Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes.
Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes.
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DOI：
10.1038/s41598-017-13665-w
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发表时间：
2017-10-19
影响因子：
4.6
通讯作者：
Zhou X
中科院分区：
文献类型：
--
作者：
Chen M;Zhou X
单细胞RNA测序（scRNAseq）技术在异质细胞群的无偏和高分辨率转录组分析中越来越受欢迎。尽管scRNAseq具有许多优点，但与任何其他基因组测序技术一样，它容易受到混杂效应的影响。控制scRNAseq数据中的混杂效应是准确下游分析的关键一步。在这里，我们提出了一种新的统计方法，我们称之为scPLS（单细胞偏最小二乘），用于对混淆效应进行稳健和准确的推断。scPLS利用了这样一个事实，即scRNAseq研究中的基因通常可以自然地分为两组：一组不受预测变量影响的控制基因和一组主要感兴趣的目标基因。利用偏最小二乘回归对两组基因进行联合建模，可以充分利用数据，提高对混杂效应的推断能力。通过大量的仿真和与其他方法的比较，我们证明了scPLS的有效性。最后，我们应用scPLS分析了两个scRNAseq数据集，以说明其在消除技术混淆效应以及消除细胞周期效应方面的好处。
Single cell RNA sequencing (scRNAseq) technique is becoming increasingly popular for unbiased and high-resolutional transcriptome analysis of heterogeneous cell populations. Despite its many advantages, scRNAseq, like any other genomic sequencing technique, is susceptible to the influence of confounding effects. Controlling for confounding effects in scRNAseq data is a crucial step for accurate downstream analysis. Here, we present a novel statistical method, which we refer to as scPLS (single cell partial least squares), for robust and accurate inference of confounding effects. scPLS takes advantage of the fact that genes in a scRNAseq study often can be naturally classified into two sets: a control set of genes that are free of effects of the predictor variables and a target set of genes that are of primary interest. By modeling the two sets of genes jointly using the partial least squares regression, scPLS is capable of making full use of the data to improve the inference of confounding effects. With extensive simulations and comparisons with other methods, we demonstrate the effectiveness of scPLS. Finally, we apply scPLS to analyze two scRNAseq data sets to illustrate its benefits in removing technical confounding effects as well as for removing cell cycle effects.
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影响因子：
64.8
作者：
通讯作者：
--
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：
64.5
作者：
Durruthy-Durruthy R;Gottlieb A;Hartman BH;Waldhaus J;Laske RD;Altman R;Heller S
通讯作者：
Heller S
影响因子：
7
作者：
Battle A;Mostafavi S;Zhu X;Potash JB;Weissman MM;McCormick C;Haudenschild CD;Beckman KB;Shi J;Mei R;Urban AE;Montgomery SB;Levinson DF;Koller D
通讯作者：
Koller D
影响因子：
5.8
作者：
Lee, Seonjoo;Chugh, Pauline E.;Dittmer, Dirk P.
通讯作者：
Dittmer, Dirk P.