Comparison of statistical methods and the use of quality control samples for batch effect correction in human transcriptome data.
Comparison of statistical methods and the use of quality control samples for batch effect correction in human transcriptome data.
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DOI：
10.1371/journal.pone.0202947
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
de Kok TMCM
中科院分区：
文献类型：
--
作者：
Espín-Pérez A;Portier C;Chadeau-Hyam M;van Veldhoven K;Kleinjans JCS;de Kok TMCM
批次效应是变异的技术来源，由于在基于人群的研究中有大量生物样本，因此需要在不同日期进行基因表达分析。本研究的目的是评估线性混合模型（LMM）和战斗在批量效应去除的性能。我们还评估了在研究设计中添加质控样本作为技术重复样本的效用。为了做到这一点，我们通过向真实的基因表达数据集添加“处理”和批处理效应来模拟基因表达数据。在灵敏度和特异性方面评估了LMM和Combat的性能（有和无质控样品），同时使用广泛的效应量、统计噪声、样本量和平衡/不平衡设计水平校正批次效应。模拟显示LMM和Combat之间的差异很小。LMM比Combat识别大效应大小和基因表达之间更强的关系，而Combat通常比LMM识别更多的真阳性和假阳性。然而，这些微小的差异仍然可以取决于研究目标。当应用这些方法中的任何一种时，质量控制样品都不会降低批次效应，表明将其纳入研究设计中没有增加价值。
Batch effects are technical sources of variation introduced by the necessity of conducting gene expression analyses on different dates due to the large number of biological samples in population-based studies. The aim of this study is to evaluate the performances of linear mixed models (LMM) and Combat in batch effect removal. We also assessed the utility of adding quality control samples in the study design as technical replicates. In order to do so, we simulated gene expression data by adding “treatment” and batch effects to a real gene expression dataset. The performances of LMM and Combat, with and without quality control samples, are assessed in terms of sensitivity and specificity while correcting for the batch effect using a wide range of effect sizes, statistical noise, sample sizes and level of balanced/unbalanced designs. The simulations showed small differences among LMM and Combat. LMM identifies stronger relationships between big effect sizes and gene expression than Combat, while Combat identifies in general more true and false positives than LMM. However, these small differences can still be relevant depending on the research goal. When any of these methods are applied, quality control samples did not reduce the batch effect, showing no added value for including them in the study design.
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影响因子：
48
作者：
Huber W;Carey VJ;Gentleman R;Anders S;Carlson M;Carvalho BS;Bravo HC;Davis S;Gatto L;Girke T;Gottardo R;Hahne F;Hansen KD;Irizarry RA;Lawrence M;Love MI;MacDonald J;Obenchain V;Oleś AK;Pagès H;Reyes A;Shannon P;Smyth GK;Tenenbaum D;Waldron L;Morgan M
通讯作者：
Morgan M
影响因子：
2.7
作者：
Head SR;Komori HK;LaMere SA;Whisenant T;Van Nieuwerburgh F;Salomon DR;Ordoukhanian P
通讯作者：
Ordoukhanian P
影响因子：
5.8
作者：
Huang, Hanwen;Lu, Xiaosun;Marron, J. S.
通讯作者：
Marron, J. S.
影响因子：
3.7
作者：
Schurmann C;Heim K;Schillert A;Blankenberg S;Carstensen M;Dörr M;Endlich K;Felix SB;Gieger C;Grallert H;Herder C;Hoffmann W;Homuth G;Illig T;Kruppa J;Meitinger T;Müller C;Nauck M;Peters A;Rettig R;Roden M;Strauch K;Völker U;Völzke H;Wahl S;Wallaschofski H;Wild PS;Zeller T;Teumer A;Prokisch H;Ziegler A
通讯作者：
Ziegler A
DOI：
10.1038/tpj.2010.57
发表时间：
2010-08
期刊：
The pharmacogenomics journal
影响因子：
--
作者：
Luo J;Schumacher M;Scherer A;Sanoudou D;Megherbi D;Davison T;Shi T;Tong W;Shi L;Hong H;Zhao C;Elloumi F;Shi W;Thomas R;Lin S;Tillinghast G;Liu G;Zhou Y;Herman D;Li Y;Deng Y;Fang H;Bushel P;Woods M;Zhang J
通讯作者：
Zhang J