Improving the statistical detection of regulated genes from microarray data using intensity-based variance estimation.
Improving the statistical detection of regulated genes from microarray data using intensity-based variance estimation.
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DOI：
10.1186/1471-2164-5-17
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发表时间：
2004-02-27
期刊：
影响因子：
4.4
通讯作者：
García-Cardeña G
中科院分区：
文献类型：
--
作者：
Comander J;Natarajan S;Gimbrone MA Jr;García-Cardeña G
基因微阵列技术提供了同时研究数千个基因调控的能力，但它的潜力是有限的，没有对观察到的基因表达变化的统计意义进行估计。由于被检测的基因数量很大，而阵列重复的数量相对较少（例如，N = 3），标准的统计方法，如Student's t检验，无法产生可靠的结果。另外两种通常用于改善显著性估计的统计方法是惩罚t检验和使用强度相关方差估计的z检验。使用23个重复的数据集比较了这些方法的性能，并引入了z检验的新实现，将具有相似最小强度的基因的方差估计汇集在一起。使用每种统计技术计算基于3个重复阵列的显著性估计，并通过将其与基于其余20个重复的可靠估计进行比较来评估其准确性。通过将每个测试统计量应用于由3个重复数组组成的多个独立集来评估其可重复性。使用强度相关方差的z检验的两个实现比使用惩罚t检验的两个实现产生更可重复的结果。此外，基于最小强度的z统计量显示出比所有其他测试的统计技术更高的精度和更高或相同的精度。一种基于强度的方差估计技术提供了一种简单有效的方法，可以改善来自复制微阵列数据集的差异调节基因的p值估计。z测试算法的实现可在。
Gene microarray technology provides the ability to study the regulation of thousands of genes simultaneously, but its potential is limited without an estimate of the statistical significance of the observed changes in gene expression. Due to the large number of genes being tested and the comparatively small number of array replicates (e.g., N = 3), standard statistical methods such as the Student's t-test fail to produce reliable results. Two other statistical approaches commonly used to improve significance estimates are a penalized t-test and a Z-test using intensity-dependent variance estimates. The performance of these approaches is compared using a dataset of 23 replicates, and a new implementation of the Z-test is introduced that pools together variance estimates of genes with similar minimum intensity. Significance estimates based on 3 replicate arrays are calculated using each statistical technique, and their accuracy is evaluated by comparing them to a reliable estimate based on the remaining 20 replicates. The reproducibility of each test statistic is evaluated by applying it to multiple, independent sets of 3 replicate arrays. Two implementations of a Z-test using intensity-dependent variance produce more reproducible results than two implementations of a penalized t-test. Furthermore, the minimum intensity-based Z-statistic demonstrates higher accuracy and higher or equal precision than all other statistical techniques tested. An intensity-based variance estimation technique provides one simple, effective approach that can improve p-value estimates for differentially regulated genes derived from replicated microarray datasets. Implementations of the Z-test algorithms are available at .
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