A weighted average difference method for detecting differentially expressed genes from microarray data.
A weighted average difference method for detecting differentially expressed genes from microarray data.
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从微阵列数据中检测出差异表达基因的加权平均差异方法。
DOI：
10.1186/1748-7188-3-8
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发表时间：
2008-06-26
影响因子：
1
通讯作者：
Shimizu, Kentaro
中科院分区：
文献类型：
--
作者：
Kadota, Koji;Nakai, Yuji;Shimizu, Kentaro
在不同的实验条件下鉴定差异表达基因（DEGs）是许多微阵列研究的重要任务。然而，为特定的应用程序选择使用哪种方法是有问题的，因为它的性能取决于评估度量、数据集等等。此外，当使用Affymetrix GeneChip®系统时，研究人员必须从许多竞争算法（如MAS， RMA和DFW）中选择预处理算法，以获得表达水平测量。对于给定的探针级数据集，需要选择合适的基因选择方法和预处理算法的组合，以获得最佳的基因检测性能。我们引入了一种新的基于折叠变化（FC）的方法——加权平均差分法（WAD）来对基因进行排序。它使用平均差异和相对平均信号强度，使高表达基因在不同条件下的平均排名较高。这个想法是基于我们的观察，即已知或潜在的标记基因（或蛋白质）往往具有高表达水平。我们将WAD与其他7种方法进行了比较；平均差（AD）、FC、等级产品（RP）、调节t统计量（modT）、微阵列显著性分析（samT）、收缩t统计量（shrinkT）和基于强度的调节t统计量（ibmT）。评估使用总共38个不同的二进制（两类）探针级数据集进行：两个人工“峰值”数据集和36个真实的实验数据集。结果表明，当同时考虑灵敏度和特异性时，WAD优于其他方法：在38个数据集中，WAD的受试者工作特征曲线下面积平均最高。当比较从三种不同的预处理数据（MAS， RMA和DFW）中产生的排名最高的基因子集时，WAD的基因排名也是最一致的。总的来说，WAD对mas预处理的数据表现最好，而基于FC的方法（AD、WAD、FC或RP）对RMA和dfw预处理的数据表现良好。WAD是一种很有希望的替代现有方法，用于对两个类的deg进行排序。它的高性能应该增加研究人员对微阵列分析的信心。
Identification of differentially expressed genes (DEGs) under different experimental conditions is an important task in many microarray studies. However, choosing which method to use for a particular application is problematic because its performance depends on the evaluation metric, the dataset, and so on. In addition, when using the Affymetrix GeneChip® system, researchers must select a preprocessing algorithm from a number of competing algorithms such as MAS, RMA, and DFW, for obtaining expression-level measurements. To achieve optimal performance for detecting DEGs, a suitable combination of gene selection method and preprocessing algorithm needs to be selected for a given probe-level dataset. We introduce a new fold-change (FC)-based method, the weighted average difference method (WAD), for ranking DEGs. It uses the average difference and relative average signal intensity so that highly expressed genes are highly ranked on the average for the different conditions. The idea is based on our observation that known or potential marker genes (or proteins) tend to have high expression levels. We compared WAD with seven other methods; average difference (AD), FC, rank products (RP), moderated t statistic (modT), significance analysis of microarrays (samT), shrinkage t statistic (shrinkT), and intensity-based moderated t statistic (ibmT). The evaluation was performed using a total of 38 different binary (two-class) probe-level datasets: two artificial "spike-in" datasets and 36 real experimental datasets. The results indicate that WAD outperforms the other methods when sensitivity and specificity are considered simultaneously: the area under the receiver operating characteristic curve for WAD was the highest on average for the 38 datasets. The gene ranking for WAD was also the most consistent when subsets of top-ranked genes produced from three different preprocessed data (MAS, RMA, and DFW) were compared. Overall, WAD performed the best for MAS-preprocessed data and the FC-based methods (AD, WAD, FC, or RP) performed well for RMA and DFW-preprocessed data. WAD is a promising alternative to existing methods for ranking DEGs with two classes. Its high performance should increase researchers' confidence in microarray analyses.
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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
DOI：
10.1142/s0219720005001442
发表时间：
2005-10-01
影响因子：
1
作者：
Breitling, Rainer;Herzyk, Pawel
通讯作者：
Herzyk, Pawel
影响因子：
1.9
作者：
Gottardo, R;Raftery, AE;Bumgarner, RE
通讯作者：
Bumgarner, RE
影响因子：
20.1
作者：
Barth, AS;Merk, S;Nabauer, M
通讯作者：
Nabauer, M
影响因子：
5.8
作者：
Cope, LM;Irizarry, RA;Speed, TP
通讯作者：
Speed, TP