The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis
Open accessAbstract
It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study. Not only can this reduce power or induce unwanted dependence across genes, but it can also introduce sources of spurious signal to many genes. This phenomenon is true even for well-designed, randomized studies. We introduce "surrogate variable analysis" (SVA) to overcome the problems caused by heterogeneity in expression studies. SVA can be applied in conjunction with standard analysis techniques to accurately capture the relationship between expression and any modeled variables of interest. We apply SVA to disease class, time course, and genetics of gene expression studies. We show that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies.
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“Some methods (specifically sva and PEER) that leveraged all data across all sites were quite successful at ameliorating the high rate of false positives (Fig. 1, Supplementary Fig. 2), removing 85.1% to 87.7% of the original total false positive DEGs.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [27]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Surrogate variable analyses (SVAs) revealed 4 significant SVA vectors that were used as covariates in the model to correct for technical artefacts and hidden confounds (Leek & Storey, 2007).”
From DNA methylation of NR3C1 and FKBP5 is associated with posttraumatic stress disorder, posttraumatic growth, and resilience. (2020) · cites this paper as Leek & Storey, 2007
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“We used surrogate variable analysis (SVA) and 26 loosely described “biologic clusters” of tissues and cells to clearly demonstrate that, after adjusting for surrogate variables, similar tissue and cell types clustered together across experiments (Supplemental Fig. S3; Leek and Storey 2007).”
From Toward the human cellular microRNAome (2017) · cites this paper as Leek and Storey 2007
In the citing paper
“However, the majority of RNA-seq false positives (>85%) could successfully be removed by subtracting the effects of latent variables identified by either sva26,27 or PEER30, which could be achieved by jointly analyzing the set of measurements of all genes across multiple sites, without a decrease in the sensitivity or specificity of DEG detection at each site or across sites.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [27]
In the citing paper
“We used surrogate variable correction in our analyses to account for such unmeasured confounders (43), which is a standard approach (44).”
From Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder (2018) · cites this paper as [43]
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Cites
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Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Self-citationBrown, R. A., Hastie, T. J., & Tibshirani, R. J. (1991) · Biometrics · doi:10.2307/2532174 · Cited by 8,263
A Direct Approach to False Discovery Rates
Storey, J. D. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00346 · Cited by 5,603
Self-citationLamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., et al. (2006) · Science · doi:10.1126/science.1132939 · Cited by 5,257
Testing Statistical Hypotheses
Niewiadomska-Bugaj, M., & Bartoszynski, R. (2021) · Wiley series in probability and statistics · doi:10.1002/9781119243830.ch12 · Cited by 5,188
Exploring the metabolic and genetic control of gene expression on a genomic scale.
Derisi, J., Iyer, V., & Brown, P. (1997) · Science · doi:10.1126/science.278.5338.680 · Cited by 4,832
Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes
Gasch, A. P., Spellman, P. T., Kao, C. M., Carmel-Harel, O., Eisen, M. B., Storz, G., Botstein, D., & Brown, P. O. (2000) · Molecular Biology of the Cell · doi:10.1091/mbc.11.12.4241 · Cited by 4,815
Research works printed but not linked: 1
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Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488
Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E., & Storey, J. D. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts034 · Cited by 6,231
Self-citationGenetic effects on gene expression across human tissues
GTEx Consortium (2017) · Nature · doi:10.1038/nature24277 · Cited by 4,036
DNA methylation arrays as surrogate measures of cell mixture distribution
Houseman, E. A., Accomando, W. P., Koestler, D. C., Christensen, B. C., Marsit, C. J., Nelson, H. H., Wiencke, J. K., & Kelsey, K. T. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-86 · Cited by 3,175
DNA methylation-based classification of central nervous system tumours
Capper, D., Jones, D., Sill, M., Hovestadt, V., Schrimpf, D., Sturm, D., Koelsche, C., Sahm, F., Chávez, L., Reuß, D., et al. (2018) · Nature · doi:10.1038/nature26000 · Cited by 3,114
Metadata
- Authors
- Leek, J. T., & Storey, J. D.
- Affiliations
- University of Washington (US)
- Corresponding author
- Storey, John D
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2007-09-28
- Volume
- 3
- Issue
- 9
- Pages
- e161
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC1994707
- OpenAlex
- W2137499573
- Cited by
- 1,930
- References
- 46
- Citations by year
- 2026: 77 · 2025: 114 · 2024: 131 · 2023: 126 · 2022: 155 · 2021: 195 · 2020: 177 · 2019: 156 · 2018: 141 · 2017: 143 · 2016: 112 · 2015: 79 · 2014: 87 · 2013: 74 · 2012: 54
- FWCI
- 8.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, other-oa)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (R01HG002913)
- National Institute of General Medical Sciences (U54GM062119, U54 GM2119)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- surrogate variable analysis, gene expression heterogeneity, unmeasured confounding, time-course expression, reproducibility, statistical power
- MeSH terms
- Algorithms, Breast Neoplasms, Computer Simulation, Data Interpretation, Statistical, Female, Humans, Kidney, Genetic Linkage, Mutation, Saccharomyces cerevisiae, Time Factors, Reproducibility of Results, Gene Expression, Genome, Human, Linear Models, Genome, Fungal, Genetic Heterogeneity, Genes, BRCA1, Quantitative Trait, Heritable, Oligonucleotide Array Sequence Analysis, Genes, BRCA2, Epigenesis, Genetic
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