Differential analysis of binarized single-cell RNA sequencing data captures biological variation

Journal Article (2021)
Author(s)

G.A. Bouland (Leiden University Medical Center, TU Delft - Pattern Recognition and Bioinformatics)

Ahmed Mahfouz (Leiden University Medical Center, TU Delft - Pattern Recognition and Bioinformatics)

Marcel J T Reinders (Leiden University Medical Center, TU Delft - Pattern Recognition and Bioinformatics)

Research Group
Pattern Recognition and Bioinformatics
Copyright
© 2021 G.A. Bouland, A.M.E.T.A. Mahfouz, M.J.T. Reinders
DOI related publication
https://doi.org/10.1093/nargab/lqab118
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 G.A. Bouland, A.M.E.T.A. Mahfouz, M.J.T. Reinders
Research Group
Pattern Recognition and Bioinformatics
Issue number
4
Volume number
3
Reuse Rights

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Abstract

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.