1. Challenges in Modeling and Analyzing DNA Methylation Data Shili Lin Department of Statistics The Ohio State University

2. Challenges How to assign/distribute a read to CpG sites to obtain inferred nucleotide resolution methylation (NTRM) data? How to combine correlated signals (modeling needed) form NTRM within a well-defined region to detect differential methylation?

3. How to Detect Differentially Methylated Regions? Averaging over the region will likely wash out the signals. Point by point analysis is not powerful and will likely lead to inconsistent signals throughout the region. Regional joint analysis needs to take correlation into account. How to model such correlation? The two-step idea (derive NTRM data then perform methylation analysis) does not take into account of uncertainty in first step. One-step analysis strategies?

4. Challenges With Methyl-CpG (Serre 2010) or MethylCap-seq (Yan 2012), NTRM are not observable but need to be inferred. Can no longer perform Fisher exact or chi-square test on 2 x 2 contingency table (columns: two alleles of a SNP; rows: counts of methylated and unmethylated cytosines at CpG site located on a SNP-containing read). If data from multiple cell lines are available, can the data be pooled (modeling needed) to increase statistical power? Can cell lines with homozygous genotypes be included in the analysis? (Poisson-binomial model? – seen in a talk)