Summary: We have developed two novel methods for Singular Value Decomposition analysis (SVD) of microarray data. The first is a threshold-based method for obtaining gene groups, and the second is a method for obtaining a measure of confidence in SVD analysis. Gene groups are obtained by identifying elements of the left singular vectors, or gene coefficient vectors, that are greater in magnitude than the threshold \batchmode \documentclass[fleqn,10pt,legalpaper]article} \usepackageamssymb} \usepackageamsfonts} \usepackageamsmath} \pagestyleempty} \begindocument} \(WN^-}1/2}\) \enddocument}, where \batchmode \documentclass[fleqn,10pt,legalpaper]article} \usepackageamssymb} \usepackageamsfonts} \usepackageamsmath} \pagestyleempty} \begindocument} \(N\) \enddocument}is the number of genes, and \batchmode \documentclass[fleqn,10pt,legalpaper]article} \usepackageamssymb} \usepackageamsfonts} \usepackageamsmath} \pagestyleempty} \begindocument} \(W\) \enddocument}is a weight factor whose default value is 3. The groups are non-exclusive and may contain genes of opposite (i.e. inversely correlated) regulatory response. The confidence measure is obtained by systematically deleting assays from the data set, interpolating the SVD of the reduced data set to reconstruct the missing assay, and calculating the Pearson correlation between the reconstructed assay and the original data. This confidence measure is applicable when each experimental assay corresponds to a value of parameter that can be interpolated, such as time, dose or concentration. Algorithms for the grouping method and the confidence measure are available in a software application called SVD Microarray ANalysis (SVDMAN). In addition to calculating the SVD for generic analysis, SVDMAN provides a new means for using microarray data to develop hypotheses for gene associations and provides a measure of confidence in the hypotheses, thus extending current SVD research in the area of global gene expression analysis.
Availability: ftp://bpublic.lanl.gov/compbio/software
Contact: brettin@lanl.gov
Supplementary information: http://home.lanl.gov/svdman
* To whom correspondence should be addressed.
概述:我们开发了两种新的基因芯片数据奇异值分解分析(SVD)方法。第一种是基于阈值的方法,用于获取基因组,第二种是用于获取SVD分析信心度的方法。通过识别左奇异向量或基因系数向量中大于阈值\batchmode \documentclass[fleqn,10pt,legalpaper]article} \usepackageamssymb} \usepackageamsfonts} \usepackageamsmath} \pagestyleempty} \begindocument} \(WN^-}1/2}\) \enddocument}的元素来获得基因组,其中\batchmode \documentclass[fleqn,10pt,legalpaper]article} \usepackageamssymb} \usepackageamsfonts} \usepackageamsmath} \pagestyleempty} \begindocument} \(N\) \enddocument}是基因数,\batchmode \documentclass[fleqn,10pt,legalpaper]article} \usepackageamssymb} \usepackageamsfonts} \usepackageamsmath} \pagestyleempty} \begindocument} \(W\) \enddocument}是权重因子,其默认值为3。这些组是非排他性的,可能包含相反(即反相关)的调节响应基因。通过从数据集中系统地删除测定值,将缩小的数据集的SVD插值以重构缺失的测定值,并计算重构测定值与原始数据之间的Pearson相关性来获得信心度度量。当每个实验测定值对应于可以插值的参数值(如时间、剂量或浓度)时,此信心度量适用。分组方法和信心度量的算法可在名为SVD Microarray Analysis(SVDMAN)的软件应用程序中获得。除了计算通用分析的SVD外,SVDMAN还提供了一种新的使用基因芯片数据开发基因关联假设的方法,并提供了假设的信心度量,从而扩展了当前在全局基因表达分析领域的SVD研究。
可用性:ftp://bpublic.lanl.gov/compbio/software
联系人:brettin@lanl.gov
补充信息:http://home.lanl.gov/svDMAn
*应通讯者地址。