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Dawnn benchmarking dataset: Heart cells processing and label simulation

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posted on 2023-05-04, 16:03 authored by George HallGeorge Hall, Sergi Castellano HerezaSergi Castellano Hereza

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 heart cells.


Input data

Dataset from: "Integrated multi-omic characterization of congenital heart disease". Nature 608 pp. 181-191 (2022).

  • heart_barcodes.tsv.gz Cell barcode list
  • heart_genes.tsv.gz Gene list
  • heart_expression_matrix.mtx.gz Cell-by-gene expression matrix

Data processing code

  • process_heart_cells.R Generates benchmarking dataset from input data. (Reads heart_barcodes.tsv.gz, heart_genes.tsv.gz, and heart_expression_matrix.mtx.gz; Runs the standard Seurat pipeline; Saves the resulting Seurat dataset as heart_tissue_cells.RDS and the resulting cell labels as benchmark_dataset_heart_data_type_labels.csv)

Resulting datasets

  • heart_tissue_cells.RDS Seurat dataset generated by process_heart_cells.R.
  • benchmark_dataset_heart_data_type_labels.csv Cell labels generated by process_heart_cells.R.


NIHR Great Ormond Street Hospital Biomedical Research Centre


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