Easier analysis of methylation array data

Methylation Array Data Analysis Tips

Since the release of Illumina’s first Methylation BeadChips, the user community has been instrumental in their widespread adoption by developing software packages for advanced methylation data analysis. While GenomeStudio continues to be used by core laboratories for basic quality control, third party Bioconductor packages offer the most functionality for downstream analysis. The two most popular packages are SeSAMe and minfi, which both provide end-to-end data analysis of Infinium Methylation BeadChips including advanced QC, updated normalization techniques, differential methylation analysis, and visualization capabilities.

For BeadChip Processing Laboratories

The GenomeStudio 2011.1 Methylation Module can be used for basic QC of methylation beadchips. The Controls Dashboard in GenomeStudio is used to visualize sample-independent and sample-dependent controls, whereas the BeadArray Controls reporter (BACR) provides a formula-based analysis of controls for fast results. GenomeStudio 2011.1 and BACR are available to download here.

For Advanced Users

The following video tutorial series, led by SeSAMe developer Wanding Zhou, provides step-by-step tutorials to familiarize new users with data analysis on SeSAMe:

Installing SeSAMe

In this video you’ll learn how to install SeSAMe to perform data analysis for the Infinium DNA methylation beadchip. All the scripts and links can be found on this SeSAMe Installation Github page. If you haven't installed R on your computer yet, please do so before watching this video.

Pre-Processing Infinium Methylation Data

This video tutorial will show you how to process IDATs into DNA methylation level data, or the beta values. This tutorial uses two public datasets from Gene Expression Omnibus or GEO. You’ll learn how to read in the signal intensity data, perform quality control, assess results, and more.

Modeling Differential Methylation

In this video, we’ll go over some of the linear modeling-based frameworks for analyzing differential DNA methylation. You’ll learn how to load packages and data, what to consider and check for prior to modeling, perform linear modeling, and investigate biological questions following test results.

Inferring Sample Metadata

This video tutorial will demonstrate how to use the SeSAMe software to infer sample metadata. This metadata can be sex, age, DNA copy number or cell fraction, or other metadata. This tutorial demonstrates various inferences to provide a broad understanding of the process.

Additional information and full documentation can be found on the SeSAMe Bioconductor page.

Minfi is a comprehensive package for methylation data analysis developed by Kasper Hansen. Extensions of minfi include RnBeads, ChAMP, and wateRmelon. Visit the minfi Bioconductor page for documentation including user guides and installation instructions. For an in-depth hands-on training of methylation array data analysis based on minfi, Columbia University offers the Epigenetics Boot Camp. In addition, archived tutorial videos using 450K data can be found here, and an introduction video by Kasper Hansen can be found here.

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