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基于微阵列荟萃分析的调节效应量和 P 值组合。

Moderated effect size and P-value combinations for microarray meta-analyses.

机构信息

INRA, UMR 1313 Génétique Animale et Biologie Intégrative, Jouy-en-Josas, F-78350, France.

出版信息

Bioinformatics. 2009 Oct 15;25(20):2692-9. doi: 10.1093/bioinformatics/btp444. Epub 2009 Jul 23.

Abstract

MOTIVATION

With the proliferation of microarray experiments and their availability in the public domain, the use of meta-analysis methods to combine results from different studies increases. In microarray experiments, where the sample size is often limited, meta-analysis offers the possibility to considerably increase the statistical power and give more accurate results.

RESULTS

A moderated effect size combination method was proposed and compared with other meta-analysis approaches. All methods were applied to real publicly available datasets on prostate cancer, and were compared in an extensive simulation study for various amounts of inter-study variability. Although the proposed moderated effect size combination improved already existing effect size approaches, the P-value combination was found to provide a better sensitivity and a better gene ranking than the other meta-analysis methods, while effect size methods were more conservative.

AVAILABILITY

An R package metaMA is available on the CRAN.

摘要

动机

随着微阵列实验的普及和它们在公共领域的可用性的增加,使用荟萃分析方法来结合来自不同研究的结果的情况越来越多。在微阵列实验中,样本量通常是有限的,荟萃分析提供了极大地增加统计功效并给出更准确结果的可能性。

结果

提出了一种调节后的效应大小组合方法,并与其他荟萃分析方法进行了比较。所有方法均应用于实际的公开可用的前列腺癌数据集,并在广泛的模拟研究中针对不同程度的研究间变异性进行了比较。虽然所提出的调节后的效应大小组合方法改进了现有的效应大小方法,但发现 P 值组合比其他荟萃分析方法具有更好的敏感性和基因排名,而效应大小方法则更为保守。

可用性

CRAN 上提供了一个名为 metaMA 的 R 包。

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