Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline.
Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline.
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DOI：
10.1186/1471-2105-14-368
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发表时间：
2013-12-21
影响因子：
3
通讯作者：
Tseng GC
中科院分区：
文献类型：
--
作者：
Chang LC;Lin HM;Sibille E;Tseng GC
随着高通量基因组技术变得准确和负担得起，在公共领域积累了越来越多的数据集，基因组信息集成和荟萃分析已经成为生物医学研究的常规。在本文中，我们专注于微阵列荟萃分析，将多个微阵列研究与相关的生物学假设相结合，以改进候选标记的检测。在文献中已经开发和应用了许多方法，但对它们的性能和性质的研究还很少。目前还没有明确的结论或指导方针来正确选择一种应用的荟萃分析方法；这一决定基本上需要统计和生物学方面的考虑。我们执行了12种微阵列Meta分析方法来组合多个模拟的表达谱，这些方法可以根据不同的假设设置目的而分类：(1)所有研究中效应大小非零的HS A：DE基因，(2)一个或多个研究中效应大小非零的HS B：DE基因，以及(3)大多数研究中效应非零的HS r：DE基因。然后，我们通过六个大规模的实际应用，使用四个定量统计评估标准：检测能力、生物关联性、稳定性和稳健性进行了全面的比较分析。我们阐明了这些方法背后的假设设置，并进一步应用多维尺度(MDS)和熵度量来分别表征荟萃分析方法和数据结构。模拟研究的汇总结果将12种方法归类为三种假设设置(HS A、HS B和HS R)。对实际数据的评价，以及MDS和熵分析的结果，为在特定应用中选择最合适的方法提供了有见地和实用的指导。所有用于模拟和真实数据的源文件都可以在作者的出版物网站上找到。
As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations. We performed 12 microarray meta-analysis methods for combining multiple simulated expression profiles, and such methods can be categorized for different hypothesis setting purposes: (1) HS A : DE genes with non-zero effect sizes in all studies, (2) HS B : DE genes with non-zero effect sizes in one or more studies and (3) HS r : DE gene with non-zero effect in "majority" of studies. We then performed a comprehensive comparative analysis through six large-scale real applications using four quantitative statistical evaluation criteria: detection capability, biological association, stability and robustness. We elucidated hypothesis settings behind the methods and further apply multi-dimensional scaling (MDS) and an entropy measure to characterize the meta-analysis methods and data structure, respectively. The aggregated results from the simulation study categorized the 12 methods into three hypothesis settings (HS A , HS B , and HS r ). Evaluation in real data and results from MDS and entropy analyses provided an insightful and practical guideline to the choice of the most suitable method in a given application. All source files for simulation and real data are available on the author’s publication website.
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影响因子：
37.3
作者：
Dreyfuss JM;Johnson MD;Park PJ
通讯作者：
Park PJ
影响因子：
3
作者：
Wu W;Dave N;Tseng GC;Richards T;Xing EP;Kaminski N
通讯作者：
Kaminski N
影响因子：
3.7
作者：
BIRNBAUM, A
通讯作者：
BIRNBAUM, A
DOI：
10.1142/s0219720005001442
发表时间：
2005-10-01
影响因子：
1
作者：
Breitling, Rainer;Herzyk, Pawel
通讯作者：
Herzyk, Pawel
DOI：
10.1073/pnas.091062498
发表时间：
2001-04-24
影响因子：
11.1
作者：
Tusher, VG;Tibshirani, R;Chu, G
通讯作者：
Chu, G