小柯机器人

近日，美国马里兰大学Rob Patro及其小组开发出单细胞RNA-seq数据分析的新方法。相关论文于2022年3月11日发表在《自然—方法学》杂志上。

研究人员表示，高通量单细胞和单核RNA测序（scRNA-seq和snRNA-seq）技术的快速发展在过去几年中产生了大量的数据。这些数据的大小、数量和独特的特征使得人们有必要开发新的计算方法，从而准确和有效地将sc/snRNA-seq数据量化为计数矩阵，构成下游分析的输入。

研究人员介绍了用于量化sc/snRNA-seq数据的alevin-fry框架。除了比其他精确量化方法更快、更节省内存外，alvin-fry还改善了其他轻量级工具所表现出的内存可扩展性和假阳性表达问题。研究人员展示了alvin-fry如何有效地用于sc/snRNA-seq数据的量化，以及RNA速度分析所需的拼接和未拼接分子的量化如何从用于生成正常基因表达计数矩阵的相同预处理数据中无缝提取。

附：英文原文

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

Author: He, Dongze, Zakeri, Mohsen, Sarkar, Hirak, Soneson, Charlotte, Srivastava, Avi, Patro, Rob

Issue&Volume: 2022-03-11

Abstract: The rapid growth of high-throughput single-cell and single-nucleus RNA-sequencing (scRNA-seq and 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 normal gene expression count matrices. Alevin-fry accurately quantifies single-cell and single-nucleus RNA-seq data with high speed and memory efficiency.

DOI: 10.1038/s41592-022-01408-3

Source: https://www.nature.com/articles/s41592-022-01408-3

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex