Dal Molin Alessandra, Baruzzo Giacomo, Di Camillo Barbara
Department of Information Engineering, University of PadovaPadova, Italy.
Front Genet. 2017 May 23;8:62. doi: 10.3389/fgene.2017.00062. eCollection 2017.
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.
单细胞转录组测序，即单细胞RNA测序，现已成为识别新型细胞类型和研究随机基因表达的主导技术。近年来，已提出了各种用于分析单细胞RNA测序数据的工具，其中许多工具旨在进行差异表达分析。在这项工作中，我们比较了四种用于单细胞RNA测序差异表达分析的不同工具，以及两种最初为分析大量RNA测序数据而开发，但在很大程度上应用于单细胞数据的常用方法。我们讨论了在两个真实数据集和一个合成数据集上获得的结果，以及关于单细胞差异表达分析前景的思考。特别是，我们在四种不同场景中探索了这些方法的性能，模拟了数据的不同单峰或双峰分布，这是单细胞转录组学的特征。我们观察到所选方法在精度和召回率、检测到的差异表达基因数量以及整体性能方面存在显著差异。总体而言，我们研究中获得的结果表明，很难确定一个性能最佳的工具，需要努力改进单细胞RNA测序数据分析方法并提高结果的准确性。
