Kling Teresia, Carén Helena
Department of Pathology and Genetics, Sahlgrenska Cancer Center, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Methods Mol Biol. 2019;1908:205-217. doi: 10.1007/978-1-4939-9004-7_14.
This chapter discusses analysis and interpretation of large-scale Illumina DNA methylation microarray data, used in the context of cancer studies. We outline commonly used normalization procedures and list issues to consider regarding data preprocessing. Focusing on software packages for R, we describe methods for finding features in the methylation data that are of importance for generating and testing hypotheses in cancer research, like differentially methylated positions or regions and global methylation trends.
本章讨论了在癌症研究背景下使用的大规模Illumina DNA甲基化微阵列数据的分析与解读。我们概述了常用的标准化程序，并列出了数据预处理方面需要考虑的问题。聚焦于R语言的软件包，我们描述了在甲基化数据中寻找对癌症研究中生成和检验假设（如差异甲基化位点或区域以及整体甲基化趋势）具有重要意义的特征的方法。
