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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In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568
PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes
Mootha, V. K., Lindgren, C. M., Eriksson, K.-F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., et al. (2003) · Nature Genetics · doi:10.1038/ng1180 · Cited by 10,197
Cluster Analysis
Everitt, B. J., Landau, S., & Leese, M. (1974) · Cited by 9,186
A comparison of normalization methods for high density oligonucleotide array data based on variance and bias
Bolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.2.185 · Cited by 8,328
Summaries of Affymetrix GeneChip probe level data
Irizarry, R. A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gng015 · Cited by 4,837
Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.
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The positive false discovery rate: a Bayesian interpretation and the q-value
Storey, J. D. (2003) · The Annals of Statistics · doi:10.1214/aos/1074290335 · Cited by 2,204
Microarray data normalization and transformation
Quackenbush, J. (2002) · Nature Genetics · doi:10.1038/ng1032 · Cited by 1,975

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Osier, M. V. in DNA Microarrays and Statistical Genomic Techniques: Design, Analysis, and Interpretation of Experiments (Marcel Dekker, New York, 2005).
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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Bias correction for selecting the minimal-error classifier from many machine learning models
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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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Microarray data analysis: from disarray to consolidation and consensus

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