Detection of high variability in gene expression from single-cell RNA-seq profiling.
Detection of high variability in gene expression from single-cell RNA-seq profiling.
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DOI：
10.1186/s12864-016-2897-6
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发表时间：
2016-08-22
期刊：
影响因子：
4.4
通讯作者：
Chen Y
中科院分区：
文献类型：
--
作者：
Chen HI;Jin Y;Huang Y;Chen Y
下一代测序技术的进步使得能够在单细胞水平上绘制基因表达图谱，能够使用单细胞RNA测序（scRNA-seq）跟踪细胞异质性和确定细胞亚群。与其中差异表达分析是不可或缺的组成部分的常规RNA-seq的目标不同，scRNA-seq的最重要目标是在细胞群体中鉴定高度可变的基因，以解释单细胞基因表达的离散性质和用于单细胞测序的测序文库制备方案的独特性。然而，缺乏针对不同scRNA-seq数据集的通用表达变异模型。因此，本研究的目的是建立一个基因表达变异模型（GEVM），利用变异系数（CV）和平均表达水平之间的关系，以解决单细胞数据的过度分散，及其相应的统计学意义，以量化的表达基因（VEGs）。我们已经建立了一个模拟框架，可以生成具有不同细胞数量、模型参数和变化水平的scRNA-seq数据。我们实现了我们的GEVM，并通过在不同条件下使用一组模拟scRNA-seq数据来证明鲁棒性。我们使用均方根误差（RMSE）评估回归稳健性，并通过改变偏离同质细胞群体的初始模型参数来评估参数估计过程。我们还将GEVM应用于真实的scRNA-seq数据，以测试不同情况下的性能。在本文中，我们提出了一个基因表达变异模型，可以用来确定显着的重复表达的基因。将该模型应用于模拟的单细胞数据，我们观察到在不同条件下具有最小均方根误差的稳健参数估计。我们还使用不同的单细胞方案在两个不同的scRNA-seq数据集上检查了模型，并确定了VEGF。获得VEGF使我们能够观察可能的亚群，提供细胞异质性的进一步证据。使用GEVM，我们可以很容易地在不同的scRNA-seq数据集中找到显著的重复表达基因。
The advancement of the next-generation sequencing technology enables mapping gene expression at the single-cell level, capable of tracking cell heterogeneity and determination of cell subpopulations using single-cell RNA sequencing (scRNA-seq). Unlike the objectives of conventional RNA-seq where differential expression analysis is the integral component, the most important goal of scRNA-seq is to identify highly variable genes across a population of cells, to account for the discrete nature of single-cell gene expression and uniqueness of sequencing library preparation protocol for single-cell sequencing. However, there is lack of generic expression variation model for different scRNA-seq data sets. Hence, the objective of this study is to develop a gene expression variation model (GEVM), utilizing the relationship between coefficient of variation (CV) and average expression level to address the over-dispersion of single-cell data, and its corresponding statistical significance to quantify the variably expressed genes (VEGs). We have built a simulation framework that generated scRNA-seq data with different number of cells, model parameters, and variation levels. We implemented our GEVM and demonstrated the robustness by using a set of simulated scRNA-seq data under different conditions. We evaluated the regression robustness using root-mean-square error (RMSE) and assessed the parameter estimation process by varying initial model parameters that deviated from homogeneous cell population. We also applied the GEVM on real scRNA-seq data to test the performance under distinct cases. In this paper, we proposed a gene expression variation model that can be used to determine significant variably expressed genes. Applying the model to the simulated single-cell data, we observed robust parameter estimation under different conditions with minimal root mean square errors. We also examined the model on two distinct scRNA-seq data sets using different single-cell protocols and determined the VEGs. Obtaining VEGs allowed us to observe possible subpopulations, providing further evidences of cell heterogeneity. With the GEVM, we can easily find out significant variably expressed genes in different scRNA-seq data sets.
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