </ul> <p><u>Resulting datasets</u></p> <ul> <li><strong>cells_sim_discerete_clusters_gex_seed_*.rds</strong> Seurat objects generated by <em>generate_test_data_discrete_clusters_sim_milo_paper.R</em>.</li> <li><strong>benchmark_dataset_sim_discrete_clusters.csv </strong>Cell labels generated by <em>generate_test_data_discrete_clusters_sim_milo_paper.R</em>.</li> </ul>