Robustness of single-cell RNA-seq for identifying differentially expressed genes

Yong Liu; Jing Huang; Rajan Pandey; Pengyuan Liu; Bhavika Therani; Qiongzi Qiu; Sridhar Rao; Aron M Geurts; Allen W Cowley Jr; Andrew S Greene; Mingyu Liang

doi:10.1186/s12864-023-09487-y

Robustness of single-cell RNA-seq for identifying differentially expressed genes

BMC Genomics. 2023 Jul 3;24(1):371. doi: 10.1186/s12864-023-09487-y.

Authors

Yong Liu^#^{1

2}, Jing Huang^#^{1

2}, Rajan Pandey¹, Pengyuan Liu^{1

2

3

4

5}, Bhavika Therani^{1

2}, Qiongzi Qiu^{1

2}, Sridhar Rao^{6

7

8}, Aron M Geurts¹, Allen W Cowley Jr¹, Andrew S Greene⁹, Mingyu Liang^{10

11}

Affiliations

¹ Department of Physiology, Center of Systems Molecular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA.
² Department of Physiology, University of Arizona College of Medicine - Tucson, Tucson, AZ, USA.
³ Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
⁴ Cancer Center, Zhejiang University, Hangzhou, China.
⁵ Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, China.
⁶ Versiti Blood Research Institute, Milwaukee, WI, USA.
⁷ Department of Cell Biology, Neurobiology, and Anatomy, Medical College of Wisconsin, Milwaukee, WI, USA.
⁸ Division of Pediatric Hematology/Oncology/Transplantation, Medical College of Wisconsin, Milwaukee, WI, USA.
⁹ The Jackson Laboratory, Bar Harbor, ME, USA.
¹⁰ Department of Physiology, Center of Systems Molecular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA. mliang1@arizona.edu.
¹¹ Department of Physiology, University of Arizona College of Medicine - Tucson, Tucson, AZ, USA. mliang1@arizona.edu.

^# Contributed equally.

Abstract

Background: A common feature of single-cell RNA-seq (scRNA-seq) data is that the number of cells in a cell cluster may vary widely, ranging from a few dozen to several thousand. It is not clear whether scRNA-seq data from a small number of cells allow robust identification of differentially expressed genes (DEGs) with various characteristics.

Results: We addressed this question by performing scRNA-seq and poly(A)-dependent bulk RNA-seq in comparable aliquots of human induced pluripotent stem cells-derived, purified vascular endothelial and smooth muscle cells. We found that scRNA-seq data needed to have 2,000 or more cells in a cluster to identify the majority of DEGs that would show modest differences in a bulk RNA-seq analysis. On the other hand, clusters with as few as 50-100 cells may be sufficient for identifying the majority of DEGs that would have extremely small p values or transcript abundance greater than a few hundred transcripts per million in a bulk RNA-seq analysis.

Conclusion: Findings of the current study provide a quantitative reference for designing studies that aim for identifying DEGs for specific cell clusters using scRNA-seq data and for interpreting results of such studies.

Keywords: Gene expression; RNA-seq; Single cell; Stem cell.

MeSH terms

Gene Expression Profiling* / methods
Humans
Induced Pluripotent Stem Cells*
RNA-Seq
Sequence Analysis, RNA / methods
Single-Cell Analysis / methods
Single-Cell Gene Expression Analysis

Abstract

MeSH terms

Grants and funding