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- SpotWhatR: a user-friendly microarray data analysis system
- Tie Koide1*, Silvia M. Salem-Izacc1, Suely L. Gomes1 and Ricardo Z.N. Vêncio2,3*
- 1Departamento de Bioquímica, Instituto de Química, Universidade de São Paulo,
- Av. Prof. Lineu Prestes, 748, 05508-900 São Paulo, SP, Brasil
- 2Instituto Israelita de Ensino e Pesquisa Albert Einstein, Hospital Israelita Albert Einstein,
- Av. Albert Einstein, 627, 05651-901 São Paulo, SP, Brasil
- 3BIOINFO-USP Núcleo de Pesquisas em Bioinformática,
- Departamento de Estatística, Instituto de Matemática e Estatística, Universidade de São Paulo,
- Rua do Matão, 1010, 05508-090 São Paulo, SP, Brasil
- *These authors contributed equally to this study.
- Corresponding author: R.Z.N. Vêncio
- E-mail: [email protected]
- Genet. Mol. Res. 5 (1): 93-107 (2006)
- Received January 10, 2006
- Accepted February 17, 2006
- Published March 31, 2006
ABSTRACT. SpotWhatR is a user-friendly microarray data analysis tool that runs under a widely and freely available R statistical language
(http://www.r-project.org) for Windows and Linux operational systems. The aim of SpotWhatR is to help the researcher to analyze microarray data by providing basic tools for data visualization, normalization, determination of differentially expressed genes, summarization by Gene Ontology terms, and clustering analysis. SpotWhatR allows researchers who are not familiar with computational programming to choose the most suitable analysis for their microarray dataset. Along with well-known procedures used in microarray data analysis, we have introduced a stand-alone implementation of the HTself method, especially designed to find differentially expressed genes in low-replication contexts. This approach is more compatible with our local reality than the usual statistical methods. We provide several examples derived from the Blastocladiella emersonii and Xylella fastidiosa Microarray Projects. SpotWhatR is freely available at
http://blasto.iq.usp.br/~tkoide/SpotWhatR, in English and Portuguese versions. In addition, the user can choose between “single experiment” and “batch processing” versions.
Key words: Microarray data analysis, Data vizualization, Clustering, Normalization, User-friendly system, Gene Ontology
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