Gene expression distribution deconvolution in single-cell RNA sequencing.
Gene expression distribution deconvolution in single-cell RNA sequencing.
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DOI：
10.1073/pnas.1721085115
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发表时间：
2018-07-10
影响因子：
11.1
通讯作者：
Zhang NR
中科院分区：
文献类型：
--
作者：
Wang J;Huang M;Torre E;Dueck H;Shaffer S;Murray J;Raj A;Li M;Zhang NR
我们开发了单细胞表达分布的去卷积(Dendend)，这是一种从单细胞RNA测序中观察到的计数恢复真实基因表达水平的跨细胞分布的方法，允许调整已知的混杂细胞水平因素。有了恢复的分布，Dendend提供了基于分布的测量的可靠估计，例如真实基因表达的分散度和真实基因表达为正的概率。这一点很重要，因为随着对这些测量的更好估计，Dendend澄清和改进了许多下游分析，包括寻找差异表达的基因、识别细胞类型和选择分化标记。另一个贡献是，我们使用9个公共数据集验证了基于唯一分子识别符的单细胞RNA测序数据的技术噪声的一个简单的“泊松-阿尔法”噪声模型，澄清了目前关于这一问题的激烈辩论。单细胞RNA测序(scRNA-seq)能够量化每个基因在细胞中的表达分布，从而能够评估其分布的离散度、非零比例和超出平均值的其他方面。这些基因表达分布的统计特征对于理解表达变异和选择群体异质性的标记基因至关重要。然而，scRNA-seq数据是有噪声的，每个细胞通常在低覆盖率下测序，因此很难从原始计数中推断基因表达分布的特性。基于对9个公共数据集的重新检查，我们提出了一个简单的具有唯一分子识别符(UMI)的scRNA-seq数据的技术噪声模型。我们开发了单细胞表达分布的去卷积(Dendend)，这是一种从观察到的scRNA-seq计数中去卷积真实的跨细胞基因表达分布的方法，导致对分布特性的改进估计，例如分散度和非零分数。Dendend可以针对细胞级别的协变量进行调整，例如细胞大小、细胞周期和批处理效果。通过与RNA FISH数据的比较，通过数据拆分和模拟，以及通过其在消除已知批次效应方面的有效性，进一步评估了Dendend的噪声模型和估计精度。我们演示了Dendend如何澄清和改进下游分析，例如寻找差异表达的基因、识别细胞类型和选择分化标记。
We developed deconvolution of single-cell expression distribution (DESCEND), a method to recover cross-cell distribution of the true gene expression level from observed counts in single-cell RNA sequencing, allowing adjustment of known confounding cell-level factors. With the recovered distribution, DESCEND provides reliable estimates of distribution-based measurements, such as the dispersion of true gene expression and the probability that true gene expression is positive. This is important, as with better estimates of these measurements, DESCEND clarifies and improves many downstream analyses including finding differentially expressed genes, identifying cell types, and selecting differentiation markers. Another contribution is that we verified using nine public datasets a simple “Poisson-alpha” noise model for the technical noise of unique molecular identifier-based single-cell RNA-sequencing data, clarifying the current intense debate on this issue. Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene’s expression distribution across cells, thus allowing the assessment of the dispersion, nonzero fraction, and other aspects of its distribution beyond the mean. These statistical characterizations of the gene expression distribution are critical for understanding expression variation and for selecting marker genes for population heterogeneity. However, scRNA-seq data are noisy, with each cell typically sequenced at low coverage, thus making it difficult to infer properties of the gene expression distribution from raw counts. Based on a reexamination of nine public datasets, we propose a simple technical noise model for scRNA-seq data with unique molecular identifiers (UMI). We develop deconvolution of single-cell expression distribution (DESCEND), a method that deconvolves the true cross-cell gene expression distribution from observed scRNA-seq counts, leading to improved estimates of properties of the distribution such as dispersion and nonzero fraction. DESCEND can adjust for cell-level covariates such as cell size, cell cycle, and batch effects. DESCEND’s noise model and estimation accuracy are further evaluated through comparisons to RNA FISH data, through data splitting and simulations and through its effectiveness in removing known batch effects. We demonstrate how DESCEND can clarify and improve downstream analyses such as finding differentially expressed genes, identifying cell types, and selecting differentiation markers.
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DOI：
10.1126/science.1247651
发表时间：
2014-02-14
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Jaitin DA;Kenigsberg E;Keren-Shaul H;Elefant N;Paul F;Zaretsky I;Mildner A;Cohen N;Jung S;Tanay A;Amit I
通讯作者：
Amit I
影响因子：
12.3
作者：
Kim JK;Marioni JC
通讯作者：
Marioni JC
影响因子：
48
作者：
Kiselev, Vladimir Yu;Kirschner, Kristina;Hemberg, Martin
通讯作者：
Hemberg, Martin
表征单细胞RNA-seq中的噪声结构可将真实性与技术随机等位基因表达区分开。
影响因子：
16.6
作者：
Kim JK;Kolodziejczyk AA;Ilicic T;Teichmann SA;Marioni JC
通讯作者：
Marioni JC
影响因子：
48
作者：
Kharchenko, Peter V.;Silberstein, Lev;Scadden, David T.
通讯作者：
Scadden, David T.