Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data
Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data
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DOI：
10.1186/s12859-019-2599-6
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发表时间：
2019-01-18
影响因子：
3
通讯作者：
Nabavi, Sheida
中科院分区：
文献类型：
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作者：
Wang, Tianyu;Li, Boyang;Nabavi, Sheida
研究背景单细胞RNA测序（scRNAseq）数据分析在生物学和生物医学研究中对于理解细胞的内在和外在过程起着重要作用。在这一领域的一个重要努力是差异表达（DE）基因的检测。然而，scRNAseq数据是高度异质性的，并且具有大量的零计数，这在检测DE基因中引入了挑战。应对这些挑战需要采用传统方法之外的新方法，这些方法基于平均表达的非零差异。已经开发了几种方法用于scRNAseq数据的差异基因表达分析。为了为选择合适的工具或开发新的工具提供指导，有必要评估和比较scRNAseq数据的差异基因表达分析方法的性能。结果在本研究中，我们对11种差异基因表达分析软件工具的性能进行了全面评估，这些工具是针对scRNAseq数据设计的或可以应用于它们。我们使用模拟和真实的数据来评估检测的准确度和精密度。使用模拟数据，我们研究了样本大小对工具检测精度的影响。使用真实的数据，我们研究了在确定DE基因，工具的运行时间，和检测到的DE基因的生物相关性的工具之间的协议。在真阳性率和调用DE基因的精确度之间存在权衡。具有较高真阳性率的方法由于其引入假阳性而倾向于显示低精确度，而具有高精确度的方法由于鉴定很少DE基因而显示低真阳性率。我们观察到，与为批量RNAseq数据设计的方法相比，为scRNAseq数据设计的当前方法往往没有显示出更好的性能。scRNAseq数据的多峰性和丰富的零读段计数是scRNAseq数据的主要特征，这在差异基因表达分析方法的性能中起着重要作用，需要在开发新方法时加以考虑。
BackgroundThe analysis of single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the detection of differentially expressed (DE) genes. scRNAseq data, however, are highly heterogeneous and have a large number of zero counts, which introduces challenges in detecting DE genes. Addressing these challenges requires employing new approaches beyond the conventional ones, which are based on a nonzero difference in average expression. Several methods have been developed for differential gene expression analysis of scRNAseq data. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to evaluate and compare the performance of differential gene expression analysis methods for scRNAseq data.ResultsIn this study, we conducted a comprehensive evaluation of the performance of eleven differential gene expression analysis software tools, which are designed for scRNAseq data or can be applied to them. We used simulated and real data to evaluate the accuracy and precision of detection. Using simulated data, we investigated the effect of sample size on the detection accuracy of the tools. Using real data, we examined the agreement among the tools in identifying DE genes, the run time of the tools, and the biological relevance of the detected DE genes.ConclusionsIn general, agreement among the tools in calling DE genes is not high. There is a trade-off between true-positive rates and the precision of calling DE genes. Methods with higher true positive rates tend to show low precision due to their introducing false positives, whereas methods with high precision show low true positive rates due to identifying few DE genes. We observed that current methods designed for scRNAseq data do not tend to show better performance compared to methods designed for bulk RNAseq data. Data multimodality and abundance of zero read counts are the main characteristics of scRNAseq data, which play important roles in the performance of differential gene expression analysis methods and need to be considered in terms of the development of new methods.