rjmcmcCHR: Nucleosome positioning mapping on a large segment, up to a...

In ArnaudDroitLab/RJMCMCNucleosomes: Bayesian hierarchical model for genome-wide nucleosome positioning with high-throughput short-read data (MNase-Seq)

Description

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 an entire chromosome.

The function will process by splittingg the GRanges of reads (as example, the reads from a chromosome) in a list of smaller GRanges segments that can be run by the rjmcmc function. All those steps are done automatically.

Usage

rjmcmcCHR(reads, seqName = NULL, zeta = 147, delta, maxLength,
  nbrIterations, kMax, lambda = 3, minInterval, maxInterval, minReads = 5,
  adaptIterationsToReads = TRUE, vSeed = -1, nbCores = 1,
  dirOut = "out", saveAsRDS = FALSE, saveSEG = TRUE)

Arguments

reads	a GRanges, the forward and reverse reads that need to be segmented.
seqName	a character string containing the label of the chromosome, present in the GRanges object, that will be used. The NULL value is accepted when only one seqname is present in the GRanges; the only seqname present will be used. Default: NULL.
zeta	a positive integer or numeric, the length of the nucleosomes. Default: 147.
delta	a positive integer or numeric, the accepted range of overlapping section between segments. The overlapping section being zeta + delta.
maxLength	a positive integer or numeric, the length of each segment.
nbrIterations	a positive integer or numeric, the number of iterations. Non-integer values of nbrIterations will be casted to integer and truncated towards zero.
kMax	a positive integer or numeric, the maximum number of degrees of freedom per region. Non-integer values of kMax will be casted to integer and truncated towards zero.
lambda	a positive numeric, the theorical mean of the Poisson distribution. Default: 3.
minInterval	a numeric, the minimum distance between two nucleosomes.
maxInterval	a numeric, the maximum distance between two nucleosomes.
minReads	a positive integer or numeric, the minimum number of reads in a potential canditate region. Non-integer values of minReads will be casted to integer and truncated towards zero. Default: 5.
adaptIterationsToReads	a logical indicating if the number of iterations must be modified in function of the number of reads. Default: TRUE.
vSeed	a integer. A seed used when reproducible results are needed. When a value inferior or equal to zero is given, a random integer is used. Default: -1.
nbCores	a positive integer, the number of cores used to run in parallel. Default: 1.
dirOut	a character string. The name of the directory where 2 directories are created (if they don't already exists). The directory "dirOut/results" contents the rjmcmc results for each segment. The directory "dirOut/done" contents file a log file for each segment in RData format. If the log file for a segment is in the directory, the program considers that it is has been processed and run the next segment. Default: "out".
saveAsRDS	a logical. When TRUE, a RDS file containing the complete output of the rjmcmc function is created. Default: FALSE.
saveSEG	a logical. When TRUE, a RDS file containing the segments generated by segmentation function is saved in directory named from paramter dirOut. Default: FALSE.

Value

a list of class "rjmcmcNucleosomesBeforeAndAfterPostTreatment" containing:

• k a integer, the number of nucleosomes.

• mu a GRanges containing the positions of the nucleosomes.

• kPost a integer, the number of nucleosomes after post-treatment and '*' as strand. The seqnames of the GRanges correspond to the seqName input value. NA when no nucleosome is detected.

• muPost a GRanges containing the positions of the nucleosomes after post-treament and '*' as strand. The seqnames of the GRanges correspond to the seqName input value. NA when no nucleosome is detected.

Author(s)

Pascal Belleau, Astrid Deschenes

Examples

## Load synthetic dataset of reads
data(syntheticNucleosomeReads)

## Use dataset of reads to create GRanges object
sampleGRanges <- GRanges(syntheticNucleosomeReads$dataIP)

## Run nucleosome detection on the entire sample
## Not run: result <- rjmcmcCHR(reads = sampleGRanges, zeta = 147, delta=50,
maxLength=1200, nbrIterations = 1000, lambda = 3, kMax = 30,
minInterval = 146, maxInterval = 292, minReads = 5, vSeed = 10113,
nbCores = 2, saveAsRDS = FALSE)
## End(Not run)

ArnaudDroitLab/RJMCMCNucleosomes documentation built on May 5, 2019, 7:06 a.m.