Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models.
Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models.
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DOI：
10.1186/1471-2105-6-128
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发表时间：
2005-05-27
影响因子：
3
通讯作者：
Beyene J
中科院分区：
文献类型：
--
作者：
Hu P;Greenwood CM;Beyene J
随着微阵列研究的爆炸式增长，正在产生大量数据。来自不同来源的基因表达数据的系统整合增加了检测差异表达基因的统计能力并允许评估异质性。然而，挑战在于设计和实施有效的分析方法来组合不同研究小组生成的数据。我们扩展了传统的效应大小模型，通过将每项研究中每个基因的质量测量纳入效应大小估计中，组合来自不同微阵列数据集的信息。我们通过整合使用不同 Affymetrix 寡核苷酸类型生成的两个数据集来说明我们的方法。我们的结果表明，所提出的用于对基因表达谱的研究间变异进行建模的质量调整加权策略不仅提高了这两个数据集之间的一致性并减少了异质性结果，而且还比之前提出的方法识别了更多的差异表达基因。数据集成和综合变得越来越重要。我们生活在一个高通量时代，技术不断变化，留下了不同形式、形状和大小的数据痕迹。因此，统计和计算方法对于从这些相关但不相同的数据源中提取最多内容至关重要。
With the explosion of microarray studies, an enormous amount of data is being produced. Systematic integration of gene expression data from different sources increases statistical power of detecting differentially expressed genes and allows assessment of heterogeneity. The challenge, however, is in designing and implementing efficient analytic methodologies for combination of data generated by different research groups. We extended traditional effect size models to combine information from different microarray datasets by incorporating a quality measure for each gene in each study into the effect size estimation. We illustrated our method by integrating two datasets generated using different Affymetrix oligonucleotide types. Our results indicate that the proposed quality-adjusted weighting strategy for modelling inter-study variation of gene expression profiles not only increases consistency and decreases heterogeneous results between these two datasets, but also identifies many more differentially expressed genes than methods proposed previously. Data integration and synthesis is becoming increasingly important. We live in a high-throughput era where technologies constantly change leaving behind a trail of data with different forms, shapes and sizes. Statistical and computational methodologies are therefore critical for extracting the most out of these related but not identical sources of data.
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影响因子：
82.9
作者：
Beer, DG;Kardia, SLR;Hanash, S
通讯作者：
Hanash, S
DOI：
10.1016/0197-2456(86)90046-2
发表时间：
1986-09-01
期刊：
CONTROLLED CLINICAL TRIALS
影响因子：
--
作者：
DERSIMONIAN, R;LAIRD, N
通讯作者：
LAIRD, N
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：
12.3
作者：
Broberg P
通讯作者：
Broberg P
影响因子：
11.5
作者：
Parmigiani, G;Garrett-Mayer, ES;Gabrielson, E
通讯作者：
Gabrielson, E