# Wed. Oct. 30, Notes on differential methylation stats

### Methods:

- Methylpy DMRfind:
- root mean square test for each CpG within an individual to determine if mCG counts:CG counts is statistically different than what would be expected from “genetic origins of methylation variation” alone. If the CpG site is significant after FDR correction, it is considered a DMS (differentially methylated site)
- DMS are combined into blocks (“region”) if they fall within a predefined window and have methylation changes in the same direction.
- Need to run group statistics

Reference: Schultz et al. (2015) Nature. doi:10.1038/nature14464 See pages 4-8 of Supp. Material

- Methylkit:
- calculateDiffMeth uses a logistic regression model to test if treatment has any effect on CpG (loci) methylation or region methylation.
- is log(πi/(1−πi))=β0+β1Treatmenti a “better” model than log(πi/(1−πi))=β0 ?
- a q-value is calculated and a cutoff can be defined to select significant DMLs or DMRs
- this method is only capable of comparing two groups at a time.

- calculateDiffMeth uses a logistic regression model to test if treatment has any effect on CpG (loci) methylation or region methylation.

Reference: methylkit documentation, see section 3.6

- DMGs:
- bionmial glm comparison between methylated and non-methylated CpGs
- glm (methylated,non_methylated ~ pH * position, family = “binomial”)
- each treatment replicate is compared to a control replicate in a combinatorial fashion to assign equal weightage to each replicate

- Liew also does glm on mean % methylation with gaussian method

- bionmial glm comparison between methylated and non-methylated CpGs

Reference Liew et al. (2018) Science Advances

Written on October 30, 2019