sdef: an R package to synthesize lists of significant features in related experiments.
sdef: an R package to synthesize lists of significant features in related experiments.
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DOI：
10.1186/1471-2105-11-270
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发表时间：
2010-05-20
影响因子：
3
通讯作者：
Richardson S
中科院分区：
文献类型：
--
作者：
Blangiardo M;Cassese A;Richardson S
在微阵列研究中，研究人员通常对两个或多个类似实验之间的相关数量比较感兴趣，这些实验涉及不同的处理方法、组织或物种。通常，每个实验都会报告显著性度量（如p值）或其他度量（如基因）对其特征进行排序。我们的目标是找到一个在所有实验中重要的特征列表，以进一步研究。在本文中，我们提出了一个名为sdef的R包，它允许用户使用先前提出的基于p值排名列表的统计方法来量化实验之间的共同性证据。Sdef实现了两种方法来实现这一目标：第一种是在实验之间独立的假设下，对观察到的共同特征与期望的共同特征的最大比率进行排列检验。第二种方法采用贝叶斯框架，由于考虑到每次实验中差异表达基因数量的不确定性，因此更为灵活。我们使用sdef重新分析公开可用的数据i) 2型糖尿病对小鼠肝脏和骨骼肌的易感性（两个实验）；Ii)哺乳动物性别之间的分子相似性（三个实验）。对于第一个例子，我们使用上述两种方法发现了68到104个基因在两种组织之间普遍受到干扰，并丰富了与肥胖和糖尿病相关的炎症途径。对于第二个例子，观察三个特征列表，我们发现110个基因在三个组织之间普遍受到干扰，使用相同的两种方法，并富集与细胞发育有关的基因。sdef是一个R包，它为研究人员提供了一种简单而强大的方法来查找在两个或多个实验中通常受到干扰的特征列表，以便进一步研究。该软件包提供了图表和表格，以帮助用户可视化和解释结果。该软件包的Windows、Linux和MacOS版本及其文档可在http://cran.r-project.org/web/packages/sdef/index.html网站上获得。
In microarray studies researchers are often interested in the comparison of relevant quantities between two or more similar experiments, involving different treatments, tissues, or species. Typically each experiment reports measures of significance (e.g. p-values) or other measures that rank its features (e.g genes). Our objective is to find a list of features that are significant in all experiments, to be further investigated. In this paper we present an R package called sdef, that allows the user to quantify the evidence of communality between the experiments using previously proposed statistical methods based on the ranked lists of p-values. sdef implements two approaches that address this objective: the first is a permutation test of the maximal ratio of observed to expected common features under the hypothesis of independence between the experiments. The second approach, set in a Bayesian framework, is more flexible as it takes into account the uncertainty on the number of genes differentially expressed in each experiment. We used sdef to re-analyze publicly available data i) on Type 2 diabetes susceptibility in mice on liver and skeletal muscle (two experiments); ii) on molecular similarities between mammalian sexes (three experiments). For the first example, we found between 68 and 104 genes commonly perturbed between the two tissues, using the two methods described above, and enrichment of the inflammation pathways, which are related to obesity and diabetes. For the second example, looking at three lists of features, we found 110 genes commonly perturbed between the three tissues, using the same two methods, and enrichment on genes involved in cell development. sdef is an R package that provides researchers with an easy and powerful methodology to find lists of features commonly perturbed in two or more experiments to be further investigated. The package is provided with plots and tables to help the user visualize and interpret the results. The Windows, Linux and MacOS versions of the package, together with the documentation are available on the website http://cran.r-project.org/web/packages/sdef/index.html.
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