GSMA: an approach to identify robust global and test Gene Signatures using Meta-Analysis.
GSMA: an approach to identify robust global and test Gene Signatures using Meta-Analysis.
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GSMA：一种使用荟萃分析来识别稳健的全局并测试基因特征的方法。
DOI：
10.1093/bioinformatics/btz561
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Draghici,Sorin
中科院分区：
文献类型：
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作者：
Shafi,Adib;Nguyen,Tin;Peyvandipour,Azam;Draghici,Sorin
动机生物医学研究的最新进展使公共存储库中可以从不同来源获得大量转录组数据。由于个体实验中存在的异质性，从多个独立研究中识别给定疾病的可重复生物标志物已成为一个主要挑战。广泛使用的荟萃分析方法，如Fisher方法，Stouffer方法，minP和maxP，至少有两个主要局限性：（i）它们对离群值敏感，（ii）它们对每个单独的研究只进行一次统计检验，因此没有充分利用潜在的样本量来获得统计功效。我们提出了一个基因水平的荟萃分析框架，该框架克服了这些限制，并确定了一个基因签名，该基因签名在给定疾病的多个独立研究中是可靠的和可重复的。该方法提供了一个全面的全球签名，可用于了解潜在的生物学现象，以及一个较小的测试签名，可用于对给定疾病的未来样本进行分类。我们通过使用包括1108个个体的9个数据集构建流感和阿尔茨海默病的疾病特征来证明该框架的实用性。然后，这些签名在包括912个个体的12个独立数据集上进行验证。结果表明，所提出的方法比大多数现有的荟萃分析方法的灵敏度和特异性方面的表现更好。所提出的特征可进一步用于诊断、预后和治疗靶点的鉴定。补充信息补充数据可在Bioinformatics online获得。
MotivationRecent advances in biomedical research have made massive amount of transcriptomic data available in public repositories from different sources. Due to the heterogeneity present in the individual experiments, identifying reproducible biomarkers for a given disease from multiple independent studies has become a major challenge. The widely used meta-analysis approaches, such as Fisher’s method, Stouffer’s method, minP and maxP, have at least two major limitations: (i) they are sensitive to outliers, and (ii) they perform only one statistical test for each individual study, and hence do not fully utilize the potential sample size to gain statistical power.ResultsHere, we propose a gene-level meta-analysis framework that overcomes these limitations and identifies a gene signature that is reliable and reproducible across multiple independent studies of a given disease. The approach provides a comprehensiveglobal signaturethat can be used to understand the underlying biological phenomena, and a smallertest signaturethat can be used to classify future samples of a given disease. We demonstrate the utility of the framework by constructing disease signatures for influenza and Alzheimer’s disease using nine datasets including 1108 individuals. These signatures are then validated on 12 independent datasets including 912 individuals. The results indicate that the proposed approach performs better than the majority of the existing meta-analysis approaches in terms of both sensitivity as well as specificity. The proposed signatures could be further used in diagnosis, prognosis and identification of therapeutic targets.Supplementary informationSupplementary data are available atBioinformaticsonline.