TCGAanalyze_DMC: Differentially methylated regions Analysis

In BioinformaticsFMRP/TCGAbiolinks: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data

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 columns with the results from the object.

Usage

TCGAanalyze_DMC(
  data,
  groupCol = NULL,
  group1 = NULL,
  group2 = NULL,
  alternative = "two.sided",
  diffmean.cut = 0.2,
  paired = FALSE,
  adj.method = "BH",
  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,
  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
alternative	wilcoxon test alternative
diffmean.cut	diffmean threshold. Default: 0.2
paired	Wilcoxon paired parameter. Default: FALSE
adj.method	Adjusted method for the p-value calculation
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
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,
   dimnames = list(paste0("cg",1:200),paste0("S",1:20))
)
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)
)
names(rowRanges) <-  paste0("cg",1:200)
colData <- S4Vectors::DataFrame(
  Treatment = rep(c("ChIP", "Input"), 5),
  row.names = paste0("S",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)$group <- c(rep("group 1",ncol(data)/2),
                         rep("group 2",ncol(data)/2))
hypo.hyper <- TCGAanalyze_DMC(data, p.cut = 0.85,"group","group 1","group 2")
SummarizedExperiment::colData(data)$group2 <- c(rep("group_1",ncol(data)/2),
                         rep("group_2",ncol(data)/2))
hypo.hyper <- TCGAanalyze_DMC(
  data = data,
  p.cut = 0.85,
  groupCol = "group2",
  group1 = "group_1",
  group2 = "group_2"
)

BioinformaticsFMRP/TCGAbiolinks documentation built on April 12, 2024, 2:08 a.m.