A general and flexible method for signal extraction from single-cell RNA-seq data.
A general and flexible method for signal extraction from single-cell RNA-seq data.
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从单细胞RNA-seq数据中提取信号的一般而灵活的方法。
DOI：
10.1038/s41467-017-02554-5
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发表时间：
2018-01-18
影响因子：
16.6
通讯作者：
Vert JP
中科院分区：
文献类型：
--
作者：
Risso D;Perraudeau F;Gribkova S;Dudoit S;Vert JP
单细胞RNA测序(scRNA-seq)是一种强大的高通量技术，使研究人员能够在单个细胞的分辨率下测量全基因组转录水平。由于单个细胞中的RNA含量很低，一些基因即使表达出来也可能无法检测到；这些基因通常被称为辍学。在这里，我们提出了一个通用和灵活的零膨胀负二项模型(ZINB-WAVE)，它导致了数据的低维表示，这些数据解释了零膨胀(辍学)、过度分散和数据的计数性质。我们用模拟数据和真实数据证明，该模型及其相关的估计过程能够给出比主成分分析(PCA)和零膨胀因子分析(ZIFA)更稳定和准确的数据低维表示，而不需要初步的归一化步骤。单细胞RNA测序(scRNA-seq)数据提供了细胞群体内转录异质性的信息。在这里，Risso等人开发了用于scRNA-seq数据的低维表示的ZINB波，其考虑了零膨胀、超色散和数据的计数性质。
Single-cell RNA-sequencing (scRNA-seq) is a powerful high-throughput technique that enables researchers to measure genome-wide transcription levels at the resolution of single cells. Because of the low amount of RNA present in a single cell, some genes may fail to be detected even though they are expressed; these genes are usually referred to as dropouts. Here, we present a general and flexible zero-inflated negative binomial model (ZINB-WaVE), which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. We demonstrate, with simulated and real data, that the model and its associated estimation procedure are able to give a more stable and accurate low-dimensional representation of the data than principal component analysis (PCA) and zero-inflated factor analysis (ZIFA), without the need for a preliminary normalization step. Single-cell RNA sequencing (scRNA-seq) data provides information on transcriptomic heterogeneity within cell populations. Here, Risso et al develop ZINB-WaVE for low-dimensional representations of scRNA-seq data that account for zero inflation, over-dispersion, and the count nature of the data.
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影响因子：
12.3
作者：
Bacher R;Kendziorski C
通讯作者：
Kendziorski C
影响因子：
2.1
作者：
Lun, Aaron T. L.;Marioni, John C.
通讯作者：
Marioni, John C.
DOI：
10.1073/pnas.1617317113
发表时间：
2016-12-20
影响因子：
11.1
作者：
Lin, Zhixiang;Yang, Can;Wong, Wing Hung
通讯作者：
Wong, Wing Hung
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
Kharchenko, Peter V.;Silberstein, Lev;Scadden, David T.
通讯作者：
Scadden, David T.