Dawnn benchmarking dataset: Keratinocytes 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 single-cell RNAseq of keratinocytes. <br> FILES: Input data Dataset from "Short-Term Treatment with Rho-Associated Kinase Inhibitor Preserves Keratinocyte Stem Cell Characteristics In Vitro". Cells 12(3), 346 (2023). <strong>skin_data_end_pipeline_1458110522.rds</strong> RDS object file to load dataset. Data processing code <strong>simulate_skin_labels_Rscript.R</strong> R code to simulate labels for benchmarking. <strong>simulate_skin_labels_bash.sh</strong> Bash script to execute <em>simulate_skin_labels_Rscript.R</em>. Outputs stored in <em>benchmark_dataset_skin.csv</em>. Resulting datasets <strong>benchmark_dataset_skin.csv</strong> Cell labels generated by <em>simulate_skin_labels_bash.sh</em>.