单细胞 RNA 测序的异构体水平定量。

Isoform-level quantification for single-cell RNA sequencing.

机构信息

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden.

McArdle Laboratory for Cancer Research, Department of Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53705-227, USA.

出版信息

Bioinformatics. 2022 Feb 7;38(5):1287-1294. doi: 10.1093/bioinformatics/btab807.

DOI:10.1093/bioinformatics/btab807

PMID:34864849

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8826380/

Abstract

MOTIVATION

RNA expression at isoform level is biologically more informative than at gene level and can potentially reveal cellular subsets and corresponding biomarkers that are not visible at gene level. However, due to the strong 3' bias sequencing protocol, mRNA quantification for high-throughput single-cell RNA sequencing such as Chromium Single Cell 3' 10× Genomics is currently performed at the gene level.

RESULTS

We have developed an isoform-level quantification method for high-throughput single-cell RNA sequencing by exploiting the concepts of transcription clusters and isoform paralogs. The method, called Scasa, compares well in simulations against competing approaches including Alevin, Cellranger, Kallisto, Salmon, Terminus and STARsolo at both isoform- and gene-level expression. The reanalysis of a CITE-Seq dataset with isoform-based Scasa reveals a subgroup of CD14 monocytes missed by gene-based methods.

AVAILABILITY AND IMPLEMENTATION

Implementation of Scasa including source code, documentation, tutorials and test data supporting this study is available at Github: https://github.com/eudoraleer/scasa and Zenodo: https://doi.org/10.5281/zenodo.5712503.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

在异构体水平上的 RNA 表达比在基因水平上更具有生物学意义，并且有可能揭示在基因水平上不可见的细胞亚群和相应的生物标志物。然而，由于强烈的 3' 偏向测序方案，高通量单细胞 RNA 测序（如 Chromium Single Cell 3' 10× Genomics）的 mRNA 定量目前是在基因水平上进行的。

结果

我们通过利用转录簇和异构体同源物的概念，开发了一种用于高通量单细胞 RNA 测序的异构体水平定量方法。该方法称为 Scasa，在模拟中与竞争方法（包括 Alevin、Cellranger、Kallisto、Salmon、Terminus 和 STARsolo）相比，在异构体和基因水平的表达上都表现良好。使用基于异构体的 Scasa 对 CITE-Seq 数据集的重新分析揭示了基于基因的方法错过的 CD14 单核细胞亚群。