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.
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Detect differential enrichment regions (peaks) across multiple experimental conditions and classify peaks into combinatorial patterns of enrichment.
library(epigraHMM)
# Define input BAM files and metadata
bamFiles <- system.file(
package = "genomationData",
"extdata",
c("wgEncodeBroadHistoneH1hescCtcfStdAlnRep1.chr21.bam",
"wgEncodeBroadHistoneH1hescSuz12051317AlnRep1.chr21.bam")
)
colData <- data.frame(condition = c("CTCF", "SUZ12"), replicate = c(1, 1))
# Create dataset object
object_differential <- epigraHMMDataSetFromBam(
bamFiles = bamFiles,
colData = colData,
genome = 'hg19',
windowSize = 500,
gapTrack = TRUE,
blackList = TRUE
)
# Set up EM control and run differential peak calling
control <- controlEM(fileName = 'control-differential', criterion = 'MACPE')
object_differential <- normalizeCounts(object_differential, control)
object_differential <- initializer(object_differential, control)
object_differential <- epigraHMM(object = object_differential, control = control, type = 'differential')
# Call peaks
peaks_differential <- callPeaks(object = object_differential)
Input: BAM files and sample metadata; Output: A GRanges object containing called differential peak regions.
Detect consensus enrichment regions (peaks) across technical or biological replicates of a single experimental condition.
library(epigraHMM)
# Define input BAM files and metadata
bamFiles <- system.file(
package = "genomationData",
"extdata",
"wgEncodeBroadHistoneH1hescCtcfStdAlnRep1.chr21.bam"
)
colData <- data.frame(condition = "CTCF", replicate = 1)
# Create dataset object
object_consensus <- epigraHMMDataSetFromBam(
bamFiles = bamFiles,
colData = colData,
genome = 'hg19',
windowSize = 500,
gapTrack = TRUE,
blackList = TRUE
)
# Set up EM control and run consensus peak calling
control <- controlEM(fileName = 'control-consensus')
object_consensus <- normalizeCounts(object_consensus, control = control)
object_consensus <- initializer(object_consensus, control)
object_consensus <- epigraHMM(object = object_consensus, control = control, type = 'consensus')
# Call peaks
peaks_consensus <- callPeaks(object = object_consensus)
Input: BAM files and sample metadata; Output: A GRanges object containing called consensus peak regions.
epigraHMM with type = 'consensus'.epigraHMM with type = 'differential'.epigraHMMDataSetFromBam) or count matrices (epigraHMMDataSetFromMatrix).plotPatterns.csaw because epigraHMM operates on fixed genomic windows.MACS2 or DiffBind for standard peak calling.bamFiles) or a count matrix (countData) of non-negative integers.data.frame (colData) containing columns named condition and replicate.GRanges object with chromosome lengths.callPeaks.normalizeCounts as the last normalization step just prior to peak calling to correct for non-linear biases.controls matrix/BAM list to model them as a covariate in the HMM.dist = 'zinb' for consensus peak calling as the zero-inflated negative binomial model provides better results in this setting..bai index files are present in the same directory as BAM files and named with the .bai suffix.colData contains the exact column names condition and replicate.addOffsets, run it before normalizeCounts so that the non-linear normalization considers existing offsets.还没有评价。欢迎分享你的使用感受。
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