The DOI matches its Crossref record; 97 of the 99 references it cites were found in a registry or an index.
Microarray data analysis: from disarray to consolidation and consensus
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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“The pooling strategy averages biological variations, increases statistical power, and reduces workload (Allison et al., 2006).”
From Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression (2020) · cites this paper as Allison et al., 2006
In the citing paper
“The pooling strategy averages biological variations, increases statistical power, and reduces workload (Allison et al., 2006).”
From Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression (2020) · cites this paper as Allison et al., 2006
In the citing paper
“The pooling strategy averages biological variations, increases statistical power, and reduces workload (Allison et al., 2006).”
From Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression (2020) · cites this paper as Allison et al., 2006
Citations
Cites
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2 of the 99 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- 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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phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data
McMurdie, P. J., & Holmes, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061217 · Cited by 21,942
Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855
GREAT improves functional interpretation of cis-regulatory regions
McLean, C. Y., Bristor, D., Hiller, M., Clarke, S. L., Schaar, B. T., Lowe, C. B., Wenger, A. M., & Bejerano, G. (2010) · Nature Biotechnology · doi:10.1038/nbt.1630 · Cited by 4,502
Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible
McMurdie, P. J., & Holmes, S. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003531 · Cited by 3,034
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008) · Genome Research · doi:10.1101/gr.079558.108 · Cited by 2,771
Metadata
- 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
- 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
- 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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