CoXpress: differential co-expression in gene expression data.
CoXpress: differential co-expression in gene expression data.
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DOI：
10.1186/1471-2105-7-509
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发表时间：
2006-11-20
影响因子：
3
通讯作者：
Watson M
中科院分区：
文献类型：
--
作者：
Watson M
分析基因表达数据的传统方法通常包括一个统计检验来发现差异表达的基因，或者使用聚类算法来发现数据集中行为相似的基因组。然而，这些方法可能会遗漏在不同实验条件下形成差异共表达模式的基因组。在这里，我们描述了一个R包，它允许研究人员识别差异共表达的基因组。我们已经开发出了一种识别不同共表达基因组的方法。使用两个公开可用的微阵列数据集演示了express的实用性。我们的软件识别出几组基因，这些基因在一组生物相关实验中高度相关，但在另一组实验中却几乎没有相关性。该软件使用重新采样方法来计算每组的p值，并提供几种方法来可视化差异共表达基因。coXpress可用于在微阵列数据集中发现显示差异共表达模式的基因组。
Traditional methods of analysing gene expression data often include a statistical test to find differentially expressed genes, or use of a clustering algorithm to find groups of genes that behave similarly across a dataset. However, these methods may miss groups of genes which form differential co-expression patterns under different subsets of experimental conditions. Here we describe coXpress, an R package that allows researchers to identify groups of genes that are differentially co-expressed. We have developed coXpress as a means of identifying groups of genes that are differentially co-expressed. The utility of coXpress is demonstrated using two publicly available microarray datasets. Our software identifies several groups of genes that are highly correlated under one set of biologically related experiments, but which show little or no correlation in a second set of experiments. The software uses a re-sampling method to calculate a p-value for each group, and provides several methods for the visualisation of differentially co-expressed genes. coXpress can be used to find groups of genes that display differential co-expression patterns in microarray datasets.
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影响因子：
7
作者：
Lee, HK;Hsu, AK;Pavlidis, P
通讯作者：
Pavlidis, P
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
DOI：
10.1073/pnas.252466999
发表时间：
2002-12-24
影响因子：
11.1
作者：
Li, KC
通讯作者：
Li, KC
影响因子：
3.3
作者：
Spellman, PT;Sherlock, G;Futcher, B
通讯作者：
Futcher, B