Differential expression analyses for single-cell RNA-Seq: old questions on new data

Abstract

Background: Single-cell RNA sequencing (scRNA-seq) is an emerging technology that enables high resolution detection of heterogeneities between cells. One important application of scRNA-seq data is to detect differential expression (DE) of genes. Currently, some researchers still use DE analysis methods developed for bulk RNA-Seq data on single-cell data, and some new methods for scRNA-seq data have also been developed. Bulk and single-cell RNA-seq data have different characteristics. A systematic evaluation of the two types of methods on scRNA-seq data is needed.

Results: In this study, we conducted a series of experiments on scRNA-seq data to quantitatively evaluate 14 popular DE analysis methods, including both of traditional methods developed for bulk RNA-seq data and new methods specifically designed for scRNA-seq data. We obtained observations and recommendations for the methods under different situations.

Conclusions: DE analysis methods should be chosen for scRNA-seq data with great caution with regard to different situations of data. Different strategies should be taken for data with different sample sizes and/or different strengths of the expected signals. Several methods for scRNA-seq data show advantages in some aspects, and DEGSeq tends to outperform other methods with respect to consistency, reproducibility and accuracy of predictions on scRNA-seq data.

Graphical abstract

Keywords

single-cell / RNA-Seq / differential expression

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AVAILABILITY OF SUPPORTING DATA

The data sets we used in our study are all come from Gene Expression Omnibus (GEO) and their accession numbers are GSE48968, GSE59127, GSE59129, GSE59130 and GSE74923 respectively.

SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at DOI 10.1007/s40484-016-0089-7.

AUTHORS’ CONTRIBUTIONS

XZ conceived the study. ZM and XZ designed the experiments and analyzed the data. ZM implemented the experiments. ZM and XZ wrote the manuscript.

ACKNOWLEDGEMENTS

The authors greatly acknowledge the contributions and suggestions from Drs. Ke Deng, Xiaowo Wang, Jun Li, Xi Wang and Zhixing Feng. This work is partially supported by the National Basic Research Program of China (2012CB316504).

COMPLIANCE WITH ETHICS GUIDELINES

The authors Zhun Miao and Xuegong Zhang declare that they have no conflict of interests. All the data sets the authors used are from public repositories.

Funding

RIGHTS & PERMISSIONS

2016 Higher Education Press and Springer-Verlag Berlin Heidelberg