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DNA methylation data deconvolution software tools | DNA modification analysis

Recent work applying EWAS suggests an important role for DNA methylation as a mechanism involved with disease. In a standard EWAS of primary tissue such as whole-blood, methylation data represent the epigenetic states of a heterogeneous mixture of cell-types. Since the epigenome is highly variable across different cell-types, correlations between the phenotype of interest and the cell-type composition lead to a large number of false discoveries(Jaffe et al.,2014; Zou et al., 2014).
The standard statistical analysis applied in EWAS uses a univariate test for correlation between the phenotype and each of the probed CpG sites. Thus, false discoveries due to cell-type heterogeneity can be addressed by adding the cell proportions as covariates. However, cell-type compositions are typically not measured and therefore a computational method has been proposed for the estimation of cell-type composition using a reference dataset which includes methylation measurements for sorted cells (Houseman et al., 2012).

(Jaffe et al.,2014) Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol.
(Zou et al., 2014) Epigenome-wide association studies without the need for cell-type composition. Nat Methods.
(Houseman et al., 2012) DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics.

Source text:
(Rahmani et al., 2016) Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies. Nat Methods.

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