Singular value decomposition for genome-wide expression data processing and modeling
Singular value decomposition for genome-wide expression data processing and modeling
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DOI：
10.1073/pnas.97.18.10101
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发表时间：
2000-08-29
影响因子：
11.1
通讯作者：
Botstein, D
中科院分区：
文献类型：
--
作者：
Alter, O;Brown, PO;Botstein, D
我们描述了使用奇异值分解转换全基因组的表达数据从基因x阵列空间减少对角化的“eigengenes”x“eigenarrays”空间，其中的eigengenes（或eigenarrays）是唯一的正交叠加的基因（或阵列）。通过过滤出被推断为代表噪声或实验伪像的特征基因和特征阵列来归一化数据，使得能够在不同实验中跨不同阵列对不同基因的表达进行有意义的比较。根据特征基因和特征阵列对数据进行分类，给出了基因表达动态的全局图，其中单个基因和阵列似乎被分类为具有相似调节和功能或相似细胞状态和生物表型的组。在标准化和排序之后，显著的特征基因和特征阵列可以与观察到的调节剂的全基因组效应相关联，或者与测量的样品相关联，其中这些调节剂分别是过度活跃或不活跃的。
We describe the use of singular value decomposition in transforming genome-wide expression data from genes x arrays space to reduced diagonalized "eigengenes" x "eigenarrays" space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes land eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.