DMRMark-package {DMRMark} | R Documentation |

##
DMR Detection by Non-Homogeneous Hidden Markov Model from Methylation Array Data

### Description

Perform differential analysis for
methylation array data. DMRMark detects differentially
methylated regions (DMRs) from array M-values.
The core of DSS is a Non-homogeneous Hidden Markov Model
for estimating spatial correlation and a novel Constrained
Gaussian Mixture Model for modeling the M-value pairs of each individual locus.

DMRMark only works for two-group comparisons currently. We have the plan to
extend the transition and response model that make then suitable for complex
experimental designs in the future.

### Author(s)

Linghao SHEN <sl013@ie.cuhk.edu.hk>

### Examples

# DMR detection performed on chr18 of a small BLCA dataset from TCGA
data(BLCA)
# Use a small subset
nprobe <- 500
# M-values
mv <- BLCA$mv[1:nprobe,]
# Distance between probes, L<0 indicates acorssing chromosomes
L = BLCA$distance[1:nprobe]
# Initialize new chain when probe distance too long
# or across different chromosomes
newChains <- which((L > 100000) | L < 0)
# The starting positions of new chains
starting <- c(1, newChains[-length(newChains)]+1)
# Run DMRMark with default options
set.seed(0)
par <- DMRMark(mv, L, starting)
# Get the posterior of being certain states
# Return the result of DMC for plotting by setting 'region=FALSE'
results <- DMRViterbi(mv, par, L, starting, region=FALSE)
# The MAP states being 3 or 4 indicate DMCs
isDMC <- (results$states > 2) + 0
mvScatter(mv, isDMC, nPlot=10000)

[Package

*DMRMark* version 1.1.1

Index]