Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms.

Alter O; Brown PO; Botstein D

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Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms.

机译：广义奇异值分解，用于两种不同生物的基因组规模表达数据集的比较分析。

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摘要

We describe a comparative mathematical framework for two genome-scale expression data sets. This framework formulates expression as superposition of the effects of regulatory programs, biological processes, and experimental artifacts common to both data sets, as well as those that are exclusive to one data set or the other, by using generalized singular value decomposition. This framework enables comparative reconstruction and classification of the genes and arrays of both data sets. We illustrate this framework with a comparison of yeast and human cell-cycle expression data sets.

机译：我们描述了两个基因组规模表达数据集的比较数学框架。通过使用广义奇异值分解，此框架将表达表达为两种数据集以及一个数据集或另一个数据集所共有的监管程序，生物过程和实验工件的效果的叠加。该框架能够对两个数据集的基因和阵列进行比较重建和分类。我们通过比较酵母和人类细胞周期表达数据集来说明此框架。

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《Proceedings of the National Academy of Sciences of the United States of America》 |2003年第6期|P.3351-3356|共6页
作者
Alter O; Brown PO; Botstein D;
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作者单位

Departments of Genetics and Biochemistry, Stanford University, Stanford, CA 94305.;

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收录信息美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
原文格式 PDF
正文语种 eng
中图分类自然科学总论;
关键词
data set; Genome; comparative analysis; Generalized; 基因组;

机译：data set;Genome;comparative analysis;Generalized;基因组;

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6. Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms [O] . Orly Alter, Patrick O. Brown, David Botstein 2003

机译：广义奇异值分解用于两种不同生物的基因组规模表达数据集的比较分析
7. Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms [O] . Alter, Orly, Brown, Patrick O., Botstein, David 2003

机译：广义奇异值分解，用于两种不同生物的基因组规模表达数据集的比较分析

Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms.

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