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Cluster analysis and display of genome-wide expression patterns
Open accessAbstract
A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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“Hierarchical clustering based on the average linkage of Pearson Correlation (Eisen et al., 1998) of miRNA profiles revealed non-random partition of the samples into two major branches, one containing all five individual neuron types and the other containing the two tissue types (Figure 3).”
From Cell-Type-Based Analysis of MicroRNA Profiles in the Mouse Brain (2012) · cites this paper as Eisen et al., 1998
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Metadata
- Authors
- Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D.
- Affiliations
- Howard Hughes Medical Institute (US)
- Corresponding author
- Botstein, David
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 1998-12-08
- Volume
- 95
- Issue
- 25
- Pages
- 14863-14868
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC24541
- OpenAlex
- W2150926065
- Cited by
- 16,184
- References
- 0
- Citations by year
- 2026: 115 · 2025: 129 · 2024: 164 · 2023: 251 · 2022: 230 · 2021: 264 · 2020: 318 · 2019: 366 · 2018: 408 · 2017: 428 · 2016: 538 · 2015: 588 · 2014: 665 · 2013: 692 · 2012: 763
- FWCI
- 73.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Howard Hughes Medical Institute
- Alfred P. Sloan Foundation
- National Institutes of Health
- National Human Genome Research Institute (R01HG000983)
- National Eye Institute
- National Cancer Institute (CA46406, R01CA077097)
- National Institute of General Medical Sciences (R37GM046406, R01GM046406)
- 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 › Fungal and yeast genetics research
- Keywords
- gene expression clustering, microarray hybridization, gene co-expression, Saccharomyces cerevisiae, human gene expression, gene function prediction, gene function similarity
- MeSH terms
- Multigene Family, Humans, Saccharomyces cerevisiae, Gene Expression, Genome, Human, Cluster Analysis, Genome, Fungal
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