Methods for evaluating gene expression from Affymetrix microarray datasets.
Methods for evaluating gene expression from Affymetrix microarray datasets.
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评估 Affymetrix 微阵列数据集基因表达的方法
DOI：
10.1186/1471-2105-9-284
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发表时间：
2008-06-17
影响因子：
3
通讯作者：
Luo ZW
中科院分区：
文献类型：
--
作者：
Jiang N;Leach LJ;Hu X;Potokina E;Jia T;Druka A;Waugh R;Kearsey MJ;Luo ZW
Affymetrix 高密度寡核苷酸表达阵列广泛应用于生物学研究的所有领域，用于测量全基因组基因表达。处理寡核苷酸微阵列数据的一个重要步骤是使用越来越多的统计方法之一产生 RNA 转录本的基因表达水平的单一值。研究人员面临的挑战是决定使用给定数据集解决特定生物学问题的最合适方法。尽管一些研究工作集中于评估几种方法在评估不同数据集的 RNA 杂交实验中的基因表达方面的性能，但目前文献中可用的用于评估从真实生物实验收集的 Affymetrix 微阵列数据的全基因组基因表达的方法的相对优点仍然存在激烈争论。本研究报告了对使用 Affymetrix 微阵列精心设计的实验评估全基因组基因表达的所有七种常用方法的性能的全面调查。该实验分析了八个遗传上不同的大麦品种，每个品种都有三个生物学重复。如此获得的数据集为当前的分析提供了平衡和理想化的结构。评估了这些方法检测差异表达基因的灵敏度、重复表达值的再现性以及调用差异表达基因的一致性。在给定的错误发现率 (FDR) 水平下，不同方法中检测到差异表达的基因数量相差两倍或更多。此外，我们建议使用包含单特征多态性（SFP）的基因作为经验测试，以比较检测真正差异基因表达的方法的能力，因为SFP很大程度上对应于顺式作用表达调节剂。 PDNN 方法在每次比较中都表现出优于所有其他方法的优越性，而默认的 Affymetrix MAS5.0 方法则明显较差。基于广泛的大麦 Affymetrix 基因表达数据集对七种常用的数据提取方法进行综合评估表明，PDNN 方法对于差异表达基因的检测具有优越的性能。
Affymetrix high density oligonucleotide expression arrays are widely used across all fields of biological research for measuring genome-wide gene expression. An important step in processing oligonucleotide microarray data is to produce a single value for the gene expression level of an RNA transcript using one of a growing number of statistical methods. The challenge for the researcher is to decide on the most appropriate method to use to address a specific biological question with a given dataset. Although several research efforts have focused on assessing performance of a few methods in evaluating gene expression from RNA hybridization experiments with different datasets, the relative merits of the methods currently available in the literature for evaluating genome-wide gene expression from Affymetrix microarray data collected from real biological experiments remain actively debated. The present study reports a comprehensive survey of the performance of all seven commonly used methods in evaluating genome-wide gene expression from a well-designed experiment using Affymetrix microarrays. The experiment profiled eight genetically divergent barley cultivars each with three biological replicates. The dataset so obtained confers a balanced and idealized structure for the present analysis. The methods were evaluated on their sensitivity for detecting differentially expressed genes, reproducibility of expression values across replicates, and consistency in calling differentially expressed genes. The number of genes detected as differentially expressed among methods differed by a factor of two or more at a given false discovery rate (FDR) level. Moreover, we propose the use of genes containing single feature polymorphisms (SFPs) as an empirical test for comparison among methods for the ability to detect true differential gene expression on the basis that SFPs largely correspond to cis-acting expression regulators. The PDNN method demonstrated superiority over all other methods in every comparison, whilst the default Affymetrix MAS5.0 method was clearly inferior. A comprehensive assessment of seven commonly used data extraction methods based on an extensive barley Affymetrix gene expression dataset has shown that the PDNN method has superior performance for the detection of differentially expressed genes.
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DOI：
10.1073/pnas.011404098
发表时间：
2001-01-02
影响因子：
11.1
作者：
Li, C;Wong, WH
通讯作者：
Wong, WH
影响因子：
3.9
作者：
PEREZROGER, I;GARCIASOGO, M;ARMENGOD, ME
通讯作者：
ARMENGOD, ME
芽殖酵母酿酒酵母乙醇耐受性的遗传解析
影响因子：
3.3
作者：
Hu, X. H.;Wang, M. H.;Luo, Z. W.
通讯作者：
Luo, Z. W.
影响因子：
5.8
作者：
Cope, LM;Irizarry, RA;Speed, TP
通讯作者：
Speed, TP
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y