Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments
Open accessAbstract
Motivation: Cell populations are never truly homogeneous; individual cells exist in biochemical states that define functional differences between them. New technology based on microfluidic arrays combined with multiplexed quantitative polymerase chain reactions now enables high-throughput single-cell gene expression measurement, allowing assessment of cellular heterogeneity. However, few analytic tools have been developed specifically for the statistical and analytical challenges of single-cell quantitative polymerase chain reactions data. Results: We present a statistical framework for the exploration, quality control and analysis of single-cell gene expression data from microfluidic arrays. We assess accuracy and within-sample heterogeneity of single-cell expression and develop quality control criteria to filter unreliable cell measurements. We propose a statistical model accounting for the fact that genes at the single-cell level can be on (and a continuous expression measure is recorded) or dichotomously off (and the recorded expression is zero). Based on this model, we derive a combined likelihood ratio test for differential expression that incorporates both the discrete and continuous components. Using an experiment that examines treatment-specific changes in expression, we show that this combined test is more powerful than either the continuous or dichotomous component in isolation, or a t-test on the zero-inflated data. Although developed for measurements from a specific platform (Fluidigm), these tools are generalizable to other multi-parametric measures over large numbers of events. Availability: All results presented here were obtained using the SingleCellAssay R package available on GitHub (http://github.com/RGLab/SingleCellAssay). Contact: rgottard@fhcrc.org Supplementary information: Supplementary data are available at Bioinformatics online.
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Several validated edgeR-LRT genes, including Slc7a11 and Igfbp6, are involved in the response to hypoxia within endothelial cells, supporting the establishment of a chronically hypoxic state in the lumbar spinal cord.
“Several of the validated edgeR-LRT genes, including Slc7a11 and Igfbp6, are involved in the response to hypoxia within endothelial cells, supporting the establishment of a chronically hypoxic state in the lumbar spinal cord31–33.”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [31]
The Methods (§2.1) describe single-cell qPCR datasets of T cells, and the Results (§3.5) evaluate a differential-expression LRT. Searches for Slc7a11, Igfbp6, endothelial hypoxia, and the lumbar spinal cord found no passage addressing the attributed biological claim. Page numbers were unavailable in our copy. (LLM, conf=0.75)
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- Authors
- McDavid, A., Finak, G., Chattopadyay, P. K., Dominguez, M., Lamoreaux, L., Ma, S. S., Roederer, M., & Gottardo, R.
- Affiliations
- University of Washington (US)
- 1 Department of Statistics, University of Washington, Seattle, WA 98195, USA, 2Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, 3ImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD 20892, USA and 4Immunology Laboratory, Vaccine Research Center, NIAID, NIH, Bethesda, MD 20892, USA
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-12-24
- Volume
- 29
- Issue
- 4
- Pages
- 461-467
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3570210
- OpenAlex
- W2137460766
- Cited by
- 395
- References
- 19
- Citations by year
- 2026: 13 · 2025: 33 · 2024: 22 · 2023: 32 · 2022: 38 · 2021: 44 · 2020: 50 · 2019: 41 · 2018: 31 · 2017: 27 · 2016: 22 · 2015: 17 · 2014: 19 · 2013: 5 · 2012: 1
- FWCI
- 5.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Bill and Melinda Gates Foundation (OPP1032325)
- National Institute of Allergy and Infectious Diseases (U19AI089986)
- National Institute of Biomedical Imaging and Bioengineering (R01EB008400)
- 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 › Molecular Biology Techniques and Applications
- Keywords
- single-cell gene expression, single-cell qPCR, microfluidic arrays, cellular heterogeneity, quality control, differential expression analysis, zero-inflated data, likelihood ratio test, exploratory data analysis
- MeSH terms
- Humans, Quality Control, Models, Statistical, Reverse Transcriptase Polymerase Chain Reaction, Gene Expression Profiling, Microfluidic Analytical Techniques, Single-Cell Analysis
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