# 3.2 - Identifying differentially methylated probes

# Identifying differentially methylated probes

The first step is the identification of differentially methylated CpGs (DMCs) carried out by function `get.diff.meth`.

In the `Supervised` mode, we compare the DNA methylation level of each distal CpG for all samples in Group 1 compared to all samples Group 2, using an unpaired one-tailed t-test. In the `Unsupervised` mode, the samples of each group (Group 1 and Group 2) are ranked by their DNA methylation beta values for the given probe, and those samples in the lower quintile (20% samples with the lowest methylation levels) of each group are used to identify if the probe is hypomethylated in Group 1 compared to Group 2. The reverse applies for the identification of hypermethylated probes. It is important to highlight that in the `Unsupervised` mode, each probe selected may be based on a different subset the samples, and thus probe sets from multiple molecular subtypes may be represented. In the `Supervised` mode, all tests are based on the same set of samples.

The 20% is a parameter to the `diff.meth` function called `minSubgroupFrac`. For the unsupervised analysis, this is set to 20% as in Yao et al. (Yao and others 2015), because we wanted to be able to detect a specific molecular subtype among samples; these subtypes often make up only a minority of samples, and 20% was chosen as a lower bound for the purposes of statistical power (high enough sample numbers to yield t-test p-values that could overcome multiple hypotheses corrections, yet low enough to be able to capture changes in individual molecular subtypes occurring in 20% or more of the cases.) This number can be set as an input to the `diff.meth` function and should be tuned based on sample sizes in individual studies. In the `Supervised` mode, where the comparison groups are implicit in the sample set and labeled, the `minSubgroupFrac` parameter is set to 100%. An example would be a cell culture experiment with 5 replicates of the untreated cell line, and another 5 replicates that include an experimental treatment.

To identify hypomethylated DMCs, a one-tailed t-test is used to rule out the null hypothesis: μg r o u p1≥μg r o u p2, where μg r o u p1 is the mean methylation within the lowest group 1 quintile (or another percentile as specified by the `minSubgroupFrac` parameter) and μg r o u p2 is the mean within the lowest group 2 quintile. Raw p-values are adjusted for multiple hypothesis testing using the Benjamini-Hochberg method, and probes are selected when they had adjusted p-value less than 0.01 (which can be configured using the `pvalue` parameter). For additional stringency, probes are only selected if the methylation difference: Δ=μg r o u p1−μg r o u p2 was greater than 0.3. The same method is used to identify hypermethylated DMCs, except we use the upper quintile, and the opposite tail in the t-test is chosen.

(Yao and others 2015, @yao2015demystifying)

# Function arguments

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”, “hyper” or “both”, showing differential methylation dirction. It can be “hypo” which is only selecting hypomethylated probes (one tailed test); “hyper” which is only selecting hypermethylated probes (one tailed test); or “both” which are probes differenly methylated (two tailed test).
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.

# Example of use

    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, # if supervised mode set to 1
                              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
    # - a volcano plot with the diff mean and significance levels
    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): 550–73.

Yao, Lijing, and others. 2015. “Inferring Regulatory Element Landscapes and Transcription Factor Networks from Cancer Methylomes.” Genome Biology 16 (1): 105.