The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Multiple Hypothesis Testing in Microarray Experiments
Open accessAbstract
DNA microarrays are part of a new and promising class of biotechnologies that allow the monitoring of expression levels in cells for thousands of genes simultaneously. An important and common question in DNA microarray experiments is the identification of differentially expressed genes, that is, genes whose expression levels are associated with a response or covariate of interest. The biological question of differential expression can be restated as a problem in multiple hypothesis testing: the simultaneous test for each gene of the null hypothesis of no association between the expression levels and the responses or covariates. As a typical microarray experiment measures expression levels for thousands of genes simultaneously, large multiplicity problems are generated. This article discusses different approaches to multiple hypothesis testing in the context of DNA microarray experiments and compares the procedures on microarray and simulated data sets.
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
The control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490
An improved Bonferroni procedure for multiple tests of significance
SIMES, R. J. (1986) · Biometrika · doi:10.1093/biomet/73.3.751 · Cited by 2,396
Multiple Comparison Procedures
Hochberg, Y., & Tamhane, A. C. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316672 · Cited by 2,389
Cited by
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn923 · Cited by 14,108
Harmonization of cortical thickness measurements across scanners and sites
Fortin, J.-P., Cullen, N., Sheline, Y. I., Taylor, W. D., Aselcioglu, I., Cook, P. A., Adams, P., Cooper, C., Fava, M., McGrath, P. J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.024 · Cited by 1,580
Highly nonrandom features of synaptic connectivity in local cortical circuits
Song, S., Sjöström, P. J., Reigl, M., Nelson, S., & Chklovskii, D. B. (2005) · PLoS Biology · doi:10.1371/journal.pbio.0030068 · Cited by 1,512
GOstat: find statistically overrepresented Gene Ontologies within a group of genes
Beißbarth, T., & Speed, T. P. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth088 · Cited by 1,257
FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes
Al-Shahrour, F., Díaz-Uriarte, R., & Dopazo, J. (2004) · Bioinformatics · doi:10.1093/bioinformatics/btg455 · Cited by 1,122
Metadata
- Authors
- Dudoit, S., Shaffer, J. P., & Boldrick, J. C.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 2003-02-01
- Volume
- 18
- Issue
- 1
- Pages
- 71-103
- ISSN
- 0883-4237 (ISSN-L)
- OpenAlex
- W1965138864
- Cited by
- 1,075
- References
- 4
- FWCI
- 23.81
- 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: Multiple Hypothesis Testing in Microarray Experiments. https://app.cochat.ai/papers/10.1214/ss/1056397487
- The paper
- Dudoit, S., Shaffer, J. P., & Boldrick, J. C. (2003). Multiple Hypothesis Testing in Microarray Experiments. Statistical Science. https://doi.org/10.1214/ss/1056397487
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.
