Package 'epigraHMM' reference manual

Title:	Epigenomic R-based analysis with hidden Markov models
Description:	epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological or technical replicates, and one for differential peaks from multi-replicate multi-condition experiments. In differential peak calling, epigraHMM provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions.
Authors:	Pedro Baldoni [aut, cre]
Maintainer:	Pedro Baldoni <[email protected]>
License:	MIT + file LICENSE
Version:	1.15.0
Built:	2024-11-29 08:19:17 UTC
Source:	https://github.com/bioc/epigraHMM

Add offsets to epigraHMMDataSet

Description

This function adds model offsets to epigraHMMDataSet

Usage

addOffsets(object, offsets)
addOffsets(object, offsets)

Arguments

`object`	an epigraHMMDataSet
`offsets`	a matrix with model offsets

Details

To be added

Value

An epigraHMMDataSet with an 'offsets' assay filled in.

References

https://github.com/plbaldoni/epigraHMM

Examples


# Creating dummy object
countData <- list('counts' = matrix(rpois(4e5,10),ncol = 4),
'controls' = matrix(rpois(4e5,5),ncol = 4))
colData <- data.frame(condition = c('A','A','B','B'), replicate = c(1,2,1,2))
object <- epigraHMMDataSetFromMatrix(countData,colData)

# Adding pre-computed offsets
object <- addOffsets(object = object,
                     offsets = matrix(rnorm(4e5),ncol = 4))

# Creating dummy object
countData <- list('counts' = matrix(rpois(4e5,10),ncol = 4),
'controls' = matrix(rpois(4e5,5),ncol = 4))
colData <- data.frame(condition = c('A','A','B','B'), replicate = c(1,2,1,2))
object <- epigraHMMDataSetFromMatrix(countData,colData)

# Adding pre-computed offsets
object <- addOffsets(object = object,
                     offsets = matrix(rnorm(4e5),ncol = 4))

Extract posterior probabilities (or combinatorial patterns) associated with differential regions

Description

Given results from epigraHMM's differential peak caller, this function will output either posterior probabilities or combinatorial patterns associated with the mixture components of the embedded mixture model.

Usage

callPatterns(
  object,
  peaks,
  hdf5 = metadata(object)$output,
  type = "all",
  fdr = NULL,
  pattern = NULL,
  ranges = NULL
)
callPatterns(
  object,
  peaks,
  hdf5 = metadata(object)$output,
  type = "all",
  fdr = NULL,
  pattern = NULL,
  ranges = NULL
)

Arguments

`object`	an epigraHMMDataSet
`peaks`	a GRanges object with differential peaks from 'callPeaks'
`hdf5`	a character with the location of the epigraHMM HDF5 output file
`type`	a character string that defines which output will be givem (see details; default is 'all')
`fdr`	the desired fdr thresholding level to define combinatorial patterns
`pattern`	a string that explicitly specifies the combinatorial pattern to be output
`ranges`	a GRanges object with the genomic ranges to subset the output

Details

The output of 'callPatterns' is always restricted to genomic windows intersecting peaks.

If ‘type = ’all'‘, all windows’ posterior probabilities associated with all differential combinatorial patterns are returned. If ‘type = ’fdr'', users must also specify the input argument 'pattern' and this function will output windows wich are associated with the given 'pattern' that pass a particular fdr threshold level. If ‘type = ’max'', this function will output the combinatorial pattern which has the maximal posterior probability for each window. If ‘type = ’ranges'', the windows that are output are restricted to those that intersect the 'ranges' input argument.

Value

A GRanges object with metadata

Author(s)

Pedro L. Baldoni, [email protected]

References

https://github.com/plbaldoni/epigraHMM

Examples

# Creating dummy object
countData <- cbind(rbind(matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 10, size = 5), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 10, size = 5), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1)),
                   rbind(matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 10, size = 5), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 10, size = 5), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1)))

colData <- data.frame(condition = c('A','B'), replicate = c(1,1))
rowRanges <- GenomicRanges::GRanges('chrA',
                     IRanges::IRanges(start = seq(1,by = 500,
                     length.out = nrow(countData)),width = 500))

object <- epigraHMMDataSetFromMatrix(countData,colData,rowRanges = rowRanges)

# Initializing
object <- initializer(object,controlEM())

# Running epigraHMM
object <- epigraHMM(object,controlEM(),type = 'differential',dist = 'nb')

# Calling peaks
peaks <- callPeaks(object = object,
                  hdf5 = S4Vectors::metadata(object)$output,
                  method = 'viterbi')
                  
# Extracting posterior probabilities
patterns <- callPatterns(object = object,peaks = peaks,type = 'max')

# Creating dummy object
countData <- cbind(rbind(matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 10, size = 5), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 10, size = 5), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1)),
                   rbind(matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 10, size = 5), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 10, size = 5), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1)))

colData <- data.frame(condition = c('A','B'), replicate = c(1,1))
rowRanges <- GenomicRanges::GRanges('chrA',
                     IRanges::IRanges(start = seq(1,by = 500,
                     length.out = nrow(countData)),width = 500))

object <- epigraHMMDataSetFromMatrix(countData,colData,rowRanges = rowRanges)

# Initializing
object <- initializer(object,controlEM())

# Running epigraHMM
object <- epigraHMM(object,controlEM(),type = 'differential',dist = 'nb')

# Calling peaks
peaks <- callPeaks(object = object,
                  hdf5 = S4Vectors::metadata(object)$output,
                  method = 'viterbi')
                  
# Extracting posterior probabilities
patterns <- callPatterns(object = object,peaks = peaks,type = 'max')

Summarize peak calls and optionally create a BED 6+3 file in broadPeak format for visualization

Description

This function imports the output from 'epigraHMM' and outputs a set of peaks (consensus or differential) for a given FDR control threshold or Viterbi sequence.

Usage

callPeaks(
  object,
  hdf5 = metadata(object)$output,
  method = "viterbi",
  saveToFile = FALSE,
  control = NULL
)
callPeaks(
  object,
  hdf5 = metadata(object)$output,
  method = "viterbi",
  saveToFile = FALSE,
  control = NULL
)

Arguments

`object`	an epigraHMMDataSet
`hdf5`	a character with the location of the epigraHMM HDF5 output file
`method`	either 'viterbi' or a numeric FDR control threshold (e.g. 0.05). Default is 'viterbi'.
`saveToFile`	a logical indicating whether or not to save the results to file. Output files are always saved with peaks of interest defined on the region level. Default is FALSE.
`control`	list of control arguments from controlEM(). This is an optional parameter and it is only required when 'saveToFile = TRUE' so that the output directory can be obtained. Default is NULL.

Value

A GRanges object with differential peak calls in BED 6+3 format

Author(s)

Pedro L. Baldoni, [email protected]

References

https://github.com/plbaldoni/epigraHMM

Examples


# Creating dummy object
countData <- rbind(matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1),
                   matrix(rnbinom(2e3,mu = 7.5,size = 5),ncol = 1),
                   matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1))

colData <- data.frame(condition = 'A', replicate = 1)

rowRanges <- GenomicRanges::GRanges('chrA',
IRanges::IRanges(start = seq(from = 1, length.out = 4e3,by = 250),width = 250))

object <- epigraHMMDataSetFromMatrix(countData,colData,rowRanges)

# Initializing
object <- initializer(object,controlEM())

# Running epigraHMM
object <- epigraHMM(object,controlEM(),type = 'consensus',dist = 'nb')

# Calling peaks
peaks <- callPeaks(object = object,
                   hdf5 = S4Vectors::metadata(object)$output,
                   method = 'viterbi')

# Creating dummy object
countData <- rbind(matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1),
                   matrix(rnbinom(2e3,mu = 7.5,size = 5),ncol = 1),
                   matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1))

colData <- data.frame(condition = 'A', replicate = 1)

rowRanges <- GenomicRanges::GRanges('chrA',
IRanges::IRanges(start = seq(from = 1, length.out = 4e3,by = 250),width = 250))

object <- epigraHMMDataSetFromMatrix(countData,colData,rowRanges)

# Initializing
object <- initializer(object,controlEM())

# Running epigraHMM
object <- epigraHMM(object,controlEM(),type = 'consensus',dist = 'nb')

# Calling peaks
peaks <- callPeaks(object = object,
                   hdf5 = S4Vectors::metadata(object)$output,
                   method = 'viterbi')

Remove effects from covariates of interest

Description

This function removes the effect from covariates of interest (such as GC content) from experimental counts

Usage

cleanCounts(object, effectNames, byNames = NULL, log = TRUE)
cleanCounts(object, effectNames, byNames = NULL, log = TRUE)

Arguments

`object`	an epigraHMMDataSet
`effectNames`	a character vector with the names of assays for which the effect will be removed from the experimental counts. Names in 'effectNames' must be assays stored in the epigraHMMDataSet 'object'.
`byNames`	a character vector with the name of an assay containing stratification variables which will be used to define stratum-specific effects. Examples of byNames assays include the 'peaks' assay from 'initializer()'. In this case, models will be fit separately for peaks and non-peaks regions. This can be useful for effects such as GC content, which are known to have a differential effect between peaks and non-peak regions. Default is NULL, i.e., effects will be removed without stratification.
`log`	a logical indicating if the effect from 'effectNames' should be log-transformed in the regression model (default is TRUE)

Value

An epigraHMMDataSet with an 'offset' assay filled in.

Author(s)

Pedro L. Baldoni, [email protected]

References

https://github.com/plbaldoni/epigraHMM

Examples


# Creating dummy object
gc <- rbeta(3e3,50,50)

countData <- list('counts' = rbind(matrix(rnbinom(2e3,mu = 7.5,size = 10),ncol = 1),
                                   matrix(rnbinom(3e3,mu = exp(0.5 + 8*gc),size = 5),ncol = 1),
                                   matrix(rnbinom(2e3,mu = 7.5,size = 10),ncol = 1)),
                  'gc' = matrix(c(rbeta(2e3,50,50),gc,rbeta(2e3,50,50)),ncol = 1))

colData <- data.frame(condition = 'A', replicate = 1)
object <- epigraHMMDataSetFromMatrix(countData,colData)

# Initializing
object <- initializer(object = object,controlEM())

# Cleaning counts
object <- cleanCounts(object = object,effectNames = 'gc',byNames = 'peaks')

# Plotting the cleaned data
#par(mfrow = c(2,1))
#smoothScatter(log1p(assay(object))~assay(object,'gc'),xlab = 'gc',ylab = 'log counts')
#smoothScatter(as.numeric(log(assay(object)+1) - assay(object,'offsets'))~assay(object,'gc'),
#              xlab = 'gc',ylab = 'log cleaned counts')

# Creating dummy object
gc <- rbeta(3e3,50,50)

countData <- list('counts' = rbind(matrix(rnbinom(2e3,mu = 7.5,size = 10),ncol = 1),
                                   matrix(rnbinom(3e3,mu = exp(0.5 + 8*gc),size = 5),ncol = 1),
                                   matrix(rnbinom(2e3,mu = 7.5,size = 10),ncol = 1)),
                  'gc' = matrix(c(rbeta(2e3,50,50),gc,rbeta(2e3,50,50)),ncol = 1))

colData <- data.frame(condition = 'A', replicate = 1)
object <- epigraHMMDataSetFromMatrix(countData,colData)

# Initializing
object <- initializer(object = object,controlEM())

# Cleaning counts
object <- cleanCounts(object = object,effectNames = 'gc',byNames = 'peaks')

# Plotting the cleaned data
#par(mfrow = c(2,1))
#smoothScatter(log1p(assay(object))~assay(object,'gc'),xlab = 'gc',ylab = 'log counts')
#smoothScatter(as.numeric(log(assay(object)+1) - assay(object,'offsets'))~assay(object,'gc'),
#              xlab = 'gc',ylab = 'log cleaned counts')

Control parameters for the EM algorithm from epigraHMM

Description

This function passes controlling parameters for the EM algorithm implemented in the epigraHMM package.

Usage

controlEM(
  epsilonEM = c(MRCPE = 0.001, MACPE = 0.001, ARCEL = 0.001),
  maxIterEM = 500,
  minIterEM = 3,
  gapIterEM = 3,
  maxCountEM = 3,
  maxDisp = 1000,
  criterion = "all",
  minZero = .Machine$double.xmin,
  probCut = 0.05,
  quiet = TRUE,
  maxIterInnerEM = 5,
  epsilonInnerEM = 0.001,
  trimOffset = 3,
  pattern = NULL,
  tempDir = tempdir(),
  fileName = "epigraHMM",
  pruningThreshold = NULL,
  quietPruning = TRUE
)
controlEM(
  epsilonEM = c(MRCPE = 0.001, MACPE = 0.001, ARCEL = 0.001),
  maxIterEM = 500,
  minIterEM = 3,
  gapIterEM = 3,
  maxCountEM = 3,
  maxDisp = 1000,
  criterion = "all",
  minZero = .Machine$double.xmin,
  probCut = 0.05,
  quiet = TRUE,
  maxIterInnerEM = 5,
  epsilonInnerEM = 0.001,
  trimOffset = 3,
  pattern = NULL,
  tempDir = tempdir(),
  fileName = "epigraHMM",
  pruningThreshold = NULL,
  quietPruning = TRUE
)

Arguments

`epsilonEM`	a named vector of positive values specifying up to four possible convergence criterion tolerances for the EM algorithm (see 'criterion' below). Default is c('MRCPE' = 1e-3, 'MACPE' = 1e-3,'ARCEL' = 1e-3).
`maxIterEM`	a positive integer giving the maximum number of EM iterations. Default is 500.
`minIterEM`	a positive integer giving the minimum number of EM iterations to start evaluating the convergence. Default is 3.
`gapIterEM`	a positive integer giving the number of EM iterations apart to compute the convergence criterion. Default is 3.
`maxCountEM`	a positive integer giving the number of consecutive EM iterations satisfying the convergence criterion in order to stop the algorithm. Default is 3.
`maxDisp`	a positive value for the upper limit constraint of the dispersion parameters. Default is 1000.
`criterion`	a character specifying the convergence criterion. Either "MRCPE" (maximum absolute relative change in parameter estimates), "MACPE" (maximum absolute change of parameter estimates), "ARCEL" (absolute relative change of the Q-function), or "all" (simultaneously check for MRCPE, MACPE, and ARCEL). Default is "all".
`minZero`	a positive value for the minimum positive value allowed in computations to avoid having zeros. Default is .Machine$double.xmin.
`probCut`	a number between 0 and 1 for the cutoff of the rejection controlled EM algorithm. Default 0.05.
`quiet`	a logical indicating whether to print messages. Default is TRUE.
`maxIterInnerEM`	a positive integer giving the maximum number of inner EM iterations. Default is 5.
`epsilonInnerEM`	a positive value with the convergence tolerance value for the inner EM algorithm. The criterion for the inner EM is "MRCPE". Default is 1e-3.
`trimOffset`	either NULL or a positive integer indicating the number of decimal places to be used in the offset. Default is 3.
`pattern`	either NULL (the default) or a list with length equal to the number of differential patterns to be modeled by the differential HMM state. See Details section below.
`tempDir`	a string where results will be saved. Default is 'tempdir()'.
`fileName`	a string with the name of the result files. Default is 'epigraHMM'.
`pruningThreshold`	a numeric value between 0 and 1 to consider when pruning rare combinatorial patterns. Default is NULL (see Details).
`quietPruning`	a logical indicating whether to print messages during the pruning step. Default is TRUE.

Details

If pattern is NULL, every possible combinatorial pattern will be considered. If pattern is a list, elements of it should specify the differential patterns to be modeled by each mixture component. For instance, if pattern = list(2,c(1,3)) the mixture model will have two components that will represent the enrichment of condition 2 alone and the enrichment of conditions 1 and 3 together.

If pruningThreshold is a value between 0 and 1, say 0.05, epigraHMM will sequentially remove differential combinatorial patterns of enrichment from any mixture model component with associated posterior mixture proportion less than 0.05.

Value

A list with components equal to the arguments

Author(s)

Pedro L. Baldoni, [email protected]

References

https://github.com/plbaldoni/epigraHMM

Examples

# No more than 100 EM iterations
control <- controlEM(maxIterEM = 100)

# No more than 100 EM iterations
control <- controlEM(maxIterEM = 100)

Perform peak calling of epigenomic data sets

Description

This function runs either consensus (one condition, multiple samples) or differential (multiple conditions and samples) peak callers for epigenomic data.

Usage

epigraHMM(object, control, type, dist = "nb")
epigraHMM(object, control, type, dist = "nb")

Arguments

`object`	an epigraHMMDataSet
`control`	list of control arguments from `controlEM`
`type`	character, either `"consensus"` or `"differential"`
`dist`	character, either `"zinb"` or `"nb"` (default)

Value

An epigraHMMDataSet object with the results from epigraHMM

Author(s)

Pedro L. Baldoni, [email protected]

References

https://github.com/plbaldoni/epigraHMM

Examples


# Creating dummy object
countData <- rbind(matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1),
                   matrix(rnbinom(2e3,mu = 7.5,size = 5),ncol = 1),
                   matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1))

colData <- data.frame(condition = 'A', replicate = 1)
object <- epigraHMMDataSetFromMatrix(countData,colData)

# Initializing
object <- initializer(object,controlEM())

# Running epigraHMM
object <- epigraHMM(object,controlEM(),type = 'consensus',dist = 'nb')

# Creating dummy object
countData <- rbind(matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1),
                   matrix(rnbinom(2e3,mu = 7.5,size = 5),ncol = 1),
                   matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1))

colData <- data.frame(condition = 'A', replicate = 1)
object <- epigraHMMDataSetFromMatrix(countData,colData)

# Initializing
object <- initializer(object,controlEM())

# Running epigraHMM
object <- epigraHMM(object,controlEM(),type = 'consensus',dist = 'nb')

Create a epigraHMMDataSet from a set of BAM files

Description

This function creates a RangedSummarizedExperiment object from of a set of BAM files. It is used to store the input data, the model offsets, and the results from the peak calling algorithms.

Usage

epigraHMMDataSetFromBam(
  bamFiles,
  colData,
  genome,
  windowSize,
  gapTrack = TRUE,
  blackList = TRUE
)
epigraHMMDataSetFromBam(
  bamFiles,
  colData,
  genome,
  windowSize,
  gapTrack = TRUE,
  blackList = TRUE
)

Arguments

`bamFiles`	a string vector (or a list of string vectors) with the path for BAM files. If bamFiles is a list of string vectors, vectors must be named, have the same dimension, and, at least, a vector with name 'counts' must exist (see details).
`colData`	a `data.frame` with the experimental data. It must contain the columns `condition` and `replicate`. `condition` refers to the experimental condition identifier (e.g. cell line name). `replicate` refers to the replicate identification number (unique for each condition).
`genome`	either a single string with the name of the reference genome (e.g. 'hg19') or a GRanges object with ranges to be tilled into a set of non-overlapping windows.
`windowSize`	an integer specifying the size of genomic windows where read counts will be computed.
`gapTrack`	either a logical (`TRUE`, the default, or `FALSE`) or a GRanges object with gap regions of the genome to be excluded. If `TRUE`, the function will discard genomic coordinates overlapping regions present in the UCSC gap table of the respective reference genome (if available). See Details section below.
`blackList`	either a logical (`TRUE`, the default, or `FALSE`) or a GRanges object with blacklisted regions of the genome to be excluded. If `TRUE`, the function will discard ENCODE blacklisted regions from selected reference genomes (if available). See Details section below.

Details

The index ".bai" files must be stored in the same directory of their respective BAM files. The index files must be named after their respective BAM files with the additional ".bai" suffix.

‘epigraHMMDataSetFromBam' will store experimental data (e.g. ChIP-seq counts) from bamFiles (or bamFiles[[’counts']], if a list is provided). Additional data (e.g. input control counts) will be stored similarly with their respective list names.

By default, the function computes read counts using csaw's estimated fragment length via cross correlation analysis. For experimental counts (e.g. ChIP-seq), sequencing reads are shifted downstream half of the estimated fragment length. For additional counts (e.g. input control), sequencing reads are not shifted prior to counting.

Additional columns included in the colData input will be passed to the resulting epigraHMMDataSet assay and can be acessed via colData() function.

The genome argument will call GenomeInfoDb::Seqinfo() to fetch the chromosome lengths of the specified genome. See ?GenomeInfoDb::Seqinfo for the list of UCSC genomes that are currently supported.

If gapTrack = TRUE and the name of a reference genome is passed as input through genome (e.g. 'hg19'), the function will discard any genomic coordinate overlapping regions specified by the respective UCSC gap table. If gapTrack is a GRanges object, the function will discard any genomic coordinate overlaping regions from gapTrack.

If blackList = TRUE and the name of a reference genome is passed as input through genome (e.g. 'hg19'), The function will fetch the manually curated blacklist tracks (Version 2) from https://github.com/Boyle-Lab/Blacklist/tree/master/lists. Current available genomes are ce10, dm3, hg19, hg38, and mm10. If blackList is a GRanges object, the function will discard any genomic coordinate overlaping regions from blackList.

Value

An epigraHMMDataSet object with sorted colData regarding conditions and replicates. Experimental counts will be stored in the 'counts' assay in the resulting epigraHMMDataSet object. Additional experimental data will be stored with their respective names from the list bamFiles.

Author(s)

Pedro L. Baldoni, [email protected]

References

https://github.com/plbaldoni/epigraHMM DOI: 10.1093/nar/gkv1191 DOI: 10.1038/s41598-019-45839-z DOI: 10.1038/nature11247

Examples

bamFiles <- system.file("extdata","euratrans",
                        "lv-H3K27me3-SHR-male-bio2-tech1.bam",
                        package="chromstaRData")
                        
colData <- data.frame(condition = 'SHR', replicate = 1)

object <- epigraHMMDataSetFromBam(bamFiles = bamFiles,
                                  colData = colData,
                                  genome = 'rn4',
                                  windowSize = 25000,
                                  gapTrack = TRUE,
                                  blackList = TRUE)

bamFiles <- system.file("extdata","euratrans",
                        "lv-H3K27me3-SHR-male-bio2-tech1.bam",
                        package="chromstaRData")
                        
colData <- data.frame(condition = 'SHR', replicate = 1)

object <- epigraHMMDataSetFromBam(bamFiles = bamFiles,
                                  colData = colData,
                                  genome = 'rn4',
                                  windowSize = 25000,
                                  gapTrack = TRUE,
                                  blackList = TRUE)

Create a epigraHMMDataSet from matrices of counts

Description

This function creates a RangedSummarizedExperiment object from matrices of counts. It is used to store the input data, the model offsets, and the results from the peak calling algorithms.

Usage

epigraHMMDataSetFromMatrix(countData, colData, rowRanges = NULL)
epigraHMMDataSetFromMatrix(countData, colData, rowRanges = NULL)

Arguments

`countData`	a matrix (or a list of matrices). If countData is a list of matrices, matrices must be named, have the same dimensions, and, at least, a matrix with name 'counts' must exist (see details).
`colData`	a `data.frame` with columns `condition` and `replicate`. `condition` refers to the experimental condition identifier (e.g. cell line name). `replicate` refers to the replicate identification number (unique for each condition).
`rowRanges`	an optional GRanges object with the genomic coordinates of the `countData`

Details

Additional columns included in the colData input will be passed to the resulting epigraHMMDataSet assay and can be acessed via colData() function.

Value

An epigraHMMDataSet object with sorted colData regarding conditions and replicates. Experimental counts will be stored in the 'counts' assay in the resulting epigraHMMDataSet object. If ‘countData' is a list of matrices, the resulting ’counts' assay will be equal to ‘countData[[’counts']]'.

Additional matrices can be included in the epigraHMMDataSet. For example, if one wants to include counts from an input control experiment from ‘countData[[’controls']]‘, an assay ’control' will be added to the resulting epigraHMMDataSet..

Author(s)

Pedro L. Baldoni, [email protected]

References

https://github.com/plbaldoni/epigraHMM

Examples


countData <- list('counts' = matrix(rpois(4e5,10),ncol = 4),
'controls' = matrix(rpois(4e5,5),ncol = 4))
colData <- data.frame(condition = c('A','A','B','B'), replicate = c(1,2,1,2))
object <- epigraHMMDataSetFromMatrix(countData,colData)

countData <- list('counts' = matrix(rpois(4e5,10),ncol = 4),
'controls' = matrix(rpois(4e5,5),ncol = 4))
colData <- data.frame(condition = c('A','A','B','B'), replicate = c(1,2,1,2))
object <- epigraHMMDataSetFromMatrix(countData,colData)

Estimate transition probability from a sequence of integers

Description

This function estimates the transition probabilities for a k-state Markov chain based on a sequence of integers that represent states of the chain

Usage

estimateTransitionProb(chain, numStates)
estimateTransitionProb(chain, numStates)

Arguments

`chain`	a vector of integers
`numStates`	an integer, the number of states in the Markov chain

Value

A k-by-k matrix of transition probabilities, such that k is the number of states of the chain

References

https://github.com/plbaldoni/epigraHMM

Examples


trueMat <- matrix(c(0.9,0.1,0.1,0.9),2,2)
simChain <- simulateMarkovChain(trueMat,1e3)
estMat <- estimateTransitionProb(simChain,2)

# estMat should be close to trueMat
estMat

trueMat <- matrix(c(0.9,0.1,0.1,0.9),2,2)
simChain <- simulateMarkovChain(trueMat,1e3)
estMat <- estimateTransitionProb(simChain,2)

# estMat should be close to trueMat
estMat

E-step of HMM (forward-backward probability + posterior probability calculation)

Description

E-step of HMM (forward-backward probability + posterior probability calculation)

Usage

expStep(pi, gamma, logf, hdf5)
expStep(pi, gamma, logf, hdf5)

Arguments

`pi`	a vector of probabilities (sum of probabilities should sum to one)
`gamma`	a matrix of transition probabilities (row sums should be one)
`logf`	a matrix of observed log-likelihood values. Columns represent hidden states, rows represent genomic regions
`hdf5`	path to where the hdf5 is saved

Examples

#Creating dummy object
countData <- rbind(matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1),
                  matrix(rnbinom(2e3,mu = 7.5,size = 5),ncol = 1),
                  matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1))



colData <- data.frame(condition = 'A', replicate = 1)
object <- epigraHMMDataSetFromMatrix(countData,colData)

#Initializing
object <- initializer(object,controlEM())

#Running epigraHMM
object <- epigraHMM(object,controlEM(),type = 'consensus',dist = 'nb')

#Example
expStep(pi = c(0.99,0.02),
       gamma = matrix(c(0.99,0.01,0.01,0.99),nrow = 2),
       logf = cbind(dnbinom(rnbinom(100,mu = 2,size = 10),mu = 2,size = 10,log = TRUE),
                    dnbinom(rnbinom(100,mu = 7.5,size = 5),mu = 7.5,size = 5,log = TRUE)),
       hdf5 = file.path(tempdir(),'tmp.h5'))
#Creating dummy object
countData <- rbind(matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1),
                  matrix(rnbinom(2e3,mu = 7.5,size = 5),ncol = 1),
                  matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1))



colData <- data.frame(condition = 'A', replicate = 1)
object <- epigraHMMDataSetFromMatrix(countData,colData)

#Initializing
object <- initializer(object,controlEM())

#Running epigraHMM
object <- epigraHMM(object,controlEM(),type = 'consensus',dist = 'nb')

#Example
expStep(pi = c(0.99,0.02),
       gamma = matrix(c(0.99,0.01,0.01,0.99),nrow = 2),
       logf = cbind(dnbinom(rnbinom(100,mu = 2,size = 10),mu = 2,size = 10,log = TRUE),
                    dnbinom(rnbinom(100,mu = 7.5,size = 5),mu = 7.5,size = 5,log = TRUE)),
       hdf5 = file.path(tempdir(),'tmp.h5'))

ENCODE ChIP-seq broad data from Helas3 cell line

Description

Data from EZH2, H3K27me3, and H3K36me3 ChIP-seq data from Helas3 cell line. For illustrative purposes, the data has been subset to chromosome 19. The dataset contains two replicates from each mark.

Usage

data(helas3)
data(helas3)

Format

An object of class "epigraHMMDataSet".

Source

ENCODE Broad Histone

References

Davis et al. (2018) NAR 46(D1):D794-D801. (PubMed)

Examples

# # # The data 'helas3' was created as follows.
# options(timeout=9999999)
# 
# url <- 'http://hgdownload.soe.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeBroadHistone/'
# samples <- c('wgEncodeBroadHistoneHelas3H3k36me3StdAlnRep1.bam',
#              'wgEncodeBroadHistoneHelas3H3k36me3StdAlnRep2.bam',
#              'wgEncodeBroadHistoneHelas3H3k27me3StdAlnRep1.bam',
#              'wgEncodeBroadHistoneHelas3H3k27me3StdAlnRep2.bam',
#              'wgEncodeBroadHistoneHelas3Ezh239875AlnRep1.bam',
#              'wgEncodeBroadHistoneHelas3Ezh239875AlnRep2.bam')
# 
# input <- paste0(url,samples)
# output <- paste0(tempdir(),samples)
# 
# for(idx in seq_len(length(input))){
#     download.file(url = input[idx],destfile = output[idx])
#     download.file(url = paste0(input[idx],'.bai'),
#                   destfile = paste0(output[idx],'.bai'))
# }
# 
# gr <- segmentGenome(genome = 'hg19',
#                     window = 1000,rm.gap = TRUE,rm.blacklist = TRUE)
# 
# cData <- data.frame(condition = rep(c('H3K36me3','H3K27me3','EZH2'),each = 2),
#                     replicate = rep(c(1,2),times = 3))
# 
# subGr <- gr[seqnames(gr) == 'chr19' & start(gr) >= 40e6 & end(gr) <= 50e6]
# 
# helas3 <-
#     epigraHMMDataSetFromBam(bamFiles = output,colData = cData,
#                             genome = subGr,windowSize = 1000)

data(helas3)
helas3
# # # The data 'helas3' was created as follows.
# options(timeout=9999999)
# 
# url <- 'http://hgdownload.soe.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeBroadHistone/'
# samples <- c('wgEncodeBroadHistoneHelas3H3k36me3StdAlnRep1.bam',
#              'wgEncodeBroadHistoneHelas3H3k36me3StdAlnRep2.bam',
#              'wgEncodeBroadHistoneHelas3H3k27me3StdAlnRep1.bam',
#              'wgEncodeBroadHistoneHelas3H3k27me3StdAlnRep2.bam',
#              'wgEncodeBroadHistoneHelas3Ezh239875AlnRep1.bam',
#              'wgEncodeBroadHistoneHelas3Ezh239875AlnRep2.bam')
# 
# input <- paste0(url,samples)
# output <- paste0(tempdir(),samples)
# 
# for(idx in seq_len(length(input))){
#     download.file(url = input[idx],destfile = output[idx])
#     download.file(url = paste0(input[idx],'.bai'),
#                   destfile = paste0(output[idx],'.bai'))
# }
# 
# gr <- segmentGenome(genome = 'hg19',
#                     window = 1000,rm.gap = TRUE,rm.blacklist = TRUE)
# 
# cData <- data.frame(condition = rep(c('H3K36me3','H3K27me3','EZH2'),each = 2),
#                     replicate = rep(c(1,2),times = 3))
# 
# subGr <- gr[seqnames(gr) == 'chr19' & start(gr) >= 40e6 & end(gr) <= 50e6]
# 
# helas3 <-
#     epigraHMMDataSetFromBam(bamFiles = output,colData = cData,
#                             genome = subGr,windowSize = 1000)

data(helas3)
helas3

Get information about peak calling results

Description

This function returns the BIC and expected log-likelihood function of the model, with respect to the last conditional distribution of unknown enrichment peaks given the data. The latter is also known as 'Q-function' in the EM context.

Usage

info(object)
info(object)

Arguments

object

an epigraHMMDataSet

Value

A list with BIC, and expected log-likelihood function of the model. If the input object contains results from a differential analysis, 'info' will also output the enrichment patterns associated with each mixture component used in the mixture model.

Author(s)

Pedro L. Baldoni, [email protected]

References

https://github.com/plbaldoni/epigraHMM

Examples


# Creating dummy object
countData <- rbind(matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1),
                   matrix(rnbinom(2e3,mu = 7.5,size = 5),ncol = 1),
                   matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1))

colData <- data.frame(condition = 'A', replicate = 1)
object <- epigraHMMDataSetFromMatrix(countData,colData)

# Initializing
object <- initializer(object,controlEM())

# Running epigraHMM
object <- epigraHMM(object,controlEM(),type = 'consensus',dist = 'nb')

# Get info
info(object)

# Creating dummy object
countData <- rbind(matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1),
                   matrix(rnbinom(2e3,mu = 7.5,size = 5),ncol = 1),
                   matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1))

colData <- data.frame(condition = 'A', replicate = 1)
object <- epigraHMMDataSetFromMatrix(countData,colData)

# Initializing
object <- initializer(object,controlEM())

# Running epigraHMM
object <- epigraHMM(object,controlEM(),type = 'consensus',dist = 'nb')

# Get info
info(object)

Initializer of epigraHMM

Description

This function call enriched windows individually for each sample in an epigraHMMDataSet. These are then used for initializing purposes in epigraHMM. By default, the Viterbi algorithm is used to determine enriched windows. Input controls and normalizing offsets are not utilized in this initialization step.

Usage

initializer(object, control)
initializer(object, control)

Arguments

`object`	an epigraHMMDataSet
`control`	list of control arguments from controlEM()

Details

To be added

Value

An epigraHMMDataSet with a 'peaks' assay filled in.

Author(s)

Pedro L. Baldoni, [email protected]

References

https://github.com/plbaldoni/epigraHMM

Examples

# Creating dummy object
countData <- rbind(matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1),
                   matrix(rnbinom(2e3,mu = 7.5,size = 5),ncol = 1),
                   matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1))

colData <- data.frame(condition = 'A', replicate = 1)
object <- epigraHMMDataSetFromMatrix(countData,colData)

# Initializing
object <- initializer(object,controlEM())

# Visualizing initialization peaks
#plot(assay(object),type = 'l')
#lines(7.5*assay(object,'peaks'),col = 'red')

# Creating dummy object
countData <- rbind(matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1),
                   matrix(rnbinom(2e3,mu = 7.5,size = 5),ncol = 1),
                   matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1))

colData <- data.frame(condition = 'A', replicate = 1)
object <- epigraHMMDataSetFromMatrix(countData,colData)

# Initializing
object <- initializer(object,controlEM())

# Visualizing initialization peaks
#plot(assay(object),type = 'l')
#lines(7.5*assay(object,'peaks'),col = 'red')

M-step (maximization w.r.t. initial and transition probabilities)

Description

M-step (maximization w.r.t. initial and transition probabilities)

Usage

maxStepProb(hdf5)
maxStepProb(hdf5)

Arguments

hdf5

path to where the hdf5 is saved

Examples

#Creating dummy object
countData <- rbind(matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1),
                  matrix(rnbinom(2e3,mu = 7.5,size = 5),ncol = 1),
                  matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1))



colData <- data.frame(condition = 'A', replicate = 1)
object <- epigraHMMDataSetFromMatrix(countData,colData)

#Initializing
object <- initializer(object,controlEM())

#Running epigraHMM
object <- epigraHMM(object,controlEM(),type = 'consensus',dist = 'nb')

#Example
maxStepProb(hdf5 = S4Vectors::metadata(object)$output)
#Creating dummy object
countData <- rbind(matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1),
                  matrix(rnbinom(2e3,mu = 7.5,size = 5),ncol = 1),
                  matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1))



colData <- data.frame(condition = 'A', replicate = 1)
object <- epigraHMMDataSetFromMatrix(countData,colData)

#Initializing
object <- initializer(object,controlEM())

#Running epigraHMM
object <- epigraHMM(object,controlEM(),type = 'consensus',dist = 'nb')

#Example
maxStepProb(hdf5 = S4Vectors::metadata(object)$output)

Normalize counts

Description

This function performs a non-linear normalization of counts with respect to a reference sample (geometric mean)

Usage

normalizeCounts(object, control, span = 1, ...)
normalizeCounts(object, control, span = 1, ...)

Arguments

`object`	an epigraHMMDataSet
`control`	list of control arguments from controlEM()
`span`	the span parameter of `loessFit` (default is 1)
`...`	arguments to be passed to `loessFit` for loess calculation

Details

This function 'limma::loessFit', which simply a wrapper for the 'stats::lowess' smoother.

Value

An epigraHMMDataSet with an 'offsets' assay filled in.

Author(s)

Pedro L. Baldoni, [email protected]

References

https://github.com/plbaldoni/epigraHMM

Examples


# Creating dummy object
countData <- list('counts' = matrix(rpois(1e5,10),ncol = 2),
'controls' = matrix(rpois(1e5,5),ncol = 2))
colData <- data.frame(condition = c('A','A'), replicate = c(1,2))
object <- epigraHMMDataSetFromMatrix(countData,colData)

# Normalizing counts
object <- normalizeCounts(object = object,control = controlEM(), span = 1)

# Creating dummy object
countData <- list('counts' = matrix(rpois(1e5,10),ncol = 2),
'controls' = matrix(rpois(1e5,5),ncol = 2))
colData <- data.frame(condition = c('A','A'), replicate = c(1,2))
object <- epigraHMMDataSetFromMatrix(countData,colData)

# Normalizing counts
object <- normalizeCounts(object = object,control = controlEM(), span = 1)

Create a plot with the results from epigraHMM

Description

'plotCounts()' plots read counts and peak regions from 'epigraHMM()'

Usage

plotCounts(
  object,
  ranges,
  hdf5 = metadata(object)$output,
  peaks = NULL,
  annotation = NULL
)
plotCounts(
  object,
  ranges,
  hdf5 = metadata(object)$output,
  peaks = NULL,
  annotation = NULL
)

Arguments

`object`	an epigraHMMDataSet
`ranges`	a GRanges object or a pair of integers with the genomic corrdinates/windows to be plotted
`hdf5`	an optional character string with the hdf5 file path from 'epigraHMM'
`peaks`	an optional parameter with a GRanges object or a vector of logicals (with length equal to the number of rows in 'object') specifying the genomic corrdinates/windows with peaks
`annotation`	an optional parameter with a GRanges object or a vector of logicals (with length equal to the number of rows in 'object') specifying the genomic corrdinates/windows of an annotation track

Details

If the input object contains the assay 'offset', reads will be normalized prior to plotting (e.g. counts/exp(offset)). Reads from replicates pertaining to the same condition are aggregated prior to plotting.

Value

A ggplot

Author(s)

Pedro L. Baldoni, [email protected]

References

https://github.com/plbaldoni/epigraHMM

Examples


countData <- rbind(matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1),
                   matrix(rnbinom(1e3,mu = 7.5,size = 5),ncol = 1),
                   matrix(rnbinom(1e3,mu = 7.5,size = 5),ncol = 1),
                   matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1))

colData <- data.frame(condition = 'A', replicate = 1)

object <- epigraHMMDataSetFromMatrix(countData,colData)

plotCounts(object,ranges = c(500,3500))

countData <- rbind(matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1),
                   matrix(rnbinom(1e3,mu = 7.5,size = 5),ncol = 1),
                   matrix(rnbinom(1e3,mu = 7.5,size = 5),ncol = 1),
                   matrix(rnbinom(1e3,mu = 2,size = 10),ncol = 1))

colData <- data.frame(condition = 'A', replicate = 1)

object <- epigraHMMDataSetFromMatrix(countData,colData)

plotCounts(object,ranges = c(500,3500))

Create a plot of differerential patterns posterior probabilities from epigraHMM

Description

'plotPatterns()' plots the posterior probabilities associated with differential patterns from a differential analysis of 'epigraHMM()'

Usage

plotPatterns(
  object,
  ranges,
  peaks,
  hdf5 = metadata(object)$output,
  colors = NULL
)
plotPatterns(
  object,
  ranges,
  peaks,
  hdf5 = metadata(object)$output,
  colors = NULL
)

Arguments

`object`	an epigraHMMDataSet
`ranges`	a GRanges object or a pair of integers with the genomic corrdinates/windows to be plotted
`peaks`	either a GRanges object or a vector of logicals (with length equal to the number of rows in 'object') specifying the genomic corrdinates/windows with peaks
`hdf5`	a character string with the hdf5 file path from 'epigraHMM'
`colors`	an optional argument that specifies the colors for each differential combinatorial pattern

Value

A pheatmat

Author(s)

Pedro L. Baldoni, [email protected]

References

https://github.com/plbaldoni/epigraHMM

Examples

# Creating dummy object
countData <- cbind(rbind(matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 10, size = 5), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 10, size = 5), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1)),
                   rbind(matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 10, size = 5), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 10, size = 5), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1)))

colData <- data.frame(condition = c('A','B'), replicate = c(1,1))
rowRanges <- GenomicRanges::GRanges('chrA',
                     IRanges::IRanges(start = seq(1,by = 500,
                     length.out = nrow(countData)),width = 500))

object <- epigraHMMDataSetFromMatrix(countData,colData,rowRanges = rowRanges)

# Initializing
object <- initializer(object,controlEM())

# Running epigraHMM
object <- epigraHMM(object,controlEM(),type = 'differential',dist = 'nb')

# Calling peaks
peaks <- callPeaks(object = object,
                  hdf5 = S4Vectors::metadata(object)$output,
                  method = 'viterbi')

# Plotting patterns
plotPatterns(object,
             ranges = peaks[1],
             peaks = peaks)

# Creating dummy object
countData <- cbind(rbind(matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 10, size = 5), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 10, size = 5), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1)),
                   rbind(matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 10, size = 5), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1),
                         matrix(rnbinom(1e2, mu = 10, size = 5), ncol = 1),
                         matrix(rnbinom(1e2, mu = 1, size = 10), ncol = 1)))

colData <- data.frame(condition = c('A','B'), replicate = c(1,1))
rowRanges <- GenomicRanges::GRanges('chrA',
                     IRanges::IRanges(start = seq(1,by = 500,
                     length.out = nrow(countData)),width = 500))

object <- epigraHMMDataSetFromMatrix(countData,colData,rowRanges = rowRanges)

# Initializing
object <- initializer(object,controlEM())

# Running epigraHMM
object <- epigraHMM(object,controlEM(),type = 'differential',dist = 'nb')

# Calling peaks
peaks <- callPeaks(object = object,
                  hdf5 = S4Vectors::metadata(object)$output,
                  method = 'viterbi')

# Plotting patterns
plotPatterns(object,
             ranges = peaks[1],
             peaks = peaks)

Segmentation of a genome in non-overlapping windows

Description

This function segments a genome into non-overlapping windows.

Usage

segmentGenome(genome, window, rm.gap = TRUE, rm.blacklist = TRUE)
segmentGenome(genome, window, rm.gap = TRUE, rm.blacklist = TRUE)

Arguments

`genome`	a string with the name of the genome (e.g. 'hg19')
`window`	an integer with the window size
`rm.gap`	a logical indicating gap regions should be removed
`rm.blacklist`	a logical indicating blacklisted regions should be removed

Value

a GRanges object with the binned genome

Author(s)

Pedro L. Baldoni, [email protected]

References

https://github.com/plbaldoni/epigraHMM

Examples


gr <- segmentGenome(genome = 'mm10', window = 500)

gr <- segmentGenome(genome = 'mm10', window = 500)

Simulates a Markov Chain of length 'n' given a matrix of transition probabilities P

Description

Simulates a Markov Chain of length 'n' given a matrix of transition probabilities P

Usage

simulateMarkovChain(P, n)
simulateMarkovChain(P, n)

Arguments

`P`	a matrix of transition probabilities (row sums should be 1)
`n`	an integer specifying thhe length of the simulated sequence

Examples

#Example
simulateMarkovChain(matrix(c(0.99,0.01,0.01,0.99),2,2),100)
#Example
simulateMarkovChain(matrix(c(0.99,0.01,0.01,0.99),2,2),100)

Package 'epigraHMM'

Help Index

Add offsets to epigraHMMDataSet

Description

Usage

Arguments

Details

Value

References

Examples

Extract posterior probabilities (or combinatorial patterns) associated with differential regions

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Summarize peak calls and optionally create a BED 6+3 file in broadPeak format for visualization

Description

Usage

Arguments

Value

Author(s)

References

Examples

Remove effects from covariates of interest

Description

Usage

Arguments

Value

Author(s)

References

Examples

Control parameters for the EM algorithm from epigraHMM

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Perform peak calling of epigenomic data sets

Description

Usage

Arguments

Value

Author(s)

References

Examples

Create a epigraHMMDataSet from a set of BAM files

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Create a epigraHMMDataSet from matrices of counts

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Estimate transition probability from a sequence of integers

Description

Usage

Arguments

Value

References

Examples

E-step of HMM (forward-backward probability + posterior probability calculation)

Description

Usage