Assessing affymetrix GeneChip microarray quality.
Assessing affymetrix GeneChip microarray quality.
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DOI：
10.1186/1471-2105-12-137
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发表时间：
2011-05-07
影响因子：
3
通讯作者：
Irizarry RA
中科院分区：
文献类型：
--
作者：
McCall MN;Murakami PN;Lukk M;Huber W;Irizarry RA
微阵列技术已成为生物科学中广泛使用的工具。在过去的十年中，用户数量呈指数级增长，随着应用程序和二级数据分析的数量迅速增加，我们预计这一速度将继续下去。各种倡议，如外部RNA控制联盟（ERCC）和微阵列质量控制（MAQC）项目，探索了为该技术提供标准的方法。微阵列成为公认的可靠技术，质量评估的统计方法将是一个不可或缺的组成部分，然而，仍然缺乏共识，在定义和测量微阵列的质量。我们开始提供一个精确的定义，微阵列的质量和审查现有的AffytechnologyGeneChip的质量指标，根据这一定义。我们表明，性能最好的指标需要多个阵列同时进行评估。虽然这样的多阵列质量度量对于实验室科学是足够的，但是随着微阵列开始用于临床环境，单阵列质量度量将是不可缺少的。为此，我们定义了一个最好的多阵列质量指标之一的单阵列版本，并表明该指标的性能以及最好的多阵列指标。然后，我们使用这个新的质量指标来评估通过基因表达综合数据库（GEO）获得的微阵列数据的质量，该数据库使用了来自809项研究的22，000多个Affytek HGU133a和HGU133plus2阵列。我们发现，这些公开可用的阵列中约有10%的质量较差。此外，微阵列测量的质量在不同的杂交、不同的研究和不同的实验室之间变化很大，一些实验产生了不可用的数据。这里描述的许多概念适用于其他高通量技术。
Microarray technology has become a widely used tool in the biological sciences. Over the past decade, the number of users has grown exponentially, and with the number of applications and secondary data analyses rapidly increasing, we expect this rate to continue. Various initiatives such as the External RNA Control Consortium (ERCC) and the MicroArray Quality Control (MAQC) project have explored ways to provide standards for the technology. For microarrays to become generally accepted as a reliable technology, statistical methods for assessing quality will be an indispensable component; however, there remains a lack of consensus in both defining and measuring microarray quality. We begin by providing a precise definition of microarray quality and reviewing existing Affymetrix GeneChip quality metrics in light of this definition. We show that the best-performing metrics require multiple arrays to be assessed simultaneously. While such multi-array quality metrics are adequate for bench science, as microarrays begin to be used in clinical settings, single-array quality metrics will be indispensable. To this end, we define a single-array version of one of the best multi-array quality metrics and show that this metric performs as well as the best multi-array metrics. We then use this new quality metric to assess the quality of microarry data available via the Gene Expression Omnibus (GEO) using more than 22,000 Affymetrix HGU133a and HGU133plus2 arrays from 809 studies. We find that approximately 10 percent of these publicly available arrays are of poor quality. Moreover, the quality of microarray measurements varies greatly from hybridization to hybridization, study to study, and lab to lab, with some experiments producing unusable data. Many of the concepts described here are applicable to other high-throughput technologies.
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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
影响因子：
14.9
作者：
Parkinson H;Kapushesky M;Kolesnikov N;Rustici G;Shojatalab M;Abeygunawardena N;Berube H;Dylag M;Emam I;Farne A;Holloway E;Lukk M;Malone J;Mani R;Pilicheva E;Rayner TF;Rezwan F;Sharma A;Williams E;Bradley XZ;Adamusiak T;Brandizi M;Burdett T;Coulson R;Krestyaninova M;Kurnosov P;Maguire E;Neogi SG;Rocca-Serra P;Sansone SA;Sklyar N;Zhao M;Sarkans U;Brazma A
通讯作者：
Brazma A
DOI：
10.1073/pnas.012025199
发表时间：
2002-04-02
影响因子：
11.1
作者：
Su, AI;Cooke, MP;Hogenesch, JB
通讯作者：
Hogenesch, JB
影响因子：
2.1
作者：
Irizarry, RA;Hobbs, B;Speed, TP
通讯作者：
Speed, TP
影响因子：
14.9
作者：
McCall MN;Uppal K;Jaffee HA;Zilliox MJ;Irizarry RA
通讯作者：
Irizarry RA