Document Type
Article
Publication Date
7-3-2023
Original Citation
Liu Y,
Huang J,
Pandey R,
Liu P,
Therani B,
Qiu Q,
Rao S,
Geurts A,
Cowley A,
Greene A,
Liang M.
Robustness of single-cell RNA-seq for identifying differentially expressed genes. BMC Genomics. 2023;24(1):371.
Keywords
JMG, Humans, Gene Expression Profiling, Single-Cell Gene Expression Analysis, Induced Pluripotent Stem Cells, RNA-Seq, Single-Cell Analysis, Sequence Analysis, RNA
JAX Source
BMC Genomics. 2023;24(1):371.
ISSN
1471-2164
PMID
37394518
DOI
https://doi.org/10.1186/s12864-023-09487-y
Grant
This work was supported by National Institutes of Health grant HL149620, DK129964, and the Advancing a Healthier Wisconsin Endowment.
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.
Comments
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