Scripts and data for the paper: Differential analysis of binarized single-cell RNA sequencing data captures biological variation

doi:10.4121/618e2cdb-d961-45c0-ab72-09bd68827580.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/618e2cdb-d961-45c0-ab72-09bd68827580
Datacite citation style:
Bouland, Gerard; Reinders, Marcel; Mahfouz, Ahmed (2024): Scripts and data for the paper: Differential analysis of binarized single-cell RNA sequencing data captures biological variation. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/618e2cdb-d961-45c0-ab72-09bd68827580.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset

Scripts and data for the paper: Differential analysis of binarized single-cell RNA sequencing data captures biological variation


Single-cell RNA sequencing data is characterized by a large number of zero counts, yet there is growing evidence that these zeros reflect biological variation rather than technical artifacts. We propose to use binarized expression profiles to identify the effects of biological variation in single-cell RNA sequencing data. Using 16 publicly available and simulated datasets, we show that a binarized representation of single-cell expression data accurately represents biological variation and reveals the relative abundance of transcripts more robustly than counts.


history
  • 2024-10-15 first online, published, posted
publisher
4TU.ResearchData
format
R/.rds R/.R text/.txt
organizations
TU Delft, Faculty of Electrical Engineering, Mathematics, and Computer Science, The Delft Bioinformatics Lab
Leiden University Medical Center, Department of Human Genetics

DATA - not available

Data and scripts already publically available at Zenodo and Github (see references)

DATA