TY - JOUR
T1 - Differential analysis of binarized single-cell RNA sequencing data captures biological variation
AU - Bouland, Gerard A.
AU - Mahfouz, Ahmed
AU - Reinders, Marcel J.T.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85127385714&partnerID=8YFLogxK
U2 - 10.1093/nargab/lqab118
DO - 10.1093/nargab/lqab118
M3 - Article
AN - SCOPUS:85127385714
SN - 2631-9268
VL - 3
JO - NAR Genomics and Bioinformatics
JF - NAR Genomics and Bioinformatics
IS - 4
M1 - lqab118
ER -