The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression
Open accessAbstract
One of the most common analysis tasks in genomic research is to identify genes that are differentially expressed (DE) between experimental conditions. Empirical Bayes (EB) statistical tests using moderated genewise variances have been very effective for this purpose, especially when the number of biological replicate samples is small. The EB procedures can however be heavily influenced by a small number of genes with very large or very small variances. This article improves the differential expression tests by robustifying the hyperparameter estimation procedure. The robust procedure has the effect of decreasing the informativeness of the prior distribution for outlier genes while increasing its informativeness for other genes. This effect has the double benefit of reducing the chance that hypervariable genes will be spuriously identified as DE while increasing statistical power for the main body of genes. The robust EB algorithm is fast and numerically stable. The procedure allows exact small-sample null distributions for the test statistics and reduces exactly to the original EB procedure when no outlier genes are present. Simulations show that the robustified tests have similar performance to the original tests in the absence of outlier genes but have greater power and robustness when outliers are present. The article includes case studies for which the robust method correctly identifies and downweights genes associated with hidden covariates and detects more genes likely to be scientifically relevant to the experimental conditions. The new procedure is implemented in the limma software package freely available from the Bioconductor repository.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
Self-citationParametric empirical Bayes inference: theory and applications
Morris, C. N. (1983) · Journal of the American Statistical Association · doi:10.1080/01621459.1983.10477920 · Cited by 1,448 · cited 8×
“Morris, C. N. (1983). Parametric empirical Bayes inference: theory and applications. Journal of the American Statistical Association 78, 47–55. Murie, C., Woody, O., Lee, A. Y. and Nadon, R. (2009). Comparison of small n statistical tests of differential expression applied to microarrays. BMC Bioinformatics 10, 45. Pi…”
BackgroundInfluentialUnderstanding mechanisms underlying human gene expression variation with RNA sequencing
Pickrell, J. K., Marioni, J. C., Pai, A. A., Degner, J. F., Engelhardt, B. E., Nkadori, E., Veyrieras, J.-B., Stephens, M., Gilad, Y., & Pritchard, J. K. (2010) · Nature · doi:10.1038/nature08872 · Cited by 1,256
Stein's Estimation Rule and its Competitors—An Empirical Bayes Approach
Efron, B., & Morris, C. (1973) · Journal of the American Statistical Association · doi:10.1080/01621459.1973.10481350 · Cited by 981
Noisy Splicing Drives mRNA Isoform Diversity in Human Cells
Pickrell, J. K., Pai, A. A., Gilad, Y., & Pritchard, J. K. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001236 · Cited by 311
Sartor, M. A., Tomlinson, C. R., Wesselkamper, S. C., Sivaganesan, S., Leikauf, G. D., & Medvedovic, M. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-538 · Cited by 263
Optimizing the noise versus bias trade-off for Illumina Whole Genome Expression Beadchips
Shi, W., Oshlack, A., & Smyth, G. K. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq871 · Cited by 225
Self-citationOpposing roles of polycomb repressive complexes in hematopoietic stem and progenitor cells
Majewski, I. J., Ritchie, M. E., Phipson, B., Corbin, J., Pakusch, M., Ebert, A., Busslinger, M., Koseki, H., Hu, Y., Smyth, G. K., et al. (2010) · Blood · doi:10.1182/blood-2009-12-260760 · Cited by 135 · cited 1×
“Majewski et al. (2008, 2010) studied mice with a mutation in the Suz12 gene that results in loss of function of the Suz12 protein and hence PRC2.”
Background
Cited by
Microbiome differential abundance methods produce different results across 38 datasets
Nearing, J. T., Douglas, G. M., Hayes, M. G., MacDonald, J., Desai, D. K., Allward, N., Jones, C. M. A., Wright, R. J., Dhanani, A. S., Comeau, A. M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28034-z · Cited by 992
Checkpoint Blockade Immunotherapy Induces Dynamic Changes in PD-1-CD8+ Tumor-Infiltrating T Cells
Kurtulus, S., Madi, A., Escobar, G., Klapholz, M., Nyman, J., Christian, E., Pawlak, M., Dionne, D., Xia, J., Rozenblatt-Rosen, O., et al. (2019) · Immunity · doi:10.1016/j.immuni.2018.11.014 · Cited by 636
Georgilis, A., Klotz, S., Hanley, C. J., Herranz, N., Weirich, B., Morancho, B., Leote, A. C., D'Artista, L., Gallage, S., Seehawer, M., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.06.007 · Cited by 237
Evaluation of variability in human kidney organoids
Phipson, B., Er, P. X., Combes, A. N., Forbes, T. A., Howden, S. E., Zappia, L., Yen, H.-J., Lawlor, K. T., Hale, L. J., Sun, J., et al. (2019) · Nature Methods · doi:10.1038/s41592-018-0253-2 · Cited by 253
Self-citationOvercoming confounding plate effects in differential expression analyses of single-cell RNA-seq data
Lun, A. T. L., & Marioni, J. C. (2017) · Biostatistics · doi:10.1093/biostatistics/kxw055 · Cited by 159
Metadata
- Authors
- Phipson, B., Lee, S., Majewski, I. J., Alexander, W. S., & Smyth, G. K.
- Venue
- The Annals of Applied Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2016-06-01
- Volume
- 10
- Issue
- 2
- Pages
- 946-963
- ISSN
- 1932-6157 (ISSN-L)
- OpenAlex
- W2288929640
- Cited by
- 917
- References
- 9
- FWCI
- 25.13
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. https://app.cochat.ai/papers/10.1214/16-aoas920
- The paper
- Phipson, B., Lee, S., Majewski, I. J., Alexander, W. S., & Smyth, G. K. (2016). Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. The Annals of Applied Statistics. https://doi.org/10.1214/16-aoas920
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
