Dawnn benchmarking dataset: Simulated linear trajectories 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 linear trajectories.

FILES:

Data processing code

• adapted_traj_sim_milo_paper.R Lightly adapted code from Dann et al. to simulate single-cell RNAseq datasets that form linear trajectories .
• generate_test_data_linear_traj_sim_milo_paper.R R code to assign simulated labels to datatsets generated from adapted_traj_sim_milo_paper.R. Seurat objects saved as cells_sim_linear_traj_gex_seed_*.rds. Simulated labels saved as benchmark_dataset_sim_linear_traj.csv.

Resulting datasets

• cells_sim_linear_traj_gex_seed_*.rds Seurat objects generated by generate_test_data_linear_traj_sim_milo_paper.R.
• benchmark_dataset_sim_linear_traj.csv Cell labels generated by generate_test_data_linear_traj_sim_milo_paper.R.