NamesDeltaBetaCut: Extraction of CpG-probes IDs or indices based on delta...

In LionelRohner/LRTools: A collection of functions written for analyzing Illumina HM450 and EPIC data

Description

The purpose of this function is to find the row names or indices of the CpG probe that have a certain absolute delta beta-value difference between two groups of choice. The function is restricted to comparison of two groups.

Usage

NamesDeltaBetaCut(
  pd,
  feature,
  beta,
  group1_Name,
  group2_Name,
  deltaBetaThreshold = 0.2,
  returnIndeces = F,
  sampleNames = "Sample_Name"
)

Arguments

pd	A minfi- or ChAMP-type sample sheet.
feature	The name of the group variable as it appears in the sample sheet.
beta	A beta-value matrix, where rows are CpG probes and columns are samples.
group1_Name	The string representation of the factor corresponding to group 1 in the group variable "feature".
group2_Name	Same as above, but for the other group.
deltaBetaThreshold	The desired delta beta-value threshold. The delta beta-value is the mean difference in beta-values between two groups. The value is converted to an absolute value. The default is 0.2.
returnIndeces	By setting "returnIndices" to "TRUE" the function will return the indices of the CpG probes in the original beta-value matrix instead of the row names. Is set to "FALSE" by default.
sampleNames	If the column containing the sample names in the pd is not named "Sample_Name" (ChAMP default naming convention), the column name has to be added separately. By default the function assumes the name to be "Sample_Name".

Value

A vector containing the row names (by default the CpG-probe ID) or indices of CpG-probes that have a user-defined delta beta difference between two groups of interest.

Examples

# create some random matrix
A <- data.frame(runif(1000,0.2,1),runif(1000,0.3,1),runif(1000,0.5,0.99),
                runif(1000,0.1,0.7),runif(1000,0.2,0.6),runif(1000,0,0.6))

rownames(A) <- paste("cg",sample(10000000:99999999,1000,replace = FALSE), sep ="")
colnames(A) <- rep(paste("Sample_",1:6,sep=""))

pd <- cbind.data.frame(colnames(A),c(rep("Group_1",3),rep("Group_2",3)))
colnames(pd) <- c("Sample_Names","Groups")

inclusion <- NamesDeltaBetaCut(pd = pd,
                               feature = pd$Groups,
                               beta = A,
                               group1_Name = "Group_1",
                               group2_Name = "Group_2",
                               deltaBetaThreshold = 0.2,
                               returnIndeces = FALSE,
                               sampleNames = "Sample_Names")

# Plot results
A_filtered <- A[rownames(A) %in% inclusion,]
Group1 <- paste("^",pd[which(pd$Groups == "Group_1"), "Sample_Names"],"$",
                collapse = "|", sep = "")
Group2 <- paste("^",pd[which(pd$Groups == "Group_2"), "Sample_Names"],"$",
                collapse = "|", sep = "")

meanG1 <- rowMeans(A[,grep(Group1, colnames(A))])
meanG2 <- rowMeans(A[,grep(Group2, colnames(A))])

hist(meanG1-meanG2, xlab = "delta beta-values",
     main = "All (grey) vs. filtered delta beta-values (red)")

meanG1_filtered <- rowMeans(A_filtered[,grep(Group1, colnames(A_filtered))])
meanG2_filtered <- rowMeans(A_filtered[,grep(Group2, colnames(A_filtered))])

hist(meanG1_filtered-meanG2_filtered, col = rgb(0.8,0.1,0.1,0.3),add = TRUE)

LionelRohner/LRTools documentation built on Dec. 17, 2021, 1:10 a.m.