Print Email Facebook Twitter Differential analysis of binarized single-cell RNA sequencing data captures biological variation Title Differential analysis of binarized single-cell RNA sequencing data captures biological variation Author Bouland, G.A. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Mahfouz, A.M.E.T.A. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Date 2021 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. To reference this document use: http://resolver.tudelft.nl/uuid:9e1f2187-d749-4934-bcf5-792ade2aad62 DOI https://doi.org/10.1093/nargab/lqab118 Source NAR Genomics and Bioinformatics, 3 (4) Part of collection Institutional Repository Document type journal article Rights © 2021 G.A. Bouland, A.M.E.T.A. Mahfouz, M.J.T. Reinders Files PDF lqab118.pdf 989.18 KB Close viewer /islandora/object/uuid:9e1f2187-d749-4934-bcf5-792ade2aad62/datastream/OBJ/view