RT Journal Article
SR Electronic
T1 Alevin-fry unlocks rapid, accurate, and memory-frugal quantification of single-cell RNA-seq data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.06.29.450377
DO 10.1101/2021.06.29.450377
A1 Dongze He
A1 Mohsen Zakeri
A1 Hirak Sarkar
A1 Charlotte Soneson
A1 Avi Srivastava
A1 Rob Patro
YR 2021
UL http://biorxiv.org/content/early/2021/07/01/2021.06.29.450377.abstract
AB The rapid growth of high-throughput single-cell and single-nucleus RNA sequencing technologies has produced a wealth of data over the past few years. The available technologies continue to evolve and experiments continue to increase in both number and scale. The size, volume, and distinctive characteristics of these data necessitate the development of new software and associated computational methods to accurately and efficiently quantify single-cell and single-nucleus RNA-seq data into count matrices that constitute the input to downstream analyses.We introduce the alevin-fry framework for quantifying single-cell and single-nucleus RNA-seq data. Despite being faster and more memory frugal than other accurate and scalable quantification approaches, alevin-fry does not suffer from the false positive expression or memory scalability issues that are exhibited by other lightweight tools. We demonstrate how alevin-fry can be effectively used to quantify single-cell and single-nucleus RNA-seq data, and also how the spliced and unspliced molecule quantification required as input for RNA velocity analyses can be seamlessly extracted from the same preprocessed data used to generate regular gene expression count matrices.Competing Interest StatementRP is a co-founder of Ocean Genomics, inc.