A web-based tool for principal component and significance analysis of microarray data

Alexei A. Sharov, Dawood B. Dudekula, Minoru S.H. Ko

研究成果: Article査読

196 被引用数 (Scopus)

抄録

Summary: We have developed a program for microarray data analysis, which features the false discovery rate for testing statistical significance and the principal component analysis using the singular value decomposition method for detecting the global trends of gene-expression patterns. Additional features include analysis of variance with multiple methods for error variance adjustment, correction of cross-channel correlation for two-color microarrays, identification of genes specific to each cluster of tissue samples, biplot of tissues and corresponding tissue-specific genes, clustering of genes that are correlated with each principal component (PC), three-dimensional graphics based on virtual reality modeling language and sharing of PC between different experiments. The software also supports parameter adjustment, gene search and graphical output of results. The software is implemented as a web tool and thus the speed of analysis does not depend on the power of a client computer.

本文言語English
ページ(範囲)2548-2549
ページ数2
ジャーナルBioinformatics
21
10
DOI
出版ステータスPublished - 2005 5 15
外部発表はい

ASJC Scopus subject areas

  • 統計学および確率
  • 生化学
  • 分子生物学
  • コンピュータ サイエンスの応用
  • 計算理論と計算数学
  • 計算数学

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