rjmcmc {RJMCMCNucleosomes} | R Documentation |
Use of a fully Bayesian hierarchical model for chromosome-wide profiling of nucleosome positions based on high-throughput short-read data (MNase-Seq data). Beware that for a genome-wide profiling, each chromosome must be treated separatly. This function is optimized to run on segments that are smaller sections of the chromosome.
rjmcmc(reads, seqName = NULL, nbrIterations, kMax, lambda = 3, minInterval, maxInterval, minReads = 5, adaptIterationsToReads = TRUE, vSeed = -1, saveAsRDS = FALSE)
reads |
a |
seqName |
a |
nbrIterations |
a positive |
kMax |
a positive |
lambda |
a positive |
minInterval |
a |
maxInterval |
a |
minReads |
a positive |
adaptIterationsToReads |
a |
vSeed |
a |
saveAsRDS |
a |
a list
of class
"rjmcmcNucleosomes" containing:
call
the matched call.
k
a integer
, the final estimation of the number
of nucleosomes. 0
when no nucleosome is detected.
mu
a GRanges
containing the positions of the
nucleosomes and '*' as strand. The seqnames
of the GRanges
correspond to the seqName
input value. NA
when no nucleosome
is detected.
k_max
a integer
, the maximum number of nucleosomes
obtained during the iteration process. NA
when no nucleosome is
detected.
Rawane Samb, Pascal Belleau, Astrid Deschenes
## Loading dataset data(reads_demo_01) ## Nucleosome positioning, running both merge and split functions result <- rjmcmc(reads = reads_demo_01, seqName = "chr_SYNTHETIC", nbrIterations = 1000, lambda = 2, kMax = 30, minInterval = 146, maxInterval = 292, minReads = 5, vSeed = 10113, saveAsRDS = FALSE) ## Print the final estimation of the number of nucleosomes result$k ## Print the position of nucleosomes result$mu ## Print the maximum number of nucleosomes obtained during the iteration ## process result$k_max