Cited by 1,339Gene expression and cancer classificationBioinformatics and Genomic NetworksGenomics and Phylogenetic Studies

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Microarray data analysis: from disarray to consolidation and consensus

Allison, David B., Cui, Xiangqin, Page, Grier P., Sabripour, Mahyar·2006·Nature Reviews Genetics

DOI 10.1038/nrg1749

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Abstract

In just a few years, microarrays have gone from obscurity to being almost ubiquitous in biological research. At the same time, the statistical methodology for microarray analysis has progressed from simple visual assessments of results to a weekly deluge of papers that describe purportedly novel algorithms for analysing changes in gene expression. Although the many procedures that are available might be bewildering to biologists who wish to apply them, statistical geneticists are recognizing commonalities among the different methods. Many are special cases of more general models, and points of consensus are emerging about the general approaches that warrant use and elaboration.

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Authors: Allison, D. B., Cui, X., Page, G. P., & Sabripour, M.
Affiliations: University of Alabama (US)
Corresponding author: Allison, David B.
ORCID: Allison, David B.
Cui, Xiangqin
Page, Grier P.
Venue: Nature Reviews Genetics
Publisher: Springer Science and Business Media LLC
Published: 2006-01-01
Volume: 7
Issue: 1
Pages: 55-65
ISSN: 1471-0056 (ISSN-L)
1471-0064
DOI: 10.1038/nrg1749
OpenAlex: W2021129827
Semantic Scholar: 7aeace2306487c5e5f3ff6711e1a5d3fc1e4c899
Cited by: 1,339
References: 99
Citations by year: 2026: 9 · 2025: 10 · 2024: 10 · 2023: 13 · 2022: 19 · 2021: 23 · 2020: 35 · 2019: 35 · 2018: 43 · 2017: 56 · 2016: 48 · 2015: 68 · 2014: 73 · 2013: 69 · 2012: 104
FWCI: 9.79
Type: journal article
Language: en
Update notices: erratum (2006-05-01)
Open access: closed
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Reviews Genetics (publishedVersion)
PubMed (publishedVersion)
Funders: National Science Foundation
U.S. Department of Defense
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 › Genomics and Phylogenetic Studies
Keywords: microarray data analysis, gene expression analysis, statistical analysis, normalization, differential gene expression, consensus methods, reproducibility
MeSH terms: Algorithms, Computer Simulation, Data Interpretation, Statistical, Gene Expression Regulation, Genetic Techniques, Genetics, Humans, Models, Biological, RNA, Messenger, Models, Statistical, Cluster Analysis, DNA, Complementary, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Microarray Analysis

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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 ›

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Microarray data analysis: from disarray to consolidation and consensus

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