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      Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis

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          Abstract

          Background

          High-throughput profiling of DNA methylation status of CpG islands is crucial to understand the epigenetic regulation of genes. The microarray-based Infinium methylation assay by Illumina is one platform for low-cost high-throughput methylation profiling. Both Beta-value and M-value statistics have been used as metrics to measure methylation levels. However, there are no detailed studies of their relations and their strengths and limitations.

          Results

          We demonstrate that the relationship between the Beta-value and M-value methods is a Logit transformation, and show that the Beta-value method has severe heteroscedasticity for highly methylated or unmethylated CpG sites. In order to evaluate the performance of the Beta-value and M-value methods for identifying differentially methylated CpG sites, we designed a methylation titration experiment. The evaluation results show that the M-value method provides much better performance in terms of Detection Rate (DR) and True Positive Rate (TPR) for both highly methylated and unmethylated CpG sites. Imposing a minimum threshold of difference can improve the performance of the M-value method but not the Beta-value method. We also provide guidance for how to select the threshold of methylation differences.

          Conclusions

          The Beta-value has a more intuitive biological interpretation, but the M-value is more statistically valid for the differential analysis of methylation levels. Therefore, we recommend using the M-value method for conducting differential methylation analysis and including the Beta-value statistics when reporting the results to investigators.

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          Most cited references13

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          Principles and challenges of genomewide DNA methylation analysis.

          Methylation of cytosine bases in DNA provides a layer of epigenetic control in many eukaryotes that has important implications for normal biology and disease. Therefore, profiling DNA methylation across the genome is vital to understanding the influence of epigenetics. There has been a revolution in DNA methylation analysis technology over the past decade: analyses that previously were restricted to specific loci can now be performed on a genome-scale and entire methylomes can be characterized at single-base-pair resolution. However, there is such a diversity of DNA methylation profiling techniques that it can be challenging to select one. This Review discusses the different approaches and their relative merits and introduces considerations for data analysis.
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            CpG island hypermethylation and tumor suppressor genes: a booming present, a brighter future.

            We have come a long way since the first reports of the existence of aberrant DNA methylation in human cancer. Hypermethylation of CpG islands located in the promoter regions of tumor suppressor genes is now firmly established as an important mechanism for gene inactivation. CpG island hypermethylation has been described in almost every tumor type. Many cellular pathways are inactivated by this type of epigenetic lesion: DNA repair (hMLH1, MGMT), cell cycle (p16(INK4a), p15(INK4b), p14(ARF)), apoptosis (DAPK), cell adherence (CDH1, CDH13), detoxification (GSTP1), etc em leader However, we still know little of the mechanisms of aberrant methylation and why certain genes are selected over others. Hypermethylation is not an isolated layer of epigenetic control, but is linked to the other pieces of the puzzle such as methyl-binding proteins, DNA methyltransferases and histone deacetylase, but our understanding of the degree of specificity of these epigenetic layers in the silencing of specific tumor suppressor genes remains incomplete. The explosion of user-friendly technologies has given rise to a rapidly increasing list of hypermethylated genes. Careful functional and genetic studies are necessary to determine which hypermethylation events are truly relevant for human tumorigenesis. The development of CpG island hypermethylation profiles for every form of human tumors has yielded valuable pilot clinical data in monitoring and treating cancer patients based in our knowledge of DNA methylation. Basic and translational will both be needed in the near future to fully understand the mechanisms, roles and uses of CpG island hypermethylation in human cancer. The expectations are high.
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              Model-based variance-stabilizing transformation for Illumina microarray data

              Variance stabilization is a step in the preprocessing of microarray data that can greatly benefit the performance of subsequent statistical modeling and inference. Due to the often limited number of technical replicates for Affymetrix and cDNA arrays, achieving variance stabilization can be difficult. Although the Illumina microarray platform provides a larger number of technical replicates on each array (usually over 30 randomly distributed beads per probe), these replicates have not been leveraged in the current log2 data transformation process. We devised a variance-stabilizing transformation (VST) method that takes advantage of the technical replicates available on an Illumina microarray. We have compared VST with log2 and Variance-stabilizing normalization (VSN) by using the Kruglyak bead-level data (2006) and Barnes titration data (2005). The results of the Kruglyak data suggest that VST stabilizes variances of bead-replicates within an array. The results of the Barnes data show that VST can improve the detection of differentially expressed genes and reduce false-positive identifications. We conclude that although both VST and VSN are built upon the same model of measurement noise, VST stabilizes the variance better and more efficiently for the Illumina platform by leveraging the availability of a larger number of within-array replicates. The algorithms and Supplementary Data are included in the lumi package of Bioconductor, available at: www.bioconductor.org.
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                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2010
                30 November 2010
                : 11
                : 587
                Affiliations
                [1 ]Northwestern University Biomedical Informatics Center (NUBIC), NUCATS, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
                [2 ]Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
                [3 ]The Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA
                [4 ]Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
                Article
                1471-2105-11-587
                10.1186/1471-2105-11-587
                3012676
                21118553
                88943c3e-54fe-46b8-972f-418576d75f55
                Copyright ©2010 Du et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 27 August 2010
                : 30 November 2010
                Categories
                Research Article

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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