Exploring expression data: Identification and analysis of coexpressed genes
Exploring expression data: Identification and analysis of coexpressed genes
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DOI：
10.1101/gr.9.11.1106
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发表时间：
1999-11-01
期刊：
影响因子：
7
通讯作者：
Yooseph, S
中科院分区：
文献类型：
--
作者：
Heyer, LJ;Kruglyak, S;Yooseph, S
分析程序需要从大量的基因表达数据中提取有用的信息。这项工作描述了一套分析工具及其应用酵母细胞周期数据。我们的方法的组成部分是（1）减少假阳性数量的相似性度量，（2）专门为分组基因表达模式设计的新聚类算法，以及（3）允许用户反馈和验证的英特尔主动图形聚类分析工具。我们使用我们的算法生成的集群来总结全基因组的表达，并启动监督聚类的基因到生物学上有意义的群体。
Analysis procedures are needed to extract useful information from the large amount of gene expression data that is becoming available. This work describes a set of analytical tools and their application to yeast cell cycle data. The components of our approach are (1) a similarity measure that reduces the number of False positives, (2) a new clustering algorithm designed specifically For grouping gene expression patterns, and (3) an intel active graphical cluster analysis tool that allows user feedback and validation. We use the clusters generated by our algorithm to summarize genome-wide expression and to initiate supervised clustering of genes into biologically meaningful groups.