@article{Jiao2018,
abstract = {Aim: We aimed to prove the existence of positional effects in the Illumina methylation beadchip data and to find an optimal correction method. Materials {\&} methods: Three HumanMethylation450, three HumanMethylation27 datasets and two EPIC datasets were analyzed. ComBat, linear regression, functional normalization and single-sample Noob were used for minimizing positional effects. The corrected results were evaluated by four methods. Results: We detected 52,988 CpG loci significantly associated with sample positions, 112 remained after ComBat correction in the primary dataset. The pre- and postcorrection comparisons indicate the positional effects could alter the measured methylation values and downstream analysis results. Conclusion: Positional effects exist in the Illumina methylation array and may bias the analyses. Using ComBat to correct positional effects is recommended.},
author = {Jiao, Chuan and Zhang, Chunling and Dai, Rujia and Xia, Yan and Wang, Kangli and Giase, Gina and Chen, Chao and Liu, Chunyu},
doi = {10.2217/epi-2017-0105},
file = {:C$\backslash$:/Users/ljsinke/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Jiao et al. - 2018 - Positional effects revealed in Illumina methylation array and the impact on analysis.pdf:pdf},
issn = {1750192X},
journal = {Epigenomics},
keywords = {ComBat,DNA methylation,Illumina Infinium MethylationEPIC BeadChip,Infinium Methylation 27K,Infinium Methylation 450K,epigenetics,epigenomics,positional effects},
month = {may},
number = {5},
pages = {643--659},
publisher = {Future Medicine Ltd.},
title = {{Positional effects revealed in Illumina methylation array and the impact on analysis}},
volume = {10},
year = {2018}
}
@article{Leek2010,
abstract = {High-throughput technologies are widely used, for example to assay genetic variants, gene and protein expression, and epigenetic modifications. One often overlooked complication with such studies is batch effects, which occur because measurements are affected by laboratory conditions, reagent lots and personnel differences. This becomes a major problem when batch effects are correlated with an outcome of interest and lead to incorrect conclusions. Using both published studies and our own analyses, we argue that batch effects (as well as other technical and biological artefacts) are widespread and critical to address. We review experimental and computational approaches for doing so. {\textcopyright} 2010 Macmillan Publishers Limited. All rights reserved.},
author = {Leek, Jeffrey T. and Scharpf, Robert B. and Bravo, H{\'{e}}ctor Corrada and Simcha, David and Langmead, Benjamin and Johnson, W. Evan and Geman, Donald and Baggerly, Keith and Irizarry, Rafael A.},
booktitle = {Nature Reviews Genetics},
doi = {10.1038/nrg2825},
file = {:C$\backslash$:/Users/ljsinke/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Leek et al. - 2010 - Tackling the widespread and critical impact of batch effects in high-throughput data.pdf:pdf},
issn = {14710056},
keywords = {Bioinformatics,Genetic techniques},
month = {oct},
number = {10},
pages = {733--739},
publisher = {Nature Publishing Group},
title = {{Tackling the widespread and critical impact of batch effects in high-throughput data}},
volume = {11},
year = {2010}
}
