TCGAanalyze_DMR {TCGAbiolinks} | R Documentation

## Differentially methylated regions Analysis

### Description

This function will search for differentially methylated CpG sites, which are regarded as possible functional regions involved in gene transcriptional regulation.

In order to find these regions we use the beta-values (methylation values ranging from 0.0 to 1.0) to compare two groups.

Firstly, it calculates the difference between the mean methylation of each group for each probes. Secondly, it calculates the p-value using the wilcoxon test using the Benjamini-Hochberg adjustment method. The default parameters will require a minimum absolute beta values delta of 0.2 and a false discovery rate (FDR)-adjusted Wilcoxon rank-sum P-value of < 0.01 for the difference.

After these analysis, we save a volcano plot (x-axis:diff mean methylation, y-axis: significance) that will help the user identify the differentially methylated CpG sites and return the object with the calculus in the rowRanges.

If the calculus already exists in the object it will not recalculated. You should set overwrite parameter to TRUE to force it, or remove the collumns with the results from the object.

### Usage

    TCGAanalyze_DMR(data, groupCol = NULL, group1 = NULL, group2 = NULL,
      calculate.pvalues.probes = "all",
      plot.filename = "methylation_volcano.pdf",
      ylab = expression(paste(-Log[10], " (FDR corrected -P values)")),
      xlab = expression(paste("DNA Methylation difference (", beta,
      "-values)")), title = NULL, legend = "Legend", color = c("black",
      "red", "darkgreen"), label = NULL, xlim = NULL, ylim = NULL,
      p.cut = 0.01, probe.names = FALSE, diffmean.cut = 0.2,
      paired = FALSE, adj.method = "BH", overwrite = FALSE, cores = 1,
      save = TRUE, save.directory = ".", filename = NULL)

### Arguments

`data` | SummarizedExperiment obtained from the TCGAPrepare
`groupCol` | Columns with the groups inside the SummarizedExperiment object. (This will be obtained by the function colData(data))
`group1` | In case our object has more than 2 groups, you should set the name of the group
`group2` | In case our object has more than 2 groups, you should set the name of the group
`calculate.pvalues.probes` | In order to get the probes faster the user can select to calculate the pvalues only for the probes with a difference in DNA methylation. The default is to calculate to all probes. Possible values: "all", "differential". Default "all"
`plot.filename` | Filename. Default: volcano.pdf, volcano.svg, volcano.png. If set to FALSE, there will be no plot.
`ylab` | y axis text
`xlab` | x axis text
`title` | main title. If not specified it will be "Volcano plot (group1 vs group2)
`legend` | Legend title
`color` | vector of colors to be used in graph
`label` | vector of labels to be used in the figure. Example: c("Not Significant","Hypermethylated in group1", "Hypomethylated in group1"))
`xlim` | x limits to cut image
`ylim` | y limits to cut image
`p.cut` | p values threshold. Default: 0.01
`probe.names` | is probe.names
`diffmean.cut` | diffmean threshold. Default: 0.2
`paired` | Wilcoxon paired parameter. Default: FALSE
`adj.method` | Adjusted method for the p-value calculation
`overwrite` | Overwrite the pvalues and diffmean values if already in the object for both groups? Default: FALSE
`cores` | Number of cores to be used in the non-parametric test Default = groupCol.group1.group2.rda
`save` | Save object with results? Default: TRUE
`save.directory` | Directory to save the files. Default: working directory
`filename` | Name of the file to save the object.

### Value

Volcano plot saved and the given data with the results (diffmean.group1.group2,p.value.group1.group2, p.value.adj.group1.group2,status.group1.group2) in the rowRanges where group1 and group2 are the names of the groups

### Examples

    nrows <- 200; ncols <- 20
    counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
    rowRanges <- GenomicRanges::GRanges(rep(c("chr1", "chr2"), c(50, 150)),
                       IRanges::IRanges(floor(runif(200, 1e5, 1e6)), width=100),
                        strand=sample(c("+", "-"), 200, TRUE),
                        feature_id=sprintf("ID%03d", 1:200))
    colData <- S4Vectors::DataFrame(Treatment=rep(c("ChIP", "Input"), 5),
                        row.names=LETTERS[1:20],
                        group=rep(c("group1","group2"),c(10,10)))
    data <- SummarizedExperiment::SummarizedExperiment(
             assays=S4Vectors::SimpleList(counts=counts),
             rowRanges=rowRanges,
             colData=colData)
    SummarizedExperiment::colData(data)$group <- c(rep("group 1",ncol(data)/2),
                             rep("group 2",ncol(data)/2))
    hypo.hyper <- TCGAanalyze_DMR(data, p.cut = 0.85,"group","group 1","group 2")
    SummarizedExperiment::colData(data)$group2 <- c(rep("group_1",ncol(data)/2),
                             rep("group_2",ncol(data)/2))
    hypo.hyper <- TCGAanalyze_DMR(data, p.cut = 0.85,"group2","group_1","group_2")

[Package TCGAbiolinks version 2.14.0 Index]