Identifying regulatory TFs

This step is to identify regulatory TF whose expression associates with TF binding motif DNA methylation which is carried out by function get.TFs.

For each motif considered to be enriched within a particular probe set, it will compare the average DNA methylation at all distal enhancer probes within ±250bp
of a motif occurrence, to the expression of human TFs. A statistical test was performed for each motif-TF pair, as follows. The samples (all groups samples) were divided into two groups: the M group, which consisted of the 20% of samples with the highest average methylation at all motif-adjacent probes, and the U group, which consisted of the 20% of samples with the lowest methylation. For each candidate motif-TF pair, the Mann-Whitney U test was used to test the null hypothesis that overall gene expression in group M was greater or equal than that in group U. All TFs were ranked by the −log₁₀(P_r), and those falling within the top 5% of this ranking were considered candidate upstream regulators.

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

Main get.pair arguments

Argument	Description
data	A multiAssayExperiment with DNA methylation and Gene Expression data. See createMAE function.
enriched.motif	A list containing output of get.enriched.motif function or a path of XX.rda file containing output of get.enriched.motif function.
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.
group2	A group from group.col.
minSubgroupFrac	A number ranging from 0 to 1 specifying the percentage of samples used to create the groups U (unmethylated) and M (methylated) used to link probes to TF expression. Default is 0.4 (lowest quintile of all samples will be in the U group and the highest quintile of all samples in the M group).
mode	A character. Can be “unsupervised” or “supervised”. If unsupervised is set the U (unmethylated) and M (methylated) groups will be selected among all samples based on methylation of each probe. Otherwise U group and M group will set as the samples of group1 or group2 as described below: If diff.dir is “hypo, U will be the group 1 and M the group2. If diff.dir is”hyper" M group will be the group1 and U the group2.
diff.dir	A character can be “hypo” or “hyper”, showing differential methylation dirction in group 1. It can be “hypo” which means the probes are hypomethylated in group1; “hyper” which means the probes are hypermethylated in group1; This argument is used only when mode is supervised nad it should be the same value from get.diff.meth function.

# Load results from previous sections
mae <- get(load("mae.rda"))
load("result/getMotif.hypo.enriched.motifs.rda")

## identify regulatory TF for the enriched motifs
TF <- get.TFs(data = mae, 
              group.col = "definition",
              group1 =  "Primary solid Tumor",
              group2 = "Solid Tissue Normal",
              minSubgroupFrac = 0.4,
              enriched.motif = enriched.motif,
              dir.out = "result", 
              cores = 1, 
              label = "hypo")

# get.TFs automatically save output files. 
# getTF.hypo.TFs.with.motif.pvalue.rda contains statistics for all TF with average 
# DNA methylation at sites with the enriched motif.
# getTF.hypo.significant.TFs.with.motif.summary.csv contains only the significant probes.
dir(path = "result", pattern = "getTF")  

## [1] "getTF.hypo.TFs.with.motif.pvalue.rda"             
## [2] "getTF.hypo.significant.TFs.with.motif.summary.csv"

# TF ranking plot based on statistics will be automatically generated.
dir(path = "result/TFrankPlot_family/", pattern = "pdf") 

## character(0)

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.