RT Journal Article
SR Electronic
T1 PsiNorm: a scalable normalization for single-cell RNA-seq data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.04.07.438822
DO 10.1101/2021.04.07.438822
A1 Matteo Borella
A1 Graziano Martello
A1 Davide Risso
A1 Chiara Romualdi
YR 2021
UL http://biorxiv.org/content/early/2021/04/09/2021.04.07.438822.abstract
AB Single-cell RNA sequencing (scRNA-seq) enables transcriptome-wide gene expression measurements at single-cell resolution providing a comprehensive view of the compositions and dynamics of tissue and organism development. The evolution of scRNA-seq protocols has led to a dramatic increase of cells throughput, exacerbating many of the computational and statistical issues that previously arose for bulk sequencing. In particular, with scRNA-seq data all the analyses steps, including normalization, have become computationally intensive, both in terms of memory usage and computational time. In this perspective, new accurate methods able to scale efficiently are desirable.Here we propose PsiNorm, a between-sample normalization method based on the power-law Pareto distribution parameter estimate. Here we show that the Pareto distribution well resembles scRNA-seq data, independently of sequencing depths and technology. Motivated by this result, we implement PsiNorm, a simple and highly scalable normalization method. We benchmark PsiNorm with other seven methods in terms of cluster identification, concordance and computational resources required. We demonstrate that PsiNorm is among the top performing methods showing a good trade-off between accuracy and scalability. Moreover PsiNorm does not need a reference, a characteristic that makes it useful in supervised classification settings, in which new out-of-sample data need to be normalized.PsiNorm is available as an R package available at https://github.com/MatteoBlla/PsiNormCompeting Interest StatementThe authors have declared no competing interest.