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Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations

机译:商业短寡核苷酸微阵列的性能评估以及噪声对跨平台关联的影响

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摘要

BACKGROUND: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.RESULTS:In this study, expression measurements for 10,763 genes uniquely represented on Affymetrix U133A/B GeneChips(R) and Amersham CodeLinkTM 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.CONCLUSIONS: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.
机译:背景:尽管微阵列的广泛使用,但在不同技术的数据分析,解释和相关性方面存在很多歧义。在不同的微阵列平台之间获得的相关结果有相当大的兴趣。迄今为止,仅发表了几项跨平台评估,但不幸的是,尚未建立关于建立这种关联的最佳方法的指南。为了解决这个问题,我们对两个商业微阵列平台进行了全面评估,以确定合适的方法进行跨平台关联。结果:在这项研究中,Affymetrix U133A / B GeneChips(R)和Amersham上唯一代表的10,763个基因的表达测量比较了CodeLinkTM UniSet Human 20 K人类微阵列。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。在整个平台之间,整个10,763个重叠基因的差异表达比的相关系数(r)为0.62。但是,当排除噪声中的基因时,相关性显着提高(r = 0.79)。除了平台间的相关性水平外,我们还评估了每个微阵列平台的精度,统计显着性概况,功率和噪声水平。通过实时PCR测定了25个基因的差异表达的准确性,两个平台的CodeLink和GeneChip的r值分别为0.92和0.79。结论:结论:作为这项研究的结果,我们建议仅使用称为'present'的基因。跨平台关联。但是,如本研究中所示,由于平台之间的噪声水平不同,因此可能会从关联中丢失大量基因。考虑到两个平台在灵敏度上的明显差异,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,我们提供了进行跨平台关联的指南。总而言之,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,最专门设计的比较。

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