Alevin-fry unlocks rapid, accurate, and memory-frugal quantification of single-cell RNA-seq data

Abstract

The rapid growth of high-throughput single-cell and single-nucleus RNA-sequencing (sc/snRNA-seq) technologies has produced a wealth of data over the past few years. The size, volume, and distinctive characteristics of these data necessitate the development of new computational methods to accurately and efficiently quantify sc/snRNA-seq data into count matrices that constitute the input to downstream analyses. We introduce the alevin-fry framework for quantifying sc/snRNA-seq data. In addition to being faster and more memory frugal than other accurate quantification approaches, alevin-fry ameliorates the memory scalability and false-positive expression issues that are exhibited by other lightweight tools. We demonstrate how alevin-fry can be effectively used to quantify sc/snRNA-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.