makeCpGregions {DMRScan} | R Documentation |
Cluster CpGs together in regions based on proximity
makeCpGregions(observations, chr, pos, maxGap = 500, minCpG = 2)
observations |
Vector of corresponding observed T-value for each CpG, must be ordered in the same way as chr and pos |
chr |
Vector of chromosome location for each CpG |
pos |
Vector giving base pair position for each CpG If unsorted, use order(chr,pos) to sort the genomic positions within each chromosome. |
maxGap |
Maximum allowed base pair gap within a cluster. Default is set to 500. |
minCpG |
Minimum number of CpGs allowed in each region to be considered. Default is set to at least 2 CpGs within each region. |
The suplied observations ordered into into a RegionList object.
To be parsed further into dmrscan
data(DMRScan.methylationData) ## Load methylation data from chromosome 22 data(DMRScan.phenotypes) ## Load phenotype (end-point for methylation data) ## Test for an association between phenotype and Methylation testStatistics <- apply(DMRScan.methylationData,1,function(x,y) summary(glm(y ~ x, family = binomial(link = "logit")))$coefficients[2,3], y = DMRScan.phenotypes) ## Set chromosomal position to each test-statistic pos<- data.frame(matrix(as.integer(unlist(strsplit(names(testStatistics), split="chr|[.]"))), ncol = 3, byrow = TRUE))[,-1] ## Set clustering features minCpG <- 3 ## Minimum number of CpGs in a tested cluster ## Maxium distance (in base-pairs) within a cluster before it is ## broken up into two seperate cluster maxGap <- 750 regions <- makeCpGregions(observations = testStatistics, chr = pos[,1], pos = pos[,2], maxGap = maxGap, minCpG = minCpG)