getSigRegion {MethCP} | R Documentation |
getSigRegion
returns the significant DMRs giving the segmented
MethCP
object.
getSigRegion( object, sig.level = 0.01, mean.coverage = 1, mean.diff = 0.1, nC.valid = 10)
object |
a |
sig.level |
significance level to call a region DMR. |
mean.coverage |
The minimum average coverage required for the reported DMRs. |
mean.diff |
The minimum differences between groups required for the reported DMRs. |
nC.valid |
number of valid cytosines required for the reported DMRs. |
a data.frame
containing the DMRs.
library(bsseq) # Simulate a small dataset with 2000 cyotsine and 6 samples, # 3 in the treatment group and 3 in the control group. The # methylation ratio are generated using Binomial distribution # with probability 0.3. nC <- 2000 sim_cov <- rnbinom(6*nC, 5, 0.5) + 5 sim_M <- vapply( sim_cov, function(x) rbinom(1, x, 0.3), FUN.VALUE = numeric(1)) sim_cov <- matrix(sim_cov, ncol = 6) sim_M <- matrix(sim_M, ncol = 6) # methylation ratios in the DMRs in the treatment group are # generated using Binomial(0.7) DMRs <- c(600:622, 1089:1103, 1698:1750) sim_M[DMRs, 1:3] <- vapply( sim_cov[DMRs, 1:3], function(x) rbinom(1, x, 0.7), FUN.VALUE = numeric(1)) # sample names sample_names <- c(paste0("treatment", 1:3), paste0("control", 1:3)) colnames(sim_cov) <- sample_names colnames(sim_M) <- sample_names # create a bs.object bs_object <- BSseq(gr = GRanges( seqnames = "Chr01", IRanges(start = (1:nC)*10, width = 1)), Cov = sim_cov, M = sim_M, sampleNames = sample_names) DMRs_pos <- DMRs*10 methcp_obj1 <- calcLociStat( bs_object, group1 = paste0("treatment", 1:3), group2 = paste0("control", 1:3), test = "DSS") methcp_obj1 <- segmentMethCP( methcp_obj1, bs_object, region.test = "weighted-coverage", mc.cores = 1) methcp_res1 <- getSigRegion(methcp_obj1)