Genesis: cluster analysis of microarray data
Open accessAbstract
Summary: A versatile, platform independent and easy to use Java suite for large-scale gene expression analysis was developed. Genesis integrates various tools for microarray data analysis such as filters, normalization and visualization tools, distance measures as well as common clustering algorithms including hierarchical clustering, self-organizing maps, k-means, principal component analysis, and support vector machines. The results of the clustering are transparent across all implemented methods and enable the analysis of the outcome of different algorithms and parameters. Additionally, mapping of gene expression data onto chromosomal sequences was implemented to enhance promoter analysis and investigation of transcriptional control mechanisms. Availability: http://genome.tugraz.at Contact: zlatko.trajanoski@tugraz.at * To whom correspondence should be addressed.
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After normalization, gene shaving, bootstrapping, and cluster analysis were performed, and slide effect, dye effect, variety effect, and duplicate design were taken into account in an ANOVA model.
“After normalization, gene shaving (22), bootstrapping (23), and cluster analysis were performed (24), and the slide effect, dye effect, variety effect, and duplicate design were taken into account in an ANOVA model (25).”
From A High-Fat Diet Coordinately Downregulates Genes Required for Mitochondrial Oxidative Phosphorylation in Skeletal Muscle (2005) · cites this paper as [24]
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- Authors
- Sturn, A., Quackenbush, J., & Trajanoski, Z.
- Affiliations
- Graz University of Technology (AT)
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2002-01-01
- Volume
- 18
- Issue
- 1
- Pages
- 207-208
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2170984819
- Cited by
- 1,761
- References
- 6
- Citations by year
- 2026: 13 · 2025: 25 · 2024: 30 · 2023: 35 · 2022: 61 · 2021: 96 · 2020: 97 · 2019: 94 · 2018: 82 · 2017: 115 · 2016: 106 · 2015: 124 · 2014: 92 · 2013: 87 · 2012: 96
- FWCI
- 13.56
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Austrian Science Fund (P14298)
- Bundesministerium für Verkehr, Innovation und Technologie
- 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 clustering, hierarchical clustering, self-organizing maps, K-means clustering, principal component analysis, support vector machine, data normalization, gene expression visualization, promoter analysis, transcriptional regulation
- MeSH terms
- Algorithms, Programming Languages, Promoter Regions, Genetic, Software, Transcription, Genetic, Cluster Analysis, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Principal Component Analysis
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