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

Journal article (2021)

Authors

G.A. Bouland Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
Ahmed Mahfouz Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
A.M.E.T.A. Mahfouz Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
Marcel J.T. Reinders Leiden University Medical Center, Pattern Recognition and Bioinformatics - EEMCS
Marcel Reinders Leiden University Medical Center, Pattern Recognition and Bioinformatics - EEMCS
M.J.T. Reinders Leiden University Medical Center, Pattern Recognition and Bioinformatics - EEMCS
M. J.T. Reinders Leiden University Medical Center, Pattern Recognition and Bioinformatics - EEMCS
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
Copyright: © 2021 G.A. Bouland, A.M.E.T.A. Mahfouz, M.J.T. Reinders
DOI: https://doi.org/10.1093/nargab/lqab118
More Info
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Published Date

2021

Language

English

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

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.