Description Author(s) Examples
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.
Linghao SHEN <sl013@ie.cuhk.edu.hk>
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | # 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)
|
Loading required package: MCMCpack
Loading required package: coda
Loading required package: MASS
##
## Markov Chain Monte Carlo Package (MCMCpack)
## Copyright (C) 2003-2018 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park
##
## Support provided by the U.S. National Science Foundation
## (Grants SES-0350646 and SES-0350613)
##
Loading required package: mvtnorm
Loading required package: ellipse
[1] "Start..."
[1] "Parameter Estimation finished."
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