Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations.
Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations.
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DOI：
10.1186/1471-2164-5-61
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发表时间：
2004-09-02
期刊：
影响因子：
4.4
通讯作者：
Alsobrook J
中科院分区：
文献类型：
--
作者：
Shippy R;Sendera TJ;Lockner R;Palaniappan C;Kaysser-Kranich T;Watts G;Alsobrook J
尽管微阵列被广泛使用，但在数据分析、解释和不同技术的相关性方面存在许多模糊性。在不同微阵列平台之间获得的相关结果中存在相当大的兴趣。到目前为止，只发表了几项跨平台评价，不幸的是，还没有制定关于进行这种关联的最佳方法的准则。为了解决这个问题，我们对两个商业微阵列平台进行了彻底的评估，以确定一种适当的方法来进行跨平台的相关性。在本研究中，比较了Affyssin U133 A/B GeneChips®和阿默舍姆CodeLink™ UniSet Human 20 K微阵列上唯一代表的10，763个基因的表达测量值。对于每个微阵列平台，根据每个制造商的标准方案，对源自相同总RNA样品的五个技术重复进行标记、杂交和定量。平台之间的10，763个重叠基因的整个集合的差异表达比率的相关系数（r）为0.62。然而，相关性显着改善（r = 0.79）时，噪声内的基因被排除。除了平台间的相关性，我们还评估了每个微阵列平台的精确度、显著性特征、功效和噪声水平。针对25个基因的实时PCR测量差异表达的准确性，并且两个平台均具有良好的相关性，CodeLink和GeneChip的r值分别为0.92和0.79。作为这项研究的结果，我们建议在跨平台相关性中只使用称为“存在”的基因。然而，在这项研究中，由于平台之间的噪声水平不同，大量基因可能会从相关性中丢失。鉴于两个平台的灵敏度明显不同，这是一个重要的考虑因素。来自微阵列分析的数据需要谨慎解释，因此，我们提供了跨平台相关性的指导方针。总之，这项研究代表了迄今为止使用最大的重叠基因集对短寡核苷酸微阵列平台进行的最全面和专门设计的比较。
Despite the widespread use of microarrays, much ambiguity regarding data analysis, interpretation and correlation of the different technologies exists. There is a considerable amount of interest in correlating results obtained between different microarray platforms. To date, only a few cross-platform evaluations have been published and unfortunately, no guidelines have been established on the best methods of making such correlations. To address this issue we conducted a thorough evaluation of two commercial microarray platforms to determine an appropriate methodology for making cross-platform correlations. In this study, expression measurements for 10,763 genes uniquely represented on Affymetrix U133A/B GeneChips® and Amersham CodeLink™ UniSet Human 20 K microarrays were compared. For each microarray platform, five technical replicates, derived from the same total RNA samples, were labeled, hybridized, and quantified according to each manufacturers' standard protocols. The correlation coefficient (r) of differential expression ratios for the entire set of 10,763 overlapping genes was 0.62 between platforms. However, the correlation improved significantly (r = 0.79) when genes within noise were excluded. In addition to levels of inter-platform correlation, we evaluated precision, statistical-significance profiles, power, and noise levels for each microarray platform. Accuracy of differential expression was measured against real-time PCR for 25 genes and both platforms correlated well with r values of 0.92 and 0.79 for CodeLink and GeneChip, respectively. As a result of this study, we recommend using only genes called 'present' in cross-platform correlations. However, as in this study, a large number of genes may be lost from the correlation due to differing levels of noise between platforms. This is an important consideration given the apparent difference in sensitivity of the two platforms. Data from microarray analysis need to be interpreted cautiously and therefore, we provide guidelines for making cross-platform correlations. In all, this study represents the most comprehensive and specifically designed comparison of short-oligonucleotide microarray platforms to date using the largest set of overlapping 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：
7
作者：
Barczak, A;Rodriguez, MW;Erle, DJ
通讯作者：
Erle, DJ
影响因子：
3.8
作者：
Li, J;Pankratz, M;Johnson, JA
通讯作者：
Johnson, JA
影响因子：
3.4
作者：
Livshits, MA;Mirzabekov, AD
通讯作者：
Mirzabekov, AD
影响因子：
14.9
作者：
Relógio, A;Schwager, C;Valcárcel, J
通讯作者：
Valcárcel, J