Identifying differentially methylated probes

This step is to identify DNA methylation changes at distal enhancer probes which is carried out by function get.diff.meth.

For each distal probe, the function first rank samples from group 1 and group 2 samples by their DNA methylation beta values. To identify hypomethylated probes, the function compared the lower control quintile (20% of control samples with the lowest methylation) to the lower experiment quintile (20% of experiment samples with the lowest methylation), using an unpaired one-tailed t-test.

(Yao et al. 2015,Yao, Berman, and Farnham (2015))

Main get.diff.meth arguments

Argument	Description
data	A multiAssayExperiment with DNA methylation and Gene Expression data. See createMAE function.
diff.dir	A character can be “hypo” or “hyper”, showing differential methylation dirction. It can be “hypo” which is only selecting hypomethylated probes; “hyper” which is only selecting hypermethylated probes
minSubgroupFrac	A number ranging from 0 to 1,specifying the fraction of extreme samples from group 1 and group 2 that are used to identify the differential DNA methylation. The default is 0.2 because we typically want to be able to detect a specific (possibly unknown) molecular subtype among tumor; these subtypes often make up only a minority of samples, and 20% was chosen as a lower bound for the purposes of statistical power. If you are using pre-defined group labels, such as treated replicates vs. untreated replicated, use a value of 1.0 (Supervised mode)
pvalue	A number specifies the significant P value (adjusted P value by BH) cutoff for selecting significant hypo/hyper-methylated probes. Default is 0.01
group.col	A column defining the groups of the sample. You can view the available columns using: colnames(MultiAssayExperiment::colData(data)).
group1	A group from group.col. ELMER will run group1 vs group2. That means, if direction is hyper, get probes hypermethylated in group 1 compared to group 2.
group2	A group from group.col. ELMER will run group1 vs group2. That means, if direction is hyper, get probes hypermethylated in group 1 compared to group 2.
sig.dif	A number specifies the smallest DNA methylation difference as a cutoff for selecting significant hypo/hyper-methylated probes. Default is 0.3.

mae <- get(load("mae.rda"))
sig.diff <- get.diff.meth(data = mae, 
                          group.col = "definition",
                          group1 =  "Primary solid Tumor",
                          group2 = "Solid Tissue Normal",
                          minSubgroupFrac = 0.2,
                          sig.dif = 0.3,
                          diff.dir = "hypo", # Search for hypomethylated probes in group 1
                          cores = 1, 
                          dir.out ="result", 
                          pvalue = 0.01)

head(sig.diff)  %>% datatable(options = list(scrollX = TRUE))

# get.diff.meth automatically save output files. 
# getMethdiff.hypo.probes.csv contains statistics for all the probes.
# getMethdiff.hypo.probes.significant.csv contains only the significant probes which
# is the same with sig.diff
dir(path = "result", pattern = "getMethdiff")  

## [1] "getMethdiff.hypo.probes.csv"            
## [2] "getMethdiff.hypo.probes.significant.csv"

Bibliography

Yao, Lijing, Benjamin P Berman, and Peggy J Farnham. 2015. “Demystifying the Secret Mission of Enhancers: Linking Distal Regulatory Elements to Target Genes.” Critical Reviews in Biochemistry and Molecular Biology 50 (6). Taylor & Francis: 550–73.

Yao, Lijing, Hui Shen, Peter W Laird, Peggy J Farnham, and Benjamin P Berman. 2015. “Inferring Regulatory Element Landscapes and Transcription Factor Networks from Cancer Methylomes.” Genome Biology 16 (1). BioMed Central: 105.