Cited by 129Single-cell and spatial transcriptomicsGene expression and cancer classificationGenomics and Phylogenetic Studies

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A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples

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Chen, Wanqiu, Zhao, Yongmei, Chen, Xin, Yang, Zhaowei, Xu, Xiaojiang, Bi, Yingtao, Chen, Vicky, Li, Jing, Choi, Hannah, Ernest, Ben, Tran, Bao, Mehta, Monika, Kumar, Parimal, Farmer, Andrew, Mir, Alain, Mehra, Urvashi Ann, Li, Jian-Liang, Moos, Malcolm, Xiao, Wenming, Wang, Charles·2021·Nature Biotechnology

DOI 10.1038/s41587-020-00748-9PMC PMC11245320

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Abstract

Comparing diverse single-cell RNA sequencing (scRNA-seq) datasets generated by different technologies and in different laboratories remains a major challenge. Here we address the need for guidance in choosing algorithms leading to accurate biological interpretations of varied data types acquired with different platforms. Using two well-characterized cellular reference samples (breast cancer cells and B cells), captured either separately or in mixtures, we compared different scRNA-seq platforms and several preprocessing, normalization and batch-effect correction methods at multiple centers. Although preprocessing and normalization contributed to variability in gene detection and cell classification, batch-effect correction was by far the most important factor in correctly classifying the cells. Moreover, scRNA-seq dataset characteristics (for example, sample and cellular heterogeneity and platform used) were critical in determining the optimal bioinformatic method. However, reproducibility across centers and platforms was high when appropriate bioinformatic methods were applied. Our findings offer practical guidance for optimizing platform and software selection when designing an scRNA-seq study.

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Authors: Chen, W., Zhao, Y., Chen, X., Yang, Z., Xu, X., Bi, Y., Chen, V., Li, J., Choi, H., Ernest, B., Tran, B., Mehta, M., Kumar, P., Farmer, A., Mir, A., Mehra, U. A., Li, J.-L., Moos, M., Xiao, W., & Wang, C.
Affiliations: Loma Linda University (US)
Frederick National Laboratory for Cancer Research (US)
National Institute of Environmental Health Sciences (US)
AbbVie (United States) (US)
Takara (United States) (US)
Center for Biologics Evaluation and Research (US)
United States Food and Drug Administration (US)
Corresponding author: Xiao, Wenming
Wang, Charles
ORCID: Chen, Wanqiu
Zhao, Yongmei
Chen, Xin
Yang, Zhaowei
Xu, Xiaojiang
Bi, Yingtao
Chen, Vicky
Li, Jing
Choi, Hannah
Tran, Bao
Mehta, Monika
Kumar, Parimal
Farmer, Andrew
Li, Jian-Liang
Moos, Malcolm
Xiao, Wenming
Wang, Charles
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2020-12-21
Volume: 39
Issue: 9
Pages: 1103-1114
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/s41587-020-00748-9
PMC: PMC11245320
OpenAlex: W3117537317
Cited by: 114
References: 55
Citations by year: 2026: 21 · 2025: 15 · 2024: 16 · 2023: 21 · 2022: 21 · 2021: 19
FWCI: 4.93
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Biotechnology (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: U.S. Department of Health & Human Services | NIH | NIH Office of the Director (S10OD019960)
Dr. Charles A. Sims’ gift to LLU Center for Genomics
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords: single-cell RNA sequencing, benchmarking, reference samples, breast cancer cells, B cells, batch effect correction, normalization methods, preprocessing pipeline, cell classification, gene detection, platform comparison, cellular heterogeneity
MeSH terms: Algorithms, B-Lymphocytes, Breast Neoplasms, Female, Humans, Sequence Analysis, RNA, Benchmarking, Gene Expression Profiling, Cell Line, Tumor, Single-Cell Analysis, Datasets as Topic

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A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples

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