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Detecting intergene correlation changes in microarray analysis: a new approach to gene selection
Open accessAbstract
Background: Microarray technology is commonly used as a simple screening tool with a focus on selecting genes that exhibit extremely large differential expressions between different phenotypes. It lacks the ability to select genes that change their relationships with other genes in different biological conditions (differentially correlated genes). We intend to enrich the above procedure by proposing a nonparametric selection procedure that selects differentially correlated genes. Results: Using both simulations and resampling techniques, we found that our procedure correctly detected genes that were not differentially expressed but differentially correlated. We also applied our procedure to a set of biological data and found some potentially important genes that were not selected by the traditional method. Discussion and Conclusion: Microarray technology yields multidimensional information on the function of the whole genome. Rather than treating intergene correlation as a nuisance to the traditional gene selection procedures which are essentially univariate, our method utilizes the rich information contained in the correlation as a new selection criterion. It can provide additional useful candidate genes for the biologists.
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“An increasingly used method that goes beyond traditional co-expression networks is differential co-expression analysis [4–7]. This approach identifies genes with varying co-expression partners under different conditions, such as disease states [4, 8–10], tissue types [11] and developmental stages [12], because these genes are more likely to be regulators that underlie phenotypic differences.”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [8]
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“Genes that are differentially co-expressed between different sample groups are more likely to be regulators, and are therefore likely to explain differences between phenotypes [4, 8–10].”
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“Differential co-expression analysis has been used to identify genes underlying differences between healthy and disease samples [4, 8–10] or between different tissues [11], cell types [5] or species [132, 133].”
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“These genes appear to play a regulatory part in the difference in the phenotype observed between two groups (Figure 3D) [8–10].”
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Metadata
- Authors
- Hu, R., Qiu, X., Glazko, G., Klebanov, L., & Yakovlev, A.
- Affiliations
- University of Rochester Medicine (US)
- University of Rochester (US)
- Charles University (CZ)
- Corresponding author
- Qiu, Xing
- Venue
- BMC Bioinformatics
- Published
- 2009-01-15
- Volume
- 10
- Issue
- 1
- Pages
- 20-20
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC2657217
- OpenAlex
- W2037580712
- Cited by
- 79
- References
- 27
- Citations by year
- 2024: 4 · 2023: 1 · 2022: 4 · 2021: 9 · 2020: 3 · 2019: 3 · 2018: 4 · 2017: 6 · 2016: 7 · 2015: 5 · 2014: 8 · 2013: 7 · 2012: 6
- FWCI
- 2.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health
- National Institute of General Medical Sciences (GM075299, GM079259, R01GM075299, R21GM079259)
- 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 › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- gene selection, microarray data, gene expression correlation, candidate genes, resampling
- MeSH terms
- Algorithms, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Databases, Genetic
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