Exploring expression data: Identification and analysis of coexpressed genes

被引:670
作者
Heyer, LJ [1 ]
Kruglyak, S [1 ]
Yooseph, S [1 ]
机构
[1] Univ So Calif, Dept Math, Los Angeles, CA 90089 USA
关键词
D O I
10.1101/gr.9.11.1106
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 [生物化学与分子生物学]; 081704 [应用化学];
摘要
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.
引用
收藏
页码:1106 / 1115
页数:10
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