Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods.
Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods.
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DOI：
10.3389/fgene.2017.00062
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发表时间：
2017
影响因子：
3.7
通讯作者：
Di Camillo B
中科院分区：
文献类型：
--
作者：
Dal Molin A;Baruzzo G;Di Camillo B
单细胞转录组测序或单细胞RNA测序现已成为鉴定新细胞类型和研究随机基因表达的主导技术。近年来，已经提出了用于分析单细胞RNA测序数据的各种工具，其中许多工具的目的是进行差异表达分析。在这项工作中，我们比较了四种不同的单细胞RNA测序差异表达的工具，以及两种流行的方法，最初是为了分析批量RNA测序数据而开发的，但主要应用于单细胞数据。我们讨论了两个真实的和一个合成数据集上获得的结果，沿着考虑单细胞差异表达分析的前景。特别是，我们探讨了四种不同情况下的方法性能，模仿不同的单峰或双峰分布的数据，作为单细胞转录组学的特征。我们观察到所选方法在精确度和召回率，检测到的差异表达基因的数量和整体性能方面存在显着差异。在全球范围内，我们研究中获得的结果表明，很难确定一种性能最佳的工具，需要努力改进单细胞RNA测序数据分析的方法，并获得更好的结果准确性。
The sequencing of the transcriptomes of single-cells, or single-cell RNA-sequencing, has now become the dominant technology for the identification of novel cell types and for the study of stochastic gene expression. In recent years, various tools for analyzing single-cell RNA-sequencing data have been proposed, many of them with the purpose of performing differentially expression analysis. In this work, we compare four different tools for single-cell RNA-sequencing differential expression, together with two popular methods originally developed for the analysis of bulk RNA-sequencing data, but largely applied to single-cell data. We discuss results obtained on two real and one synthetic dataset, along with considerations about the perspectives of single-cell differential expression analysis. In particular, we explore the methods performance in four different scenarios, mimicking different unimodal or bimodal distributions of the data, as characteristic of single-cell transcriptomics. We observed marked differences between the selected methods in terms of precision and recall, the number of detected differentially expressed genes and the overall performance. Globally, the results obtained in our study suggest that is difficult to identify a best performing tool and that efforts are needed to improve the methodologies for single-cell RNA-sequencing data analysis and gain better accuracy of results.