Application of random matrix theory to microarray data for discovering functional gene modules
Application of random matrix theory to microarray data for discovering functional gene modules
复制标题
随机矩阵理论在微阵列数据中的应用以发现功能基因模块
DOI：
10.1103/physreve.73.031924
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发表时间：
2006-03-01
影响因子：
2.4
通讯作者：
Zhou, JZ
中科院分区：
文献类型：
--
作者：
Luo, F;Zhong, JX;Zhou, JZ
结果表明，酵母基因芯片共表达相关矩阵的光谱起伏遵循随机矩阵理论(RMT)的高斯正交集成(GOE)描述，去除相关系数的小值会导致RMT的GOE统计量向泊松统计量的转变。这种转变与基因表达网络从全球网络到孤立模块网络的结构变化直接相关。
We show that spectral fluctuation of coexpression correlation matrices of yeast gene microarray profiles follows the description of the Gaussian orthogonal ensemble (GOE) of the random matrix theory (RMT) and removal of small values of the correlation coefficients results in a transition from the GOE statistics to the Poisson statistics of the RMT. This transition is directly related to the structural change of the gene expression network from a global network to a network of isolated modules.