Methods For Estimating Changes In Dna Methylation In The Presence Of Cell Type Heterogeneity Kevin Mcgregor by Kevin Mcgregor instant download after payment.

Though a large proportion of CpG sites in mammals are methylated, methylation signatures differ notably between cell types. Consequently, when measuring methylation levels on whole blood or other types of tissues involving multiple cell types, it can be difficult to distinguish the changes associated with a phenotype of interest from those occurring as a result of varying proportions of different cell types among subjects.

This phenomenon is of concern when changes in cell type proportion are associated with the phenotype itself, thereby making cell type proportion a confounder. There are several recently developed methods that attempt to correct for this confounding, including one method based on an external validation data set (Houseman et al., BMC Bioinformatics 2012), a reference-free method (Houseman et al., Bioinformatics 2014), Surrogate Variable Analysis (Leek and Storey, PLoS Genetics 2007), Independent Surrogate Variable Analysis (Teschendorff, Bioinformatics 2011), the FAST-LMM-EWASher method (Zou, Nature Methods 2014), Deconfounding (Repsilber, BMC Bioinformatics 2010), and CellCDecon (Wagner, PhD Thesis 2014).

In order to compare the performance of each method, we have artificially re-combined measures of methylation obtained from cell-separated analysis of whole blood. Specifically, methylation measures are available for monocytes and CD4 T-cells. We randomly chose a subset of the samples to be disease cases, then we designated a set of CpG sites to be associated with the disease.

A new artificial set of methylation measurements was generated by combining the values from each cell type with variable proportions of each cell type in each subject.
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