Dawnn benchmarking dataset: Organoid 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 bile duct organoids.

FILES:

Input datasets

Dataset from "Cholangiocyte organoids can repair bile ducts after transplantation in the human liver". Science 371(6531) pp. 839-846 (2021).

• E-MTAB-8495.aggregated_filtered_normalised_counts.mtx Single-cell RNAseq expresison matrix.
• E-MTAB-8495.aggregated_filtered_normalised_counts.mtx_cols Column names.
• E-MTAB-8495.aggregated_filtered_normalised_counts.mtx_rows Row names.

Data processing code

• process_organoid_cells_data.R Generates benchmarking dataset from input data. (Reads E-MTAB-8495.aggregated_filtered_normalised_counts.* files; Runs the standard Seurat pipeline; Saves the resulting Seurat dataset as organoid_cells.RDS)
• simulate_organoid_labels_Rscript.R R code to simulate labels for benchmarking.
• simulate_organoid_labels_bash.sh Bash script to execute simulate_organoid_labels_Rscript.R. Outputs stored in benchmark_dataset_organoid_labels.csv.

Resulting datasets

• organoid_cells.RDS Seurat dataset generated by process_organoid_cells_data.R.
• benchmark_dataset_organoid_labels.csv Cell labels generated by simulate_organoid_labels_bash.sh.