Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data.
Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data.
复制标题
微阵列数据的可重复性：对微阵列质量控制（MAQC）数据的进一步分析。
DOI：
10.1186/1471-2105-8-412
复制
发表时间：
2007-10-25
影响因子：
3
通讯作者：
Tsai CA
中科院分区：
文献类型：
--
作者：
Chen JJ;Hsueh HM;Delongchamp RR;Lin CJ;Tsai CA
基因表达谱芯片数据的可比性和可靠性一直是研究者关注的问题。最近完成的微阵列质量控制（MAQC）项目提供了一个独特的机会，以评估跨多个站点的再现性和跨多个平台的可比性。为得出微阵列基因表达测量的平台间和平台内可比性/再现性的结论而提出的MAQC分析是不充分的。我们评估的再现性/可比性的MAQC数据的12901个共同的基因在四个滴定样品产生的5个高密度单色微阵列平台和TaqMan技术。我们讨论了使用相关系数作为度量来评估平台间和平台内的再现性以及重叠基因（POG）的百分比作为MAQC评估基因选择程序的度量的一些问题。在平台内和平台间分析中共使用了293个阵列。层次聚类分析表明，在五个平台之间的测量强度的显着差异。许多基因在一个平台中显示出小的倍数变化，而在另一个平台中显示出大的倍数变化，即使平台之间的相关性很高。方差分析显示，样本中30%的基因表达在五个平台上显示出不一致的模式。我们说明，POG不反映所选基因列表的准确性。非重叠基因可以用严格切割真正差异表达，重叠基因可以用非严格截止无差异表达。此外，POG是一个不可用的选择标准。POG可以随着截止值的变化而不规则地增加或减少;没有标准来确定截止值，以便POG被优化。利用各种统计方法，我们证明了不同平台和平台内不同地点测得的强度存在差异。在每个平台内，表达模式通常是一致的，但存在站点间的差异。用于监管决策的数据分析方法的评价不应考虑治疗效应，当不存在治疗效应时，“具有非严格p值临界值的倍数变化临界值”可能导致100%假阳性错误选择。
Many researchers are concerned with the comparability and reliability of microarray gene expression data. Recent completion of the MicroArray Quality Control (MAQC) project provides a unique opportunity to assess reproducibility across multiple sites and the comparability across multiple platforms. The MAQC analysis presented for the conclusion of inter- and intra-platform comparability/reproducibility of microarray gene expression measurements is inadequate. We evaluate the reproducibility/comparability of the MAQC data for 12901 common genes in four titration samples generated from five high-density one-color microarray platforms and the TaqMan technology. We discuss some of the problems with the use of correlation coefficient as metric to evaluate the inter- and intra-platform reproducibility and the percent of overlapping genes (POG) as a measure for evaluation of a gene selection procedure by MAQC. A total of 293 arrays were used in the intra- and inter-platform analysis. A hierarchical cluster analysis shows distinct differences in the measured intensities among the five platforms. A number of genes show a small fold-change in one platform and a large fold-change in another platform, even though the correlations between platforms are high. An analysis of variance shows thirty percent of gene expressions of the samples show inconsistent patterns across the five platforms. We illustrated that POG does not reflect the accuracy of a selected gene list. A non-overlapping gene can be truly differentially expressed with a stringent cut, and an overlapping gene can be non-differentially expressed with non-stringent cutoff. In addition, POG is an unusable selection criterion. POG can increase or decrease irregularly as cutoff changes; there is no criterion to determine a cutoff so that POG is optimized. Using various statistical methods we demonstrate that there are differences in the intensities measured by different platforms and different sites within platform. Within each platform, the patterns of expression are generally consistent, but there is site-by-site variability. Evaluation of data analysis methods for use in regulatory decision should take no treatment effect into consideration, when there is no treatment effect, "a fold-change cutoff with a non-stringent p-value cutoff" could result in 100% false positive error selection.
登录
查看更多内容
影响因子：
46.9
作者：
Canales, Roger D.;Luo, Yuling;Goodsaid, Federico M.
通讯作者：
Goodsaid, Federico M.
影响因子：
48
作者：
Bammler, T;Beyer, RP;Zarbl, H
通讯作者：
Zarbl, H
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
影响因子：
48
作者：
Larkin, JE;Frank, BC;Quackenbush, J
通讯作者：
Quackenbush, J
影响因子：
48
作者：
Irizarry, RA;Warren, D;Yu, W
通讯作者：
Yu, W