An open source microarray data analysis system with GUI: Quintet

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dc.contributor.authorJun Kyoung Choe-
dc.contributor.authorTae Hoon Chung-
dc.contributor.authorSynyong Park-
dc.contributor.authorH G Cho-
dc.contributor.authorCheol-Goo Hur-
dc.date.accessioned2017-04-19T09:03:05Z-
dc.date.available2017-04-19T09:03:05Z-
dc.date.issued2005-
dc.identifier.issn0302-974X-
dc.identifier.uri10.1007/11548706_41ko
dc.identifier.urihttps://oak.kribb.re.kr/handle/201005/7022-
dc.description.abstractWe address Quintet, an R-based unified cDNA microarray data analysis system with GUI. Five principal categories of microarray data analysis have been coherently integrated in Quintet: data processing steps such as faulty spot filtering and normalization, data quality assessment (QA), identification of differentially expressed genes (DEGs), clustering of gene expression profiles, and classification of samples. Though many microarray data analysis systems normally consider DEG identification and clustering/classification the most important problems, we emphasize that data processing and QA are equally important and should be incorporated into the regular-base data analysis practices because microarray data are very noisy. In each analysis category, customized plots and statistical summaries are also given for users convenience. Using these plots and summaries, analysis results can be easily examined for their biological plausibility and compared with other results. Since Quintet is written in R, it is highly extendable so that users can insert new algorithms and experiment them with minimal efforts. Also, the GUI makes it easy to learn and use and since R-language and its GUI engine, Tcl/Tk, are available in all operating systems, Quintet is OS-independent too.-
dc.titleAn open source microarray data analysis system with GUI: Quintet-
dc.title.alternativeAn open source microarray data analysis system with GUI: Quintet-
dc.typeArticle-
dc.citation.titleLecture Notes in Artificial Intelligence-
dc.citation.number1-
dc.citation.endPage401-
dc.citation.startPage392-
dc.citation.volume3642-
dc.contributor.affiliatedAuthorJun Kyoung Choe-
dc.contributor.affiliatedAuthorTae Hoon Chung-
dc.contributor.affiliatedAuthorSynyong Park-
dc.contributor.affiliatedAuthorCheol-Goo Hur-
dc.contributor.alternativeName최준경-
dc.contributor.alternativeName정태훈-
dc.contributor.alternativeName박선용-
dc.contributor.alternativeName조환규-
dc.contributor.alternativeName허철구-
dc.identifier.bibliographicCitationLecture Notes in Artificial Intelligence, vol. 3642, no. 1, pp. 392-401-
dc.identifier.doi10.1007/11548706_41-
dc.description.journalClassN-
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