DMRViterbi: Viterbi algorithm to estimate posterior probabilities of...

In DMRMark: DMR Detection by Non-Homogeneous Hidden Markov Model from Methylation Array Data

Description

This function takes M-values and estimated parameters from 'DMRMark', then uses Viterbi algorithm for estimating states' posterior probabilities for each locus.

Usage

DMRViterbi(mv, pars, L = rep(1, nrow(mv)), starting = NULL, 
			pd = NULL, region = TRUE, 
			orderBy = c("max", "mean", "median", "min"), VitP = NULL)

Arguments

mv	The input M-values matrix, NA is not allowed.
pars	The list of model parameters. Getting by calling 'DMRMark'.
L	A vector to specify the distance between each probes in bp. $L < 0$ represents change of chromosome. Default is $L = 1$ for all probes.
starting	A vector to specify the position to initial new chains. We suggest new chains should be initiated at least at starting of new chromosome. When it is null, new chains initiate at beginning and where $L > 100000 or $L < 0$.
pd	A design matrix, which can be generated by 'stats::model.matrix'. If the M-values are totally paired or single paired, just leave it to be NULL.
region	If set to TRUE, this function returns the regions formed by Viterbi posterior states. Otherwise, it returns posterior probabilities and states for individual loci. Default is TRUE.
orderBy	Only enabled when 'region = TRUE'. Order the regions by which statistics? Choice include 'max', 'mean', 'median' and 'min', which orders the regions by the maximum, geometric mean, median or minimum of the posterior probabilities in each region respectively. Default is 'max'.
VitP	Only enabled when 'region = FALSE'. The minimum posterior probabilities required to be the DMC states. A locus with DMC's posterior probability lower than 'VitP' will in the non-DMC states with highest probabilities. When set to NULL, simply return the MAP states. Default is NULL.

Value

If 'region = FALSE', the return value is a list contains:

states	The MAP methylation status satisfies the 'VitP'.
deltas	The matrix with each row corresponds to the posterior probabilities of each locus in which states.

If 'region = TRUE', the return value is a dataframe with the following fields:

begin	Beginning of each region. In probe index.
ending	Ending of each region. In probe index.
MAP_state	The MAP state of each region.
minVP	The minimum Viterbi posterior probability of the MAP state in each region
meanVP	The geometric mean of Viterbi posterior probability of the MAP state in each region
maxVP	The maximum Viterbi posterior probability of the MAP state in each region
midVP	The median Viterbi posterior probability of the MAP state in each region

Author(s)

Linghao SHEN <[e-mail]>

See Also

See DMRMark about model parameter estimation

Examples

	# DMRViterbi
	# 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
	pars <- DMRMark(mv, L, starting)
	# Get the posterior of being certain states
	results <- DMRViterbi(mv, pars, L, starting)
	head(results)

DMRMark documentation built on May 2, 2019, 1:53 p.m.