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

Microarray data analysis: from disarray to consolidation and consensus

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

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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Crossref · 10.1038/nrg1749

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Research works printed but not linked: 2

  1. Osier, M. V. in DNA Microarrays and Statistical Genomic Techniques: Design, Analysis, and Interpretation of Experiments (Marcel Dekker, New York, 2005).
  2. Dobbin, K. & Simon, R. Sample size determination in microarray experiments for class comparison and prognostic classification. Biostatistics 6, 27–38 (2005). · DOI

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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.
Venue
Nature Reviews Genetics
Published
2006-01-01
Volume
7
Issue
1
Pages
55-65
ISSN
  • 1471-0056 (ISSN-L)
  • 1471-0064
OpenAlex
W2021129827
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
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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