A web-based tool for principal component and significance analysis of microarray data
A web-based tool for principal component and significance analysis of microarray data
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DOI：
10.1093/bioinformatics/bti343
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发表时间：
2005-05-15
期刊：
影响因子：
5.8
通讯作者：
Ko, MSH
中科院分区：
文献类型：
--
作者：
Sharov, AA;Dudekula, DB;Ko, MSH
我们开发了一个微阵列数据分析程序，其特点是使用错误发现率来测试统计显着性，并使用奇异值分解方法进行主成分分析来检测基因表达模式的全局趋势。其他功能包括使用多种误差方差调整方法进行方差分析、校正双色微阵列的跨通道相关性、识别每个组织样本簇的特异性基因、组织和相应组织特异性基因的双标图、与每个主成分 (PC) 相关的基因聚类、基于虚拟现实建模语言的三维图形以及不同实验之间共享 PC。该软件还支持参数调整、基因搜索和结果的图形输出。该软件作为网络工具实现，因此分析速度不依赖于客户端计算机的功能。
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.