plotPatterns: Create a plot of differerential patterns posterior...

In plbaldoni/epigraHMM: Epigenomic R-based analysis with hidden Markov models

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
)

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, [e-mail]

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)

plbaldoni/epigraHMM documentation built on Oct. 15, 2023, 7:53 p.m.