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<!DOCTYPE html>
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<h1 class="mt-4 mb-3">References <!-- <small>Subheading</small> --></h1>
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<h2>Citing RnBeads</h2>
<p>
Please cite RnBeads 2.0 as follows:
<ul>
<li><a href="https://doi.org/10.1186/s13059-019-1664-9">Fabian Müller*, Michael Scherer*, Yassen Assenov*, Pavlo Lutsik*, Jörn Walter, Thomas Lengauer and Christoph Bock (2019) <i>RnBeads 2.0: comprehensive analysis of DNA methylation data</i>, Genome Biology, 20:55</a></li>
</ul>
For citing the original version of RnBeads:
<ul>
<li><a href="http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3115.html">Yassen Assenov*, Fabian Müller*, Pavlo Lutsik*, Jörn Walter, Thomas Lengauer and Christoph Bock (2014) <i>Comprehensive Analysis of DNA Methylation Data with RnBeads</i>, Nature Methods, 11(11):1138-1140</a></li>
</ul>
</p>
<h2>On the Shoulders of Giants</h2>
<div class="row">
<p>We would also like to emphasize that RnBeads makes extensive use of other R packages, and it may be appropriate to cite some of the underlying methods and contributing packages as well.</p>
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<h4>Infinium data processing</h4>
<ul>
<li>Davis, S., Du, P., Bilke, S., Triche, T., Bootwalla, M. (2013) methylumi: Handle Illumina methylation data. R package version 2.6.1.</li>
</ul>
<h4>Filtering cross-reactive and SNP-overlapping probes</h4>
<ul>
<li>Chen, Y., Lemire, M., Choufani, S., Butcher, D.T., Grafodatskaya, D., Zanke, B.W., Gallinger, S., Hudson, T.J., Weksberg, R. (2013) Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics, 8(2), 203-209</li>
<li>Nordlund, J., Bäcklin, C. L. et al. (2013) Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia. Genome Biology, 14:r105</li>
<li>Pidsley, R., Zotenko, E., Peters, T.J., Lawrence, M.G., Risbridger, G.P., Molloy, P., Djik, S.V., Muhlhausler, B., Strizaker, C., Clark, S.J. (2016) Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biology, 17(1), 208</li>
</ul>
<h4>Background correction</h4>
<ul>
<li>Triche, T.J., Jr., Weisenberger, D.J., Van Den Berg, D., Laird, P.W., Siegmund, K.D. (2013). Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res, 41(7), e90.</li>
</ul>
<h4>SWAN</h4>
<ul>
<li>Maksimovic, J., Gordon, L., Oshlack, A. (2012). SWAN: Subset quantile Within-Array Normalization for Illumina Infinium Infinium 450k BeadChips. Genome Biology, 13(6), R44.</li>
</ul>
<h4>Functional normalization (minfi.funnorm)</h4>
<ul>
<li>Fortin, J., Labbe, A., Lemire, M., Zanke, B.W., Hudson, T. J., Fertig, E.J., Greenwood, C.M.T., Hansen, K.D. (2014) Functional normalization of 450k methylation array data improves replication in large cancer studies. BioRxiv.</li>
</ul>
<h4>SWAN, functional normalization</h4>
<ul>
<li>Aryee, M.J., Jaffe, A.E., Corrada-Bravo, H., Ladd-Acosta, C., Feinberg, A.P., Hansen, K.D., Irizarry, R.A. (2014) Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30(10), 1363-1369</li>
</ul>
<h4>BMIQ</h4>
<ul>
<li>Teschendorff, A.E., Marabita, F., Lechner, M., Bartlett, T., Tegner, J., Gomez-Cabrero, D., Beck, S. (2013) A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics 29(2), 189-196.
</ul>
<h4>Data-driven normalization approaches</h4>
<ul>
<li>Pidsley, R., Wong, C., Volta, M., Lunnon, K., Mill, J., Schalkwyk, L. (2013) A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics, 14(1), 293.</li>
<li>Schalkwyk, L.C., Pidsley R., Wong C.C., Touleimat, W.C.F.N., Defrance M., Teschendorff A., Maksimovic J. (2013). wateRmelon: Illumina 450 methylation array normalization and metrics. R package version 1.4.0.</li>
</ul>
<h4>Genome-wide coverage plots in quality control for bisulfite sequencing data</h4>
<ul>
<li>Yin, T., Cook, D., Lawrence, M. (2012). ggbio: an R package for extending the grammar of graphics for genomic data. Genome Biology, 13(8), pp. R77.</li>
</ul>
<h4>Differential methylation using the "limma" method</h4>
<ul>
<li>Smyth, G.K. (2005). Limma: linear models for microarray data. In Gentleman R, Carey, V., Dudoit., S., Irizarry R., Huber W. (eds.), Bioinformatics and Computational Biology Solutions Using R and Bioconductor, pp. 397-420. Springer, New York.</li>
</ul>
<h4>Reference-based cell type heterogeneity estimation</h4>
<ul>
<li>Houseman, E.A., Accomando, W.P., Koestler, D.C., Christensen, B.C., Marsit, C.J., Nelson, H.H., Wiencke, J.K., Kelsey, K.T. (2012). DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13:86.</li>
</ul>
<h4>Reference-free correction for cell type heterogeneity</h4>
<ul>
<li>Houseman, E.A., Molitor, J., Marsit, C.J. (2014). Reference-Free Cell Mixture Adjustments in Analysis of DNA Methylation Data. Bioinformatics, btu029.</li>
</ul>
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