Single-cell mRNA quantification and differential analysis with Census.
Single-cell mRNA quantification and differential analysis with Census.
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DOI：
10.1038/nmeth.4150
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发表时间：
2017-03
期刊：
影响因子：
48
通讯作者：
Trapnell C
中科院分区：
文献类型：
--
作者：
Qiu X;Hill A;Packer J;Lin D;Ma YA;Trapnell C
单细胞基因表达研究有望通过对基因调控的惊人高分辨率观察，揭示罕见的细胞类型和发育和疾病中的神秘状态。然而，来自单细胞RNA-Seq的测量是高度可变的，这使得分析细胞之间表达差异的努力令人沮丧。我们引入了Census，这是一种可通过我们的单细胞分析工具包Monocle 2获得的算法，该算法将相对RNA-Seq表达水平转换为相对转录本计数，而无需实验性加标对照。我们表明，分析相对转录本计数的变化导致显着改善的准确性相比，归一化读取计数，并使新的统计测试，以确定发育调控基因。我们通过重新分析几种发育和疾病背景下的单细胞研究来探索人口普查的力量。普查计数可以用广泛使用的回归技术进行分析，以揭示细胞命运依赖性基因表达、剪接模式和等位基因失衡的变化，这表明普查能够在多个基因调控层进行稳健的单细胞分析。
Single-cell gene expression studies promise to unveil rare cell types and cryptic states in development and disease through a stunningly high-resolution view of gene regulation. However, measurements from single-cell RNA-Seq are highly variable, frustrating efforts to assay how expression differs between cells. We introduce Census, an algorithm available through our single-cell analysis toolkit Monocle 2, which converts relative RNA-Seq expression levels into relative transcript counts without the need for experimental spike-in controls. We show that analyzing changes in relative transcript counts leads to dramatic improvements in accuracy compared to normalized read counts and enables new statistical tests for identifying developmentally regulated genes. We explore the power of Census through reanalysis of single-cell studies in several developmental and disease contexts. Census counts can be analyzed with widely used regression techniques to reveal changes in cell fate-dependent gene expression, splicing patterns, and allelic imbalances, demonstrating that Census enables robust single-cell analysis at multiple layers of gene regulation.