Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data
Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data
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DOI：
10.1186/1471-2105-7-359
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发表时间：
2006-07-26
影响因子：
3
通讯作者：
Culhane, Aedin C.
中科院分区：
文献类型：
--
作者：
Jeffery, Ian B.;Higgins, Desmond G.;Culhane, Aedin C.
背景：许多特征选择方法已被应用于微阵列数据中差异表达基因的识别。其中包括简单倍数变化、经典t统计量和适度t统计量。尽管这些方法返回的基因列表通常是不相似的，但很少有直接比较。我们提出了一个实证研究中，我们比较了一些最常用的特征选择方法。我们将这些方法应用于9个公开可用的数据集，并比较了产生的基因列表以及它们在测试数据集的类预测中的表现。微阵列显著性分析（SAM）、方差分析（ANOVA）、经验贝叶斯t统计、模板匹配、maxT、组间分析（BGA），受试者工作特征（ROC）曲线下面积、Welch t统计量、倍数变化、秩积和随机选择的基因集。在每种情况下，这些方法应用于9个不同的二进制（两类）微阵列数据集。首先，我们发现不同方法产生的基因列表几乎没有一致性。在所有10种特征选择方法中，只有8%到21%的基因是共同的。其次，我们评估了类预测效率的每个基因列表中的训练和测试交叉验证使用四个监督classifiers.Conclusion：我们报告说，选择的特征选择方法，基因的基因的数量，在generative的情况下（样本）和噪声的数据集，大大影响分类成功。对特征选择的选择提出了建议。ROC曲线下面积在具有低噪声水平和大样本量的数据集上表现良好。当数据集的样本数较低或噪声水平较高时，秩产品表现良好。经验贝叶斯t统计量在一系列样本量中表现良好。
Background: Numerous feature selection methods have been applied to the identification of differentially expressed genes in microarray data. These include simple fold change, classical t-statistic and moderated t-statistics. Even though these methods return gene lists that are often dissimilar, few direct comparisons of these exist. We present an empirical study in which we compare some of the most commonly used feature selection methods. We apply these to 9 publicly available datasets, and compare, both the gene lists produced and how these perform in class prediction of test datasets.Results: In this study, we compared the efficiency of the feature selection methods; significance analysis of microarrays (SAM), analysis of variance (ANOVA), empirical bayes t-statistic, template matching, maxT, between group analysis (BGA), Area under the receiver operating characteristic (ROC) curve, the Welch t-statistic, fold change, rank products, and sets of randomly selected genes. In each case these methods were applied to 9 different binary ( two class) microarray datasets. Firstly we found little agreement in gene lists produced by the different methods. Only 8 to 21% of genes were in common across all 10 feature selection methods. Secondly, we evaluated the class prediction efficiency of each gene list in training and test cross-validation using four supervised classifiers.Conclusion: We report that the choice of feature selection method, the number of genes in the genelist, the number of cases (samples) and the noise in the dataset, substantially influence classification success. Recommendations are made for choice of feature selection. Area under a ROC curve performed well with datasets that had low levels of noise and large sample size. Rank products performs well when datasets had low numbers of samples or high levels of noise. The Empirical bayes t-statistic performed well across a range of sample sizes.