Down-weighting overlapping genes improves gene set analysis.
Down-weighting overlapping genes improves gene set analysis.
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DOI：
10.1186/1471-2105-13-136
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发表时间：
2012-06-19
影响因子：
3
通讯作者：
Romero R
中科院分区：
文献类型：
--
作者：
Tarca AL;Draghici S;Bhatti G;Romero R
基于微阵列数据识别在给定条件下受到显著影响的基因集是当前生命科学研究中的关键步骤。大多数基因集分析方法平等地对待基因，不管它们对给定基因集的特异性如何。在这项工作中，我们提出了一种新的基因集分析方法，计算基因集得分的平均值的绝对值的加权调节基因的t分数。基因权重被设计为强调出现在少数基因集中的基因，而不是出现在许多基因集中的基因。我们证明了该方法在分析对应于KEGG通路的基因集时的有用性，因此我们称我们的方法为重叠基因加权下的通路分析（PADOG）。与大多数基因集分析方法不同，这些方法通过分析2-3个数据集，然后对结果进行人工解释来验证，这里采用的验证使用24个不同的数据集和完全客观的评估方案，该方案做出最少的假设，并消除了对分析结果可能有偏见的人工评估的需要。PADOG使用基因表达谱和待分析的基因组集合中已有的信息显著改善了基因组排序并提高了分析的灵敏度。PADOG优于其他现有方法的优点被证明是稳定的基因集的数据库中的变化进行分析。PADOG作为R包实现，可在http://bioinformaticsprb.med.wayne.edu/PADOG/or http://www.bioconductor.org上获得。
The identification of gene sets that are significantly impacted in a given condition based on microarray data is a crucial step in current life science research. Most gene set analysis methods treat genes equally, regardless how specific they are to a given gene set. In this work we propose a new gene set analysis method that computes a gene set score as the mean of absolute values of weighted moderated gene t-scores. The gene weights are designed to emphasize the genes appearing in few gene sets, versus genes that appear in many gene sets. We demonstrate the usefulness of the method when analyzing gene sets that correspond to the KEGG pathways, and hence we called our method Pathway Analysis with Down-weighting of Overlapping Genes (PADOG). Unlike most gene set analysis methods which are validated through the analysis of 2-3 data sets followed by a human interpretation of the results, the validation employed here uses 24 different data sets and a completely objective assessment scheme that makes minimal assumptions and eliminates the need for possibly biased human assessments of the analysis results. PADOG significantly improves gene set ranking and boosts sensitivity of analysis using information already available in the gene expression profiles and the collection of gene sets to be analyzed. The advantages of PADOG over other existing approaches are shown to be stable to changes in the database of gene sets to be analyzed. PADOG was implemented as an R package available at: http://bioinformaticsprb.med.wayne.edu/PADOG/or http://www.bioconductor.org.
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影响因子：
--
作者：
Massa MS;Chiogna M;Romualdi C
通讯作者：
Romualdi C
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：
24
作者：
Barth, Andreas S.;Kuner, Ruprecht;Sueltmann, Holger
通讯作者：
Sueltmann, Holger
影响因子：
3
作者：
Ackermann, Marit;Strimmer, Korbinian
通讯作者：
Strimmer, Korbinian
影响因子：
11.5
作者：
Hong, Yi;Ho, Kok Sun;Cheah, Peh Yean
通讯作者：
Cheah, Peh Yean