Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient.
Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient.
复制标题
使用收缩相关系数对复制微阵列进行基因组规模群集分析。
DOI：
10.1186/1471-2105-9-288
复制
发表时间：
2008-06-18
影响因子：
3
通讯作者：
Roux, Stanley J.
中科院分区：
文献类型：
--
作者：
Yao, Jianchao;Chang, Chunqi;Salmi, Mari L.;Hung, Yeung Sam;Loraine, Ann;Roux, Stanley J.
目前，聚类与某种形式的相关系数作为基因相似性度量已成为一种流行的方法来分析基因组数据。Pearson相关系数和标准差（SD）加权相关系数是两个最广泛使用的相关性作为聚类微阵列数据的相似性度量。然而，这两个相关性不是最佳的分析复制的微阵列数据产生的大多数实验室。需要有效的相关系数来提供对重复的微阵列数据的统计学上充分的分析。在这项研究中，我们描述了一种新的相关系数，收缩相关系数（SCC），充分利用复制的微阵列实验样本之间的相似性。该方法同时考虑了重复的数量和每个实验组内的方差，在聚类表达数据，并提供了一个强大的统计估计的错误复制的微阵列数据。SCC的值通过与目前最广泛使用的其他两个相关系数（Pearson相关系数和SD加权相关系数）的比较来揭示，所述相关系数使用对来自酿酒酵母的合成表达数据以及真实的基因表达数据的统计测量。两个领先的聚类方法，层次和k-均值聚类进行了比较。比较表明，使用SCC实现更好的聚类性能。应用基于SCC的层次聚类从蕨类植物Ceratopteris richardii的萌发孢子中获得的复制的微阵列数据，我们发现了两个簇的基因在孢子萌发过程中具有共享的表达模式。功能分析表明，在苔藓和被子植物等不同植物谱系中控制萌发的一些遗传机制在蕨类植物中也是保守的。这项研究表明，SCC是一种替代皮尔逊相关系数和SD加权相关系数，是特别有用的聚类重复的微阵列数据。这种计算方法通常对蛋白质组学数据或其他高通量分析方法有用。
Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. This study shows that SCC is an alternative to the Pearson correlation coefficient and the SD-weighted correlation coefficient, and is particularly useful for clustering replicated microarray data. This computational approach should be generally useful for proteomic data or other high-throughput analysis methodology.
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影响因子：
3
作者：
Killion PJ;Sherlock G;Iyer VR
通讯作者：
Iyer VR
斯坦福微阵列数据库：新分析工具的实现和软件的开源发布。
影响因子：
14.9
作者：
Demeter, Janos;Beauheim, Catherine;Gollub, Jeremy;Hernandez-Boussard, Tina;Jin, Heng;Maier, Donald;Matese, John C.;Nitzberg, Michael;Wymore, Farrell;Zachariah, Zachariah K.;Brown, Patrick O.;Sherlock, Gavin;Ball, Catherine A.
通讯作者：
Ball, Catherine A.
DOI：
10.1073/pnas.150242097
发表时间：
2000-07-18
影响因子：
11.1
作者：
Holter, NS;Mitra, M;Fedoroff, NV
通讯作者：
Fedoroff, NV
DOI：
10.1073/pnas.97.18.10101
发表时间：
2000-08-29
影响因子：
11.1
作者：
Alter, O;Brown, PO;Botstein, D
通讯作者：
Botstein, D
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y