A meta-clustering analysis indicates distinct pattern alteration between two series of gene expression profiles for induced ischemic tolerance in rats
A meta-clustering analysis indicates distinct pattern alteration between two series of gene expression profiles for induced ischemic tolerance in rats
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DOI：
10.1152/physiolgenomics.00107.2004
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发表时间：
2005-04-14
影响因子：
4.6
通讯作者：
Aburatani, H
中科院分区：
文献类型：
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作者：
Kano, M;Tsutsumi, S;Aburatani, H
我们已经开发了一种可视化的方法，称为“聚类重叠分布图”（CODM），比较聚类结果的时间序列基因表达谱在两种不同的条件下产生的。尽管人们已经提出了各种针对基因表达数据的聚类算法，但很少有有效的方法来比较不同条件下的聚类结果。利用CODM，三维空间和颜色的利用允许直观地可视化簇集组成的变化、两种条件之间基因表达模式的变化以及与其他已知基因信息（如转录因子）的关系。我们将CODM应用于从大鼠四血管闭塞模型结合全身性低血压和时间匹配的假手术对照动物（假手术）获得的时间序列基因表达谱，确定两者之间的不同模式改变。比较不同条件下基因表达水平的时间序列动态变化在基因表达谱分析的各个领域（包括毒理基因组学和药物基因组学）都具有重要意义。CODM对于这些领域中的各种类型的分析都很有价值，因为它集成并同时可视化了聚类结果中的各种类型的信息。
We have developed a visualization methodology, called a "cluster overlap distribution map" (CODM), for comparing the clustering results of time series gene expression profiles generated under two different conditions. Although various clustering algorithms for gene expression data have been proposed, there are few effective methods to compare clustering results for different conditions. With CODM, the utilization of three-dimensional space and color allows intuitive visualization of changes in cluster set composition, changes in the expression patterns of genes between the two conditions, and relationship with other known gene information, such as transcription factors. We applied CODM to time series gene expression profiles obtained from rat four-vessel occlusion models combined with systemic hypotension and time-matched sham control animals (with sham operation), identifying distinct pattern alteration between the two. Comparisons of dynamic changes of time series gene expression levels under different conditions are important in various fields of gene expression profiling analysis, including toxicogenomics and pharmacogenomics. CODM will be valuable for various types of analyses within these fields, because it integrates and simultaneously visualizes various types of information across clustering results.