Epigenetics

Siegmund, Kimberly D.; Lin, S.

doi:10.1002/9780470061619.ch40

Cited by 2 publications

(2 citation statements)

References 69 publications

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“…An earlier review of the literature found many statistical methods utilized mixture-model approaches (Siegmund and Lin 2007). This theme is continued in some more recent work (Khalili et al 2009; Sun et al 2009).…”

Section: Methodsmentioning

confidence: 99%

Statistical approaches for the analysis of DNA methylation microarray data

Siegmund

2011

Hum Genet

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Following the rapid development and adoption in DNA methylation microarray assays, we are now experiencing a growth in the number of statistical tools to analyze the resulting large-scale data sets. As is the case for other microarray applications, biases caused by technical issues are of concern. Some of these issues are old (e.g. two-color dye bias and probe- and array-specific effects), while others are new (e.g. fragment length bias and bisulfite conversion efficiency). Here, I highlight characteristics of DNA methylation that suggest standard statistical tools developed for other data types may not be directly suitable. I then describe the microarray technologies most commonly in use, along with the methods used for pre-processing and obtaining a summary measure. I finish with a section describing down-stream analyses of the data, focusing on methods that model percentage DNA methylation as the outcome, and methods for integrating DNA methylation with gene expression or genotype data.

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Section: Methodsmentioning

confidence: 99%

Statistical approaches for the analysis of DNA methylation microarray data

Siegmund

2011

Hum Genet

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“…However, due to different features and/or different experimental variation of these two biological systems and technological platforms, a method suitable for gene expression analysis may prove to be poor for methylation data. Since methylation microarray platforms are relatively new technologies, statistical methods proposed specifically for analyzing data generated from them are limited (Siegmund and Lin, 2007). …”

Section: Introductionmentioning

confidence: 99%

A robust unified approach to analyzing methylation and gene expression data

Khalili¹,

Huang²,

Lin³

2009

Computational Statistics & Data Analysis

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Microarray technology has made it possible to investigate expression levels, and more recently methylation signatures, of thousands of genes simultaneously, in a biological sample. Since more and more data from different biological systems or technological platforms are being generated at an incredible rate, there is an increasing need to develop statistical methods that are applicable to multiple data types and platforms. Motivated by such a need, a flexible finite mixture model that is applicable to methylation, gene expression, and potentially data from other biological systems, is proposed. Two major thrusts of this approach are to allow for a variable number of components in the mixture to capture non-biological variation and small biases, and to use a robust procedure for parameter estimation and probe classification. The method was applied to the analysis of methylation signatures of three breast cancer cell lines. It was also tested on three sets of expression microarray data to study its power and type I error rates. Comparison with a number of existing methods in the literature yielded very encouraging results; lower type I error rates and comparable/better power were achieved based on the limited study. Furthermore, the method also leads to more biologically interpretable results for the three breast cancer cell lines.

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Epigenetics

Cited by 2 publications

References 69 publications

Statistical approaches for the analysis of DNA methylation microarray data

Statistical approaches for the analysis of DNA methylation microarray data

A robust unified approach to analyzing methylation and gene expression data

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