Package 'ChIPQC' reference manual

Title:	Quality metrics for ChIPseq data
Description:	Quality metrics for ChIPseq data.
Authors:	Tom Carroll, Wei Liu, Ines de Santiago, Rory Stark
Maintainer:	Tom Carroll <[email protected]>, Rory Stark <[email protected]>
License:	GPL (>= 3)
Version:	1.49.3
Built:	2026-05-20 19:27:44 UTC
Source:	https://github.com/bioc/ChIPQC

ChIPQC - Quality metrics for ChIPseq data

Description

ChIPQC analyzes aligned reads (in .bam format) for ChIP-seq samples and their associated controls, computing a variety of quality control metrics and statistics, and providing reporting and plotting functions to enable assessment of experimental data for further analysis.

Details

Package:	ChIPQC
Type:	Package
Version:	0.1
Date:	2014-03-01
License:	GPL3

ChIPQC primarily uses two object classes: ChIPQCsample, which encapsulates the information about individual samples, and ChIPQCexperiment, which encapsulates information about larger ChIP-seq experiments (consisting of a number of samples). The primary entry point is the constructor function ChIPQC, which takes a description of an entire experiment, constructs objects for all the samples, and computes the quality metrics.

Author(s)

Tom Carroll and Rory Stark

Maintainers: Tom Carroll <[email protected]> and Rory Stark <[email protected]>

References

Frontiers?

Retrieve average peak profiles

Description

Retrieve the average peak profile for a sample or set of samples.

Methods

signature(object = "ChIPQCexperiment"): Retrieve a matrix of the average peak profiles for all of the samples in an ChIP-seq experiment. Each column represents a sample, and each row a base pair position, centered on peak summits.
signature(object = "list"): Retrieve a matrix of the average peak profiles for all of the samples in a list of ChIPQC samples. Each column represents a sample, and each row a base pair position, centered on peak summits.
signature(object = "ChIPQCsample"): Retrieve a vector representing the average peak profile for a sample. Each column represents a basepair position, centered on the peak summits.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
CTCFprofile = averagepeaksignal(QCsample(exampleExp,1))
length(CTCFprofile)
plot(CTCFprofile,type='l',ylab="mean pileup")

allprofiles = averagepeaksignal(exampleExp)
dim(allprofiles)
for(i in 1:ncol(allprofiles)) lines(allprofiles[,i],col=i)

data(example_QCexperiment)
CTCFprofile = averagepeaksignal(QCsample(exampleExp,1))
length(CTCFprofile)
plot(CTCFprofile,type='l',ylab="mean pileup")

allprofiles = averagepeaksignal(exampleExp)
dim(allprofiles)
for(i in 1:ncol(allprofiles)) lines(allprofiles[,i],col=i)

Construct a `ChIPQCexperiment` object

Description

Constructs a new ChIPQCexperiment object.

Usage

ChIPQC(experiment, annotation, chromosomes, samples,
       consensus=FALSE, bCount=FALSE, mapQCth=15, blacklist=NULL, 
       profileWin=400, fragmentLength=125, shifts=1:300, ...)
ChIPQC(experiment, annotation, chromosomes, samples,
       consensus=FALSE, bCount=FALSE, mapQCth=15, blacklist=NULL, 
       profileWin=400, fragmentLength=125, shifts=1:300, ...)

Arguments

experiment

A specification of the ChIP-seq experiment to evaluate. This can either be a dataframe, a filename for a .csv file, or a DBA-object object as defined in the DiffBind package. Columns names in sample sheet may include:

SampleID:: Identifier string for sample
Tissue:: Identifier string for tissue type
Factor:: Identifier string for factor
Condition:: Identifier string for condition
Treatment:: Identifier string for treatment
Replicate:: Replicate number of sample
bamReads:: file path for bam file containing aligned reads for ChIP sample
bamControl:: file path for bam file containing aligned reads for control sample
ControlID:: Identifier string for control sample
Peaks:: path for file containing peaks for sample. Format determined by PeakCaller field or caller parameter
PeakCaller:: Identifier string for peak caller used. If Peaks is not a bed file, this will determine how the Peaks file is parsed. If missing, will use default peak caller specified in caller parameter. Possible values:

“raw”:: text file file; peak score is in fourth column
“bed”:: .bed file; peak score is in fifth column
“narrow”:: default peak.format: narrowPeaks file
“macs”:: MACS .xls file
“swembl”:: SWEMBL .peaks file
“bayes”:: bayesPeak file
“fp4”:: FindPeaks v4

PeakFormat:: string indicating format for peak files; see PeakCaller and dba.peakset
ScoreCol:: column in peak files that contains peak scores
LowerBetter:: logical indicating that lower scores signify better peaks

See the documentation for the sampleSheet parameter of dba for details.

annotation

Either a character string indicating the genome and version to use as a genomic annotation, or a previously defined annotation (obtained using QCannotation on a previously defined ChIPQCexperiment object.) May be left unspecified, in which case no genomic feature analysis is performed. The following annotation specifiers are supported:

“hg19”:: Human, version 19
“hg18”:: Human, version 18
“mm10”:: Mouse, version 10
“mm9” :: Mouse, version 19
“rn4” :: Rat, version 4
“ce6” :: C. Elgans, version 6
“dm3” :: D. Melanogaster, version 3

Alternatively, you can construct your own annotation; see the package vignette for more information.

chromosomes

Specification of which chromosomes to use for computing QC statistics. If missing, the first chromosome which has a peak is checked. If NULL, all chromosomes will be checked (which may be time-consuming). This can be a character string (e.g. “chr18”) or a vector or list of character strings. If it is an integer or vector of integers, the chromosomes will be checked based on the order that they are listed in a peak set.

samples

list of ChIPCsample objects. If present, the sample objects will be taken directly from this list instead of being computed using the ChIPQCsample constructor.

consensus

If consensus is a GRanges object, all samples will use this peakset when computing peak-based metrics. If consensus=TRUE, a consensus peakset will be generated and used for all samples, derived by merging overlapping peaks in all provided peaksets, keeping any peaks that overlap in at least two samples To avoid this behavior, set consensus=FALSE; this will result in only supplied peaksets being used for calculation of peak-based metrics (and no peak-based metric being computed for samples with no peakset specified, such as controls).

bCount

if TRUE, the peak scores for all samples will be based on read counts using dba.count using a consensus peakset. If consenus is missing, any samples (such as controls) that are not already associated with a peakset will be associated with the consensus peakset (if consensus is not missing, all samples will be associated with the consensus peakset). Note that the re-counting process may be time-consuming.

mapQCth

An integer representing a mapping quality score threshold. Only reads with mapping quality scores above this threshold will be used for some statistics.

blacklist

A GRanges object or filename specifying a bed file containing genomic regions that should be excluded from the analysis. If missing and the annotation is “hg19”, a default blacklist, blacklist_hg19 derived from the UCSC list, will be used. No blacklist is used if this is set to NULL, or is left missing and the annotation is not “hg19”.

profileWin

An integer indicating the width, in base pairs, of the window to be used for peak profiles. Peaks will be centered on their summits, and include half the window size upstream and half downstream of this point.

fragmentLength

An integer indicating the expected fragment length of the libraries. Optional, as this value will be computed for each library.

shifts

A vector of values to try when computing optimal shift sizes.

...

additional parameters passed to dba.count if bCount=TRUE.

Details

ChIPQC first constructs a new DBA-object object if one is not provided. Next it computes the annotation if one is not provided. The main loop constructs new ChIPQCsample objects for each sample (and unique control sample).

Value

A ChIPQCexperiment object.

Note

ChIPQC uses bplapply from the BiocParallel package to build the ChIPQCsample object in parallel, if supported. Control of the parallelization can be effected using BiocParallel functions, such as register.

Author(s)

Thomas Carroll and Rory Stark

Examples

## Not run: exampleExp = ChIPQC(samples,annotation="hg19")
data(example_QCexperiment)
exampleExp

## Not run: tamoxifen = ChIPQC(samples, ,annotation="hg19", consensus=TRUE, bCounts=T)
data(tamoxifen_QC)
tamoxifen
## Not run: exampleExp = ChIPQC(samples,annotation="hg19")
data(example_QCexperiment)
exampleExp

## Not run: tamoxifen = ChIPQC(samples, ,annotation="hg19", consensus=TRUE, bCounts=T)
data(tamoxifen_QC)
tamoxifen

Example data sets for `ChIPQC` package, each containing a `ChIPQCexperiment` object, as well as a pre-compiled blacklist for hg19.

Description

The tamoxifen_QC example data set contains a ChIPQCexperiment object named tamoxifen. This data set, based on Ross-Innes et al (2012), includes 11 ER ChIP libraries, and their input controls, divided into tamoxifen responsive and tamoxifen resistant sample groups. Only data for chromosome 18 (chr18) are included.

The example_QCexperiment data set contains a ChIPQCexperiment object named exampleExp. This data set, derived from ENCODE data, includes 6 ChIP libraries. Only data for chromosome 22 (chr22) are included.

blacklist_hg19 data set includes a GRanges object named blacklist_hg19 containing blacklisted regions for the human genome, derived from the UCSC blacklist.

Usage

data(tamoxifen_QC)
data(example_QCexperiment)
data(blacklist_hg19)
data(tamoxifen_QC)
data(example_QCexperiment)
data(blacklist_hg19)

Format

tamoxifen_QC: A single ChIPQCexperiment object named tamoxifen is loaded. This object is used for the ChIPQC-package examples and vignette. This object can also be used with the DiffBind package (see related data objects tamoxifen).

example_QCexperiment: A single ChIPQCexperiment object named exampleExp is loaded. This object is used for the ChIPQC-package examples and vignette.

blacklist_hg19: A single GRanges object named blacklist.hg19 that is used by default when processing hg19 data sets.

Source

Ross-Innes, C. S., Stark, R., Teschendorff, A. E., Holmes, K. A., Ali, H. R., Dunning, M. J., Brown, G. D., Gojis, O., Ellis, I. O., Green, A. R., Ali, S., Chin, S.-F., Palmieri, C., Caldas, C., and Carroll, J. S. (2012). Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature 481, 389-393.

Examples

data(tamoxifen_QC)
tamoxifen
plotRegi(tamoxifen)

data(example_QCexperiment)
exampleExp

data(blacklist_hg19)
blacklist.hg19
data(tamoxifen_QC)
tamoxifen
plotRegi(tamoxifen)

data(example_QCexperiment)
exampleExp

data(blacklist_hg19)
blacklist.hg19

Deprecated functions in package ‘ChIPQC’

Description

These functions are provided for compatibility with older versions of ‘ChIPQC’ only, and will be defunct at the next release.

Details

The following functions are deprecated and will be made defunct in the next Bioconductor release cycle:

ChIPQCreport: there is no replacement function. The Nozzle.R1 CRAN package that ChIPQCreport depended on for HTML report generation is no longer available and cannot be installed. ChIP-seq quality metrics are still available through the exported accessor methods (QCmetrics, coveragehistogram, crosscoverage, flagtagcounts, fragmentlength, ssd, frip, etc.). For visual ChIP-seq QC reports, the deeptools Python package is the de facto standard. The original ChIPQCreport source code is preserved in inst/legacy/report.r and can be accessed via system.file("legacy/report.r", package = "ChIPQC") for reference and possible future revival.

ChIPQCexperiment instances

Description

The ChIPQCexperiment class is built around a list of ChIPQCsample objects, each representing a ChIP or control sample in a ChIP-seq experiment. These objects are created using the ChIPQC function.

Slots

.Data:: Object of class "list": internal
Samples:: Object of class "list": List of ChIPQCsample objects.
DBA:: Object of class "DBA": DBA-object object (from package DiffBind)
annotation:: Object of class "list" : annotation data

Extends

Class "list".

Methods

QCmetadata: signature(object = "ChIPQCexperiment"): see QCmetadata.
QCmetrics: signature(object = "ChIPQCexperiment"): see QCmetrics.
QCsample: signature(object = "ChIPQCexperiment"): see QCsample.
QCcontrol: signature(object = "ChIPQCexperiment"): see QCcontrol.
QCannotation: signature(object = "ChIPQCexperiment"): see QCannotation.
QCdba: signature(object = "ChIPQCexperiment"): see QCdba.
averagepeaksignal: signature(object = "ChIPQCexperiment"): see averagepeaksignal.
coveragehistogram: signature(object = "ChIPQCexperiment"): see coveragehistogram.
crosscoverage: signature(object = "ChIPQCexperiment"): see crosscoverage.
flagtagcounts: signature(object = "ChIPQCexperiment"): see flagtagcounts.
fragmentlength: signature(object = "ChIPQCexperiment"): see fragmentlength.
FragmentLengthCrossCoverage: signature(object = "ChIPQCexperiment"): see FragmentLengthCrossCoverage.
frip: signature(object = "ChIPQCexperiment"): see frip.
mapped: signature(object = "ChIPQCexperiment"): see mapped.
reads: signature(object = "ChIPQCexperiment"): see reads.
duplicates: signature(object = "ChIPQCexperiment"): see duplicates.
duplicateRate: signature(object = "ChIPQCexperiment"): see duplicateRate.
Normalisedaveragepeaksignal: signature(object = "ChIPQCexperiment"): see Normalisedaveragepeaksignal.
peaks: signature(object = "ChIPQCexperiment"):see peaks.
readlength: signature(object = "ChIPQCexperiment"): see readlength.
ReadLengthCrossCoverage: signature(object = "ChIPQCexperiment"): see ReadLengthCrossCoverage.
RelativeCrossCoverage: signature(object = "ChIPQCexperiment"):see RelativeCrossCoverage.
ribl: signature(object = "ChIPQCexperiment"): see ribl.
rip: signature(object = "ChIPQCexperiment"): see rip.
show: signature(object = "ChIPQCexperiment"): see show.
ssd: signature(object = "ChIPQCexperiment"): see ssd.
regi: signature(object = "ChIPQCexperiment"): see regi.
plotCC: signature(object = "ChIPQCexperiment"): see plotCC.
plotCoverageHist: signature(object = "ChIPQCexperiment"): see plotCoverageHist.
plotFribl: signature(object = "ChIPQCexperiment"): see plotFribl.
plotPeakProfile: signature(object = "ChIPQCexperiment"): see plotPeakProfile.
plotRap: signature(object = "ChIPQCexperiment"): see plotRap.
plotRegi: signature(object = "ChIPQCexperiment"): see plotRegi.
plotCorHeatmap: signature(object = "ChIPQCexperiment"): see plotCorHeatmap.
plotPrincomp: signature(object = "ChIPQCexperiment"): see plotPrincomp.
ChIPQCreport: signature(object = "ChIPQCexperiment"): see ChIPQCreport.

Author(s)

Thomas Carroll and Rory Stark

Examples

## Not run: exampleExp = ChIPQC(samples)
data(example_QCexperiment)
exampleExp

## Not run: tamoxifen = ChIPQC(samples, consensus=TRUE, bCounts=T)
data(tamoxifen_QC)
tamoxifen
## Not run: exampleExp = ChIPQC(samples)
data(example_QCexperiment)
exampleExp

## Not run: tamoxifen = ChIPQC(samples, consensus=TRUE, bCounts=T)
data(tamoxifen_QC)
tamoxifen

Generate a summary QC report

Description

Generate an HTML summary QC report.

Methods

signature(experiment = "ChIPQCexperiment", facet=TRUE, reportName="ChIPQC", reportFolder="ChIPQCreport", facetBy=c("Tissue","Factor","Condition","Treatment"), colourBy=c("Replicate"))

Generates an summary QC report for the experiment in HTML format.

experiment	`ChIPQCexperiment` object
facet	logical indicating whether or not to facet using experimental metadata.
reportName	filename of main report file (.html).
reportFolder	directory name where plot graphics will be saved
facetBy	metadata fields to use for faceting
colourBy	metadata field to color by

signature(experiment = "list", facet=TRUE, reportName="ChIPQC", reportFolder="ChIPQCreport", facetBy=c("Sample"), colourBy=c("Sample"))

Generates an summary QC report for a list of ChIPQCsample objects in HTML format.

experiment	`list` object
facet	logical indicating whether or not to facet using experimental metadata.
reportName	filename of main report file (.html).
reportFolder	directory name where plot graphics will be saved
facetBy	metadata fields to use for faceting
colourBy	metadata field to color by

signature(sample = "ChIPQCsample", reportName="ChIPQC", reportFolder="ChIPQCreport",)

Generate a summary QC report for a sample in HTML format.

sample	`ChIPQCsample` object
reportName	filename of main report file (.html).
reportFolder	directory name where plot graphics will be saved

Note

ChIPQCreport is deprecated as of ChIPQC 1.49.1. The Nozzle.R1 CRAN package it depended on for HTML report generation is no longer available. See ?'ChIPQC-deprecated' for replacements (QC accessor methods; deeptools for visual reports). The original implementation is preserved at system.file("legacy/report.r", package = "ChIPQC").

Author(s)

Thomas Carroll and Rory Stark

Class `"ChIPQCsample"`

Description

Object containing quality metrics computed for a ChIP-seq (or associated control) sample.

Objects from the Class

Objects can be created using the ChIPQCsample function.

Constructor Function

ChIPQCsample(reads, peaks, annotation, chromosomes = NULL, mapQCth = 15, blacklist, profileWin = 400, fragmentLength = 125, shifts = 1:300, runCrossCor = FALSE,verboseT=FALSE)

reads

character string filename of .bam file

peaks

GRanges object or character string filename of peaks. If present, peak-based metrics will be computed.

annotation

"hg20"	Human, version 20
"hg19"	Human, version 19
"hg18"	Human, version 18
"mm10"	Mouse, version 10
"mm9"	Mouse, version 19
"rn4"	Rat, version 4
"ce6"	C. Elgans, version 6
"dm3"	D. Melanogaster, version 3

Alternatively, you can construct your own annotation; see the package Vignette for more information.

chromosomes

mapQCth

An integer representing a mapping quality score threshold. Only reads with mapping quality scores above this threshold will be used for some statistics.

blacklist

profileWin

fragmentLength

An integer indicating the expected fragment length of the libraries. Optional, as this value will be computed.

shifts

A vector of values to try when computing optimal shift sizes.

runCrossCor

Compute cross-correlation in addition to cross-coverage. This will take more compute time, and is currently not used in the final report.

verboseT

TRUE or FALSE, specifying whether to report progress. Default is TRUE. When set to FALSE ChIPQC does not report any progress until complete.

Slots

AveragePeakSignal:: Object of class "list"
CrossCoverage:: Object of class "numeric"
CrossCorrelation:: Object of class "numeric"
SSD:: Object of class "numeric"
SSDBL:: Object of class "numeric"
CountsInPeaks:: Object of class "numeric"
CountsInBlackList:: Object of class "numeric"
CountsInFeatures:: Object of class "list"
PropInFeatures:: Object of class "list"
CoverageHistogram:: Object of class "numeric"
FlagAndTagCounts:: Object of class "numeric"
readlength:: Object of class "numeric"
seqnames:: Object of class "Rle"
ranges:: Object of class "IRanges"
strand:: Object of class "Rle"
elementMetadata:: Object of class "DataFrame"
seqinfo:: Object of class "Seqinfo"
metadata:: Object of class "list"

Extends

Class "GRanges"

Methods

averagepeaksignal: signature(object = "ChIPQCsample"): see averagepeaksignal.
coveragehistogram: signature(object = "ChIPQCsample"): see coveragehistogram.
crosscoverage: signature(object = "ChIPQCsample"): see crosscoverage.
flagtagcounts: signature(object = "ChIPQCsample"): see flagtagcounts.
fragmentlength: signature(object = "ChIPQCsample"): see fragmentlength.
FragmentLengthCrossCoverage: signature(object = "ChIPQCsample"): see FragmentLengthCrossCoverage.
frip: signature(object = "ChIPQCsample"): see frip.
mapped: signature(object = "ChIPQCsample"): see mapped.
reads: signature(object = "ChIPQCsample"): see reads.
duplicates: signature(object = "ChIPQCsample"): see duplicates.
duplicateRate: signature(object = "ChIPQCsample"): see duplicateRate.
Normalisedaveragepeaksignal: signature(object = "ChIPQCsample"): see Normalisedaveragepeaksignal.
peaks: signature(object = "ChIPQCsample"):see peaks.
readlength: signature(object = "ChIPQCsample"): see readlength.
ReadLengthCrossCoverage: signature(object = "ChIPQCsample"): see ReadLengthCrossCoverage.
RelativeCrossCoverage: signature(object = "ChIPQCsample"):see RelativeCrossCoverage.
ribl: signature(object = "ChIPQCsample"): see ribl.
rip: signature(object = "ChIPQCsample"): see rip.
show: signature(object = "ChIPQCsample"): see show.
ssd: signature(object = "ChIPQCsample"): see ssd.
regi: signature(object = "ChIPQCsample"): see regi.
plotCC: signature(object = "ChIPQCsample"): see plotCC.
plotCoverageHist: signature(object = "ChIPQCsample"): see plotCoverageHist.
plotFribl: signature(object = "ChIPQCsample"): see plotFribl.
plotPeakProfile: signature(object = "ChIPQCsample"): see plotPeakProfile.
plotRap: signature(object = "ChIPQCsample"): see plotRap.
plotRegi: signature(object = "ChIPQCsample"): see plotRegi.

Author(s)

Thomas Carroll and Rory Stark

References

Carroll TS, Liang Z, Salama R, Stark R and Santiago Id (in press). Impact of artefact removal on ChIP quality metrics in ChIP-seq and ChIP-exo data. Frontiers in Genetics.

Examples

bamFile <- system.file("extdata", "ex1.bam", 
                        package="Rsamtools")
ex1 <- ChIPQCsample(bamFile,annotation=NULL)
readlength(ex1)
fragmentlength(ex1)
bamFile <- system.file("extdata", "ex1.bam", 
                        package="Rsamtools")
ex1 <- ChIPQCsample(bamFile,annotation=NULL)
readlength(ex1)
fragmentlength(ex1)

Retrieve histogram data representing densities of coverage pileups

Description

Retrieve histogram data representing densities of coverage pileups.

Methods

signature(object = "ChIPQCexperiment"): Retrieve a matrix of coverage histogram data for all samples in a ChIP-seq experiment. Each column represents a sample, and each row a pileup height, with the value representing the number of basepair positions that report this pileup height.
signature(object = "list"): Retrieve a matrix of coverage histogram data for all ChIPQCsamples in a list. Each column represents a sample, and each row a pileup height, with the value representing the number of basepair positions that report this pileup height.
signature(object = "ChIPQCsample"): Retrieve a vector representing coverage histogram data for a sample. Values represent the number of base pairs positions that report the pileup value. The value in position 1 of the vector the contains the number of examined basepair positions that are overlapped by exactly zero reads, while position 2 shows the number of basepair positions overlapped by exactly one read, etc.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
CTCFcoverage = coveragehistogram(QCsample(exampleExp,1))
length(CTCFcoverage)
plot(log10(CTCFcoverage),type='l',ylab="log10 Density",
                          xlab="Pileup")

allcoverages = coveragehistogram(exampleExp)
dim(allcoverages)
for(i in 1:ncol(allcoverages)) lines(log10(allcoverages[,i]),col=i)
data(example_QCexperiment)
CTCFcoverage = coveragehistogram(QCsample(exampleExp,1))
length(CTCFcoverage)
plot(log10(CTCFcoverage),type='l',ylab="log10 Density",
                          xlab="Pileup")

allcoverages = coveragehistogram(exampleExp)
dim(allcoverages)
for(i in 1:ncol(allcoverages)) lines(log10(allcoverages[,i]),col=i)

Retrieve the cross coverage values for a range of shift sizes

Description

Retrieves the cross-coverage values for a range of shift sizes.

Methods

signature(object = "ChIPQCexperiment"): Retrieve a matrix of cross-coverage data for all samples in an ChIP-seq experiment. Each column represents a sample, and each row a shift size, with the value representing the cross-coverage using that size read.
signature(object = "list"): Retrieve a matrix of cross-coverage data for all samples in a list of ChIPQCsample objects. Each column represents a sample, and each row a shift size, with the value representing the cross-coverage using that size read.
signature(object = "ChIPQCsample"): Retrieve a vector of cross-coverage data for a sample. Each position in the vector corresponds to a shift size, with the value representing the cross-coverage using that size read.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
CTCFcoverage = crosscoverage(QCsample(exampleExp,1))
length(CTCFcoverage)
plot(CTCFcoverage,type='l',
     ylab="Cross-coverage",
     xlab="Fragment length") 

allcoverages = crosscoverage(exampleExp)
dim(allcoverages)
for(i in 1:ncol(allcoverages)) lines(allcoverages[,i],col=i)
data(example_QCexperiment)
CTCFcoverage = crosscoverage(QCsample(exampleExp,1))
length(CTCFcoverage)
plot(CTCFcoverage,type='l',
     ylab="Cross-coverage",
     xlab="Fragment length") 

allcoverages = crosscoverage(exampleExp)
dim(allcoverages)
for(i in 1:ncol(allcoverages)) lines(allcoverages[,i],col=i)

Retrieve duplication rates

Description

Retrieve duplication rates.

Methods

signature(object = "ChIPQCexperiment", bFiltered): Retrieve a vector of the duplication rates for each sample in an experiment. A read is considered duplicated if another read maps to the same genomic location; the duplication rate is the number of duplicated reads divided by the total number of reads for a sample. If bFiltered=TRUE (or is missing), only reads that pass the mapping quality filter for each sample are included. if bFiltered=FALSE, all reads for each sample will be included.
signature(object = "list", bFiltered): Retrieve a vector of the duplication rates for each sample in a list of ChIPQCsample objects. A read is considered duplicated if another read maps to the same genomic location; the duplication rate is the number of duplicated reads divided by the total number of reads for a sample. If bFiltered=TRUE (or is missing), only reads that pass the mapping quality filter for each sample are included. if bFiltered=FALSE, all reads for each sample will be included.
signature(object = "ChIPQCsample", bFiltered): Retrieve the duplication rate for a sample. A read is considered duplicated if another read maps to the same genomic location; the duplication rate is the number of duplicated reads divided by the total number of reads for the sample. If bFiltered=TRUE (or is missing), only reads that pass the mapping quality filter for the sample are included. if bFiltered=FALSE, all reads for the sample will be included.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
duplicateRate(exampleExp)
duplicateRate(QCsample(exampleExp,1))
data(example_QCexperiment)
duplicateRate(exampleExp)
duplicateRate(QCsample(exampleExp,1))

Retrieve numbers of duplicate reads.

Description

Retrieve the numbers of duplicate reads.

Methods

signature(object = "ChIPQCexperiment", bFiltered): Retrieve a vector of the numbers of duplicate reads for each sample in an experiment. A read is considered duplicated if another read maps to the same genomic location. If bFiltered=TRUE (or is missing), this will be the number of duplicates that pass the mapping quality filter for each sample. if bFiltered=FALSE, it will be the total number of duplicates for each sample.
signature(object = "list", bFiltered): Retrieve a vector of the numbers of duplicate reads for each sample in a list of ChIPQCsample objects. A read is considered duplicated if another read maps to the same genomic location. If bFiltered=TRUE (or is missing), this will be the number of duplicates that pass the mapping quality filter for each sample. if bFiltered=FALSE, it will be the total number of duplicates for each sample.
signature(object = "ChIPQCsample", bFiltered): Retrieve the number of duplicates for a sample. A read is considered duplicated if another read maps to the same genomic location. If bFiltered=TRUE (or is missing), this will be the number of duplicates that pass the mapping quality filter. if bFiltered=FALSE, it will be the total number of duplicates for the sample.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
duplicates(exampleExp)
duplicates(QCsample(exampleExp,1))
data(example_QCexperiment)
duplicates(exampleExp)
duplicates(QCsample(exampleExp,1))

Retrieve numbers of reads that pass various filters

Description

Retrieve numbers of reads that pass various filters

Methods

signature(object = "ChIPQCexperiment")

Retrieve a matrix of counts passing various filters for all the samples in an experiment. Each column represents the counts in a sample.

signature(object = "list")

Retrieve a matrix of counts passing various filters for all the samples in a list of ChIPQCsample objects. Each column represents the counts in a sample.

signature(object = "ChIPQCsample")

Retrieve a vector of counts passing various filters. The values are:

UnMapped	Number of reads that are not mapped (aligned)
Mapped	Number of reads that are mapped (aligned)
Duplicates	Number of reads that align to exactly the same place as another read
MapQPass	Number of reads with a mapping quality score greater than or equal to the specified threshold
MapQPassandDup	Number of reads that are mapped (aligned) and not duplicates

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
flagtagcounts(exampleExp)
flagtagcounts(QCsample(exampleExp,1))
data(example_QCexperiment)
flagtagcounts(exampleExp)
flagtagcounts(QCsample(exampleExp,1))

Retrieve the estimated fragment length

Description

Retrieve the estimated fragment length.

Methods

signature(object = "ChIPQCexperiment"): Retrieve a vector of estimated fragments sizes, one for each sample in the experiment.
signature(object = "list"): Retrieve a vector of estimated fragments sizes, one for each sample in a list of ChIPQCsample objects.
signature(object = "ChIPQCsample", width): Retrieve the estimated fragment length for a sample. If width is missing, the readlength derived from the bam file is used as the read length.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
fragmentlength(exampleExp)
fragmentlength(QCsample(exampleExp,1))
data(example_QCexperiment)
fragmentlength(exampleExp)
fragmentlength(QCsample(exampleExp,1))

Retrieve fraction of reads in peaks

Description

Retrieve the fraction of reads in peaks

Methods

signature(object = "ChIPQCexperiment"): Retrieve a vector of values representing the proportion of reads that overlap peaks for each sample in an experiment.
signature(object = "list"): Retrieve a vector of values representing the proportion of reads that overlap peaks for each sample in a list of ChIPQCsample objects.
signature(object = "ChIPQCsample"): Retrieve a value representing the proportion of reads that overlap the peaks for a sample.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
frip(exampleExp)
frip(QCsample(exampleExp,1))
data(example_QCexperiment)
frip(exampleExp)
frip(QCsample(exampleExp,1))

Retrieve numbers of mapped reads

Description

Retrieve the numbers of mapped reads.

Methods

signature(object = "ChIPQCexperiment"): Retrieve a vector of the numbers of mapped (aligned) reads for each sample in an experiment.
signature(object = "list"): Retrieve a vector of the numbers of mapped (aligned) reads for each sample in a list of ChIPQCsample objects.
signature(object = "ChIPQCsample"): Retrieve the number of mapped (aligned) reads in a sample.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
mapped(exampleExp)
mapped(QCsample(exampleExp,1))
data(example_QCexperiment)
mapped(exampleExp)
mapped(QCsample(exampleExp,1))

Retrieve normalised average peak profiles

Description

Retrieve normalised average peak profiles

Methods

signature(object = "ChIPQCexperiment"): Retrieve a matrix of normalised average peak signal data for all samples in a ChIP-seq experiment. Each column represents a sample, and each row a base pair position, centered on peak summits.
signature(object = "list"): Retrieve a matrix of normalised average peak signal data for all samples in a list of ChIPQCsample objects. Each column represents a sample, and each row a base pair position, centered on peak summits.
signature(object = "ChIPQCsample"): Retrieve a vector representing the normalised average peak profile for a sample. Each column represents a basepair position, centered on the peak summits.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
CTCFprofile = Normalisedaveragepeaksignal(QCsample(exampleExp,1))
length(CTCFprofile)
plot(CTCFprofile,type='l',ylab="normalised mean pileup")

allprofiles = Normalisedaveragepeaksignal(exampleExp)
dim(allprofiles)
for(i in 1:ncol(allprofiles)) lines(allprofiles[,i],col=i)
data(example_QCexperiment)
CTCFprofile = Normalisedaveragepeaksignal(QCsample(exampleExp,1))
length(CTCFprofile)
plot(CTCFprofile,type='l',ylab="normalised mean pileup")

allprofiles = Normalisedaveragepeaksignal(exampleExp)
dim(allprofiles)
for(i in 1:ncol(allprofiles)) lines(allprofiles[,i],col=i)

Retrieve peaks

Description

Retrieve peaks.

Methods

signature(object = "ChIPQCexperiment"): Retrieve a GRangesList of the peaks associated with all the samples in an experiment.
signature(object = "list"): Retrieve a GRangesList of the peaks associated with all the samples in a list of ChIPQCsample objects.
signature(object = "ChIPQCsample"): Retrieve a GRanges object containing the peaks associated with a sample.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
expPeaks = peaks(exampleExp)
length(expPeaks)
peaks(QCsample(exampleExp,1))
data(example_QCexperiment)
expPeaks = peaks(exampleExp)
length(expPeaks)
peaks(QCsample(exampleExp,1))

Generate Cross-Coverage plots

Description

Generate Cross-Coverage plots.

Methods

signature(object = "ChIPQCexperiment", method)

Generate cross-coverage plots for all the samples in an experiment.

signature(object = "list", method)

Generate cross-coverage plots for list of samples in an experiment.

signature(object = "ChIPQCsample", methods)

Generate cross-coverage plots for a sample. Supported methods include:

"Coverage" [default]

Coverage plot

Note

plotCC uses ggplot2 for plotting, and returns a ggplot2 plot dataframe.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
plotCC(exampleExp)
plotCC(exampleExp,excludedBox=TRUE)
plotCC(QCsample(exampleExp,1))
plotCC(QCsample(exampleExp)[1:4])
data(example_QCexperiment)
plotCC(exampleExp)
plotCC(exampleExp,excludedBox=TRUE)
plotCC(QCsample(exampleExp,1))
plotCC(QCsample(exampleExp)[1:4])

Generate Correlation Heatmap for ChIP samples

Description

Generates correlation heatmap for ChIP samples.

Methods

signature(object = "ChIPQCexperiment", attributes, ...)

Generate correlation heatmap, including clustering dendrogram, for all the samples in an experiment that are associated with a peakset.

attributes	character string, or vector of character strings, representing metadata field names, for use in labeling
...	additional parameters passed to `dba.plotHeatmap`

Note

plotCorHeatmap uses dba.plotHeatmap for plotting.

Author(s)

Rory Stark and Thomas Carroll

Examples

data(tamoxifen_QC)
plotCorHeatmap(tamoxifen,attributes=c("Tissue","Condition","Replicate"))
data(tamoxifen_QC)
plotCorHeatmap(tamoxifen,attributes=c("Tissue","Condition","Replicate"))

Generate coverage histogram plot

Description

Generate coverage histogram plot.

Methods

signature(object = "ChIPQCexperiment"): Generate coverage histogram plots for all the samples in an experiment.
signature(object = "list"): Generate coverage histogram plots for all the samples in a list of ChIPQCsamples.
signature(object = "ChIPQCsample"): Generate coverage histogram plots for a sample.

Note

Uses ggplot2 for plotting, and returns a ggplot2 plot dataframe.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
plotCoverageHist(exampleExp,facetBy=c("Tissue","Factor"))
plotCoverageHist(QCsample(exampleExp,1))
data(example_QCexperiment)
plotCoverageHist(exampleExp,facetBy=c("Tissue","Factor"))
plotCoverageHist(QCsample(exampleExp,1))

Generate fraction of reads in blacklist plot

Description

Generate fraction of reads in blacklist plot.

Methods

signature(object = "ChIPQCexperiment", type="barstacked",facet=T, facetBy=c("Tissue","Factor"),AsPercent=TRUE): Generate fraction of reads in blacklist plots for all the samples in an experiment.
signature(object = "list", type="barstacked",facet=T, facetBy=c("Sample"),AsPercent=TRUE): Generate fraction of reads in blacklist plots for all the samples in a list of ChIPQCsample objects..
signature(object = "ChIPQCsample", type="barstacked",AsPercent=TRUE): Generate fraction of reads in blacklist plots for a sample.

Note

plotFribl uses ggplot2 for plotting, and returns a ggplot2 plot dataframe.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
plotFribl(exampleExp)
plotFribl(QCsample(exampleExp,1))
data(example_QCexperiment)
plotFribl(exampleExp)
plotFribl(QCsample(exampleExp,1))

Generate fraction of reads in peaks plot

Description

Generate fraction of reads in peaks plot.

Methods

signature(object = "ChIPQCexperiment", type="barstacked",facet=T, facetBy=c("Tissue","Factor"),AsPercent=TRUE): Generate fraction of reads in peaks plots for all the samples in an experiment.
signature(object = "list", type="barstacked",facet=T, facetBy=c("Sample"),AsPercent=TRUE): Generate fraction of reads in peaks plots for all the samples in a list of ChIPQCsample objects.
signature(object = "ChIPQCsample",type="barstacked",facet=T, facetBy=c("Tissue","Factor"),AsPercent=TRUE): Generate fraction of reads in peaks plots for a sample.

Note

plotFrip usesggplot2 for plotting, and returns a ggplot2 plot dataframe.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
plotFrip(exampleExp)
plotFrip(QCsample(exampleExp,1))
data(example_QCexperiment)
plotFrip(exampleExp)
plotFrip(QCsample(exampleExp,1))

Generate peak profile plots

Description

Generate peak profile plots.

Methods

signature(object = "ChIPQCexperiment", method): Generate peak profile plots for all the samples in an experiment.
signature(object = "list", method): Generate peak profile plots for all the samples in a list of ChIPQCsample objects..
signature(object = "ChIPQCsample", method): Generate peak profile plots for a sample.

Note

plotPeakProfile uses ggplot2 for plotting, and returns a ggplot2 plot dataframe.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
plotCoverageHist(exampleExp,facetBy=c("Tissue","Factor"))
plotCoverageHist(QCsample(exampleExp,1))
data(tamoxifen_QC)
plotCoverageHist(tamoxifen,facetBy=c("Tissue","Condition"))
data(example_QCexperiment)
plotCoverageHist(exampleExp,facetBy=c("Tissue","Factor"))
plotCoverageHist(QCsample(exampleExp,1))
data(tamoxifen_QC)
plotCoverageHist(tamoxifen,facetBy=c("Tissue","Condition"))

Generate Principal Components Analysis plot for ChIP samples

Description

Generate principal components analysis plot for ChIP samples.

Methods

signature(object = "ChIPQCexperiment", attributes, ...)

Generate principal components analysis plot, for all the samples in an experiment that are associated with a peakset.

attributes	character string, or vector of character strings, representing metadata field names, for use grouping samples by color.
...	additional parameters passed to `dba.plotPCA`

Note

plotPrincomp uses dba.plotPCA for plotting.

Author(s)

Rory Stark and Thomas Carroll

Examples

data(tamoxifen_QC)
plotPrincomp(tamoxifen,attributes=c("Condition"))
data(tamoxifen_QC)
plotPrincomp(tamoxifen,attributes=c("Condition"))

Generate reads in peaks plot

Description

Generate reads in peaks plot.

Methods

signature(object = "ChIPQCexperiment", type="barstacked",facet=T, facetBy=c("Tissue","Factor")): Generate reads in peaks plots for all the samples in an experiment.
signature(object = "list", type="barstacked",facet=T, facetBy=c("Sample")): Generate reads in peaks plots for all the samples in a list of ChIPQCsample objects.
signature(object = "ChIPQCexperiment", type="barstacked",facet=T, facetBy=c("Tissue","Factor")): Generate reads in peaks plots for all the samples in an experiment.
signature(object = "ChIPQCsample",type="barstacked",facet=T, facetBy=c("Tissue","Factor")): Generate reads in peaks plots for a sample.

Note

plotRap uses ggplot2 for plotting, and returns a ggplot2 plot dataframe.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
plotRap(exampleExp,facetBy=c("Tissue","Factor"))
plotRap(QCsample(exampleExp,1))
data(example_QCexperiment)
plotRap(exampleExp,facetBy=c("Tissue","Factor"))
plotRap(QCsample(exampleExp,1))

Generate relative enrichment of genomic features plot

Description

Generate relative enrichment of genomic features plot.

Methods

signature(object = "ChIPQCexperiment", facet=T, facetBy=c("Tissue","Factor")): Generate relative enrichment of genomic features plots for all the samples in an experiment.
signature(object = "list",facet=T, facetBy=c("Sample")): Generate relative enrichment of genomic features plots for all the samples in a list of ChIPQCsample objects.
signature(object = "ChIPQCsample"): Generate relative enrichment of genomic features plots for a sample.

Note

plotRegi uses ggplot2 for plotting, and returns a ggplot2 plot dataframe.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
plotRegi(exampleExp,facetBy=c("Tissue","Factor"))
plotRegi(QCsample(exampleExp,1))
data(tamoxifen_QC)
plotRegi(tamoxifen,facetBy=c("Tissue","Condition"))
data(example_QCexperiment)
plotRegi(exampleExp,facetBy=c("Tissue","Factor"))
plotRegi(QCsample(exampleExp,1))
data(tamoxifen_QC)
plotRegi(tamoxifen,facetBy=c("Tissue","Condition"))

Generate SSD plot

Description

Generate SSD metric plot. If blacklists supplied, will generate SSD prior and post blacklisting

Methods

signature(object = "ChIPQCexperiment", facet=T, facetBy=c("Tissue","Factor")): Generate SSD metric plot for all samples in experiment. If blacklists supplied, will generate SSD prior and post blacklisting
signature(object = "list", facet=T, facetBy=c("Tissue","Factor")): Generate SSD metric plot for list of samples in experiment. If blacklists supplied, will generate SSD prior and post blacklisting
signature(object = "ChIPQCsample"): Generate SSD metric plot for single sample. If blacklists supplied, will generate SSD prior and post blacklisting

Note

plotSSD uses ggplot2 for plotting, and returns a ggplot2 plot gg object.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
plotSSD(exampleExp,facetBy=c("Tissue","Factor"))
plotSSD(QCsample(exampleExp,1))
plotSSD(QCsample(exampleExp)[1:3])
data(tamoxifen_QC)
plotSSD(tamoxifen,facetBy=c("Tissue","Condition"))
data(example_QCexperiment)
plotSSD(exampleExp,facetBy=c("Tissue","Factor"))
plotSSD(QCsample(exampleExp,1))
plotSSD(QCsample(exampleExp)[1:3])
data(tamoxifen_QC)
plotSSD(tamoxifen,facetBy=c("Tissue","Condition"))

Retrieve an annotation description, or a processed annotation

Description

Retrieve an annotation description, or a processed annotation, from a ChIPQCexperiment object.

Methods

signature(object = "ChIPQCexperiment", bRetrieve=FALSE): Retrieve the annotation. If bRetrieve=FALSE (default), the character string describing the annotation is returned (currently only “hg19” is supported). If bRetrieve=TRUE, a processed annotation is returned (in the form of a list). This can be used in subsequent calls to ChIPQC and/or ChIPQCsample for efficiency purposes.

Author(s)

Rory Stark and Thomas Carroll

Examples

data(example_QCexperiment)
QCannotation(exampleExp)
data(example_QCexperiment)
QCannotation(exampleExp)

Retrieve control objects associated with a sample

Description

Retrieve the ChIPQCsample objects representing controls, or a specific ChIPQCsample representing the control for a specific sample, from a ChIPQCexperiment object.

Methods

signature(object = "ChIPQCexperiment", sampleID): Get the control sample (as a ChIPQCsample object) associated with a ChIP sample, or, if sampleID is missing, a list of all samples used as controls. sampleID is a character string or an integer.

Author(s)

Rory Stark and Thomas Carroll

Examples

data(tamoxifen_QC)
controls = QCcontrol(tamoxifen)
length(controls)
names(controls)
controls[[1]]
bt474control = QCcontrol(tamoxifen,"BT4741")
bt474control
data(tamoxifen_QC)
controls = QCcontrol(tamoxifen)
length(controls)
names(controls)
controls[[1]]
bt474control = QCcontrol(tamoxifen,"BT4741")
bt474control

Retrieve the `DBA-object` object associated with an experiment

Description

Retrieve the DBA-object object associated with a ChIPQCexperiment object.

Methods

signature(object = "ChIPQCexperiment"): Retrieves the DBA-object object associated with a ChIPQCexperiment. This object can be used with DiffBind functions to further analyse a ChIP-seq experiment, including performing a differential binding analysis.

Author(s)

Rory Stark and Thomas Carroll

Examples

data(tamoxifen_QC)
tamoxifenDBA = QCdba(tamoxifen)
## library(DiffBind)
## tamoxifenDBA
data(tamoxifen_QC)
tamoxifenDBA = QCdba(tamoxifen)
## library(DiffBind)
## tamoxifenDBA

Retrieve metadata associated with an experiment

Description

Retrieve metadata for a ChIPQCexperiment object.

Methods

signature(object = "ChIPQCexperiment"): Retrieve a data frame containing metadata for all the samples in a ChIP-seq experiment represented by a ChIPQCexperiment object.
signature(object = "list"): Retrieve a data frame containing metadata for all the samples in a list of ChIPQCsample objects.

Author(s)

Rory Stark and Thomas Carroll

Examples

data(tamoxifen_QC)
meta = QCmetadata(tamoxifen)
meta
data(tamoxifen_QC)
meta = QCmetadata(tamoxifen)
meta

Retrieve consolidated set of QC metrics

Description

Retrieves a consolidated set of QC metrics.

Methods

signature(object = "ChIPQCexperiment"): Retrieves a matrix of QC metrics for all the samples in an experiment, with a column of values for each sample.
signature(object = "list"): Retrieves a matrix of QC metrics for all the samples in a list of ChIPQCsample objects, with a column of values for each sample.
signature(object = "ChIPQCsample"): Retrieves a vector of QC metrics for a sample.

Author(s)

Rory Stark and Thomas Carroll

Examples

data(example_QCexperiment)
QCmetrics(exampleExp)

data(tamoxifen_QC)
QCmetrics(tamoxifen)

QCmetrics(QCsample(tamoxifen,1))
data(example_QCexperiment)
QCmetrics(exampleExp)

data(tamoxifen_QC)
QCmetrics(tamoxifen)

QCmetrics(QCsample(tamoxifen,1))

Retrieve sample objects associated with an experiment

Description

Retrieves a list of ChIPQCsample objects, or one specific ChIPQCsample object, from a ChIPQCexperiment object.

Methods

signature(object = "ChIPQCexperiment", sampleID): If sampleID is missing or equal to 0, the full list of ChIPQCsample objects is returned. If sampleID is an integer n, the ChIPQCsample object corresponding to the nth sample is returned. If sampleID is a character string, the ChIPQCsample object corresponding to the sample with that ID is returned.

Author(s)

Rory Stark and Thomas Carroll

Examples

data(example_QCexperiment)
samples = QCsample(exampleExp)
length(samples)
names(samples)
samples$CTCF_1
data(example_QCexperiment)
samples = QCsample(exampleExp)
length(samples)
names(samples)
samples$CTCF_1

Retrieve read length values

Description

Retrieve read length values.

Methods

signature(object = "ChIPQCexperiment"): Retrieve a vector of read length values, one for each sample in an experiment.
signature(object = "list"): Retrieve a vector of read length values, one for each sample in a list of ChIPQCsample objects.
signature(object = "ChIPQCsample"): Retrieve the read length value for a sample.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
readlength(exampleExp)
readlength(QCsample(exampleExp,1))
data(example_QCexperiment)
readlength(exampleExp)
readlength(QCsample(exampleExp,1))

Retrieve the cross coverage values when extending reads to the optimal fragment length.

Description

Retrieve the cross coverage values when extending reads to the optimal fragment length.

Methods

signature(object = "ChIPQCexperiment"): Retrieve a vector of cross-coverage values for all samples in a ChIP-seq experiment, when all reads are shifted by the optimal fragment length (the maximum cross-coverage value).
signature(object = "list"): Retrieve a vector of cross-coverage values for all samples in a list of ChIPQCsample objects, when all reads are shifted by the optimal fragment length (the maximum cross-coverage value).
signature(object = "ChIPQCsample"): Retrieve the cross-coverage value for a sample, when all reads are shifted by the optimal fragment length (the maximum cross-coverage value).

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
FragmentLengthCrossCoverage(exampleExp)
FragmentLengthCrossCoverage(QCsample(exampleExp,1))
data(example_QCexperiment)
FragmentLengthCrossCoverage(exampleExp)
FragmentLengthCrossCoverage(QCsample(exampleExp,1))

Retrieve the cross coverage values without extending reads

Description

Retrieve the cross coverage values without extending reads.

Methods

signature(object = "ChIPQCexperiment"): Retrieve a vector of cross-coverage values for all samples in a ChIP-seq experiment, with no shift.
signature(object = "list"): Retrieve a vector of cross-coverage values for all samples in a list of ChIPQCsample objects, with no shift.
signature(object = "ChIPQCsample"): Retrieve the cross-coverage value for a sample, with no shift.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
ReadLengthCrossCoverage(exampleExp)
ReadLengthCrossCoverage(QCsample(exampleExp,1))
data(example_QCexperiment)
ReadLengthCrossCoverage(exampleExp)
ReadLengthCrossCoverage(QCsample(exampleExp,1))

Retrieve numbers of reads

Description

Retrieve the numbers of reads.

Methods

signature(object = "ChIPQCexperiment", bFiltered): Retrieve a vector of the numbers of reads for each sample in an experiment. If bFiltered=TRUE (or is missing), this will be the number of reads that pass the mapping quality filter for each sample. if bFiltered=FALSE, it will be the total number of reads for each sample.
signature(object = "list", bFiltered): Retrieve a vector of the numbers of reads for each sample in a list of ChIPQCsample objects. If bFiltered=TRUE (or is missing), this will be the number of reads that pass the mapping quality filter for each sample. if bFiltered=FALSE, it will be the total number of reads for each sample.
signature(object = "ChIPQCsample", bFiltered): Retrieve the number of reads for a sample. If bFiltered=TRUE (or is missing), this will be the number of reads that pass the mapping quality filter. if bFiltered=FALSE, it will be the total number of reads for the sample.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
reads(exampleExp)
reads(QCsample(exampleExp,1))
data(example_QCexperiment)
reads(exampleExp)
reads(QCsample(exampleExp,1))

Retrieve genomic profile information

Description

Retrieve genomic profile information in terms of relative enrichment over background genomic distribution.

Methods

signature(object = "ChIPQCexperiment")

Retrieve a matrix of relative enrichment values for a variety of genomic features. Each column represents the enrichment values for one sample in the experiment.

signature(object = "list")

Retrieve a matrix of relative enrichment values for a variety of genomic features. Each column represents the enrichment values for one sample in a list of ChIPQCsample objects.

signature(object = "ChIPQCsample")

Retrieve a vector of relative enrichment values for a variety of genomic features for a sample. Relative enrichment is computed as the proportion of reads overlapping a genomic feature type compared to the overall proportion of base pairs in the genome comprising those features. Genomic features include:

3UTRs	3' UTRs
5UTRs	5' UTRs
Introns	Intronic (non-coding) portions of gene bodies
Transcripts	Transcribed regions, including exons
Promoters500	500bp regions immediately upstream of annotated TSSs
Promoters2000to500	2500bp regions from 2000bp immediately upstream of annotated TSSs to 500bp downstream
Promoters20000to2000	22000bp regions from 20000bp immediately upstream of annotated TSSs to 2000bp downstream

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
genomicprofile = regi(exampleExp)
heatmap(genomicprofile)
regi(QCsample(exampleExp,1))
data(example_QCexperiment)
genomicprofile = regi(exampleExp)
heatmap(genomicprofile)
regi(QCsample(exampleExp,1))

Retrieve the relative cross coverage values for a range of shift sizes

Description

Retrieve the relative cross-coverage values for a range of shift sizes

Methods

signature(object = "ChIPQCexperiment"): Retrieve a vector of relative cross-coverage values for all samples in a ChIP-seq experiment, computed based on the maximal value (when extending the reads to the optimal fragment length) versus the cross-coverage values using non-extended reads.
signature(object = "list"): Retrieve a vector of relative cross-coverage values for all samples in a list of ChIPQCsample objects, computed based on the maximal value (when extending the reads to the optimal fragment length) versus the cross-coverage values using non-extended reads.
signature(object = "ChIPQCsample"): Retrieve the relative cross-coverage value for a sample, computed based on the maximal value (when extending the reads to the optimal fragment length) versus the cross-coverage values using non-extended reads.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
RelativeCrossCoverage(exampleExp)
RelativeCrossCoverage(QCsample(exampleExp,1))
data(example_QCexperiment)
RelativeCrossCoverage(exampleExp)
RelativeCrossCoverage(QCsample(exampleExp,1))

Retrieve numbers of reads overlapping blacklisted regions

Description

Retrieve the numbers of reads overlapping blacklisted regions.

Methods

signature(object = "ChIPQCexperiment"): Retrieve a vector of the numbers of reads overlapping blacklisted regions for each sample in an experiment.
signature(object = "list"): Retrieve a vector of the numbers of reads overlapping blacklisted regions for each sample in a list of ChIPQCsample objects.
signature(object = "ChIPQCsample"): Retrieve the number of reads overlapping blacklisted regions in a sample.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
ribl(exampleExp)
ribl(QCsample(exampleExp,1))
data(example_QCexperiment)
ribl(exampleExp)
ribl(QCsample(exampleExp,1))

Retrieve numbers of reads overlapping peaks

Description

Retrieve the numbers of reads overlapping peaks.

Methods

signature(object = "ChIPQCexperiment"): Retrieve a vector of the numbers of reads overlapping peaks for each sample in an experiment.
signature(object = "list"): Retrieve a vector of the numbers of reads overlapping peaks for each sample in a list of ChIPQCsample objects.
signature(object = "ChIPQCsample"): Retrieve the number of reads overlapping peaks in a sample.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
rip(exampleExp)
rip(QCsample(exampleExp,1))
data(example_QCexperiment)
rip(exampleExp)
rip(QCsample(exampleExp,1))

Retrieve SSD (squared sum of deviations) values of peak coverage

Description

Retrieve SSD (squared sum of deviations) values of peak coverage density.

Methods

signature(object = "ChIPQCexperiment"): Retrieve a vector of SSD values, one for each sample in an experiment.
signature(object = "list"): Retrieve a vector of SSD values, one for each sample in a list of ChIPQCsample objects.
signature(object = "ChIPQCsample"): Retrieve the SSD (squared sum of deviations) for a sample, computed from the standard deviation of the coveragehistogram.

Note

uses the SSD calculation from the chipseq package.

Author(s)

Thomas Carroll and Rory Stark

Examples

data(example_QCexperiment)
ssd(exampleExp)
ssd(QCsample(exampleExp,1))
data(example_QCexperiment)
ssd(exampleExp)
ssd(QCsample(exampleExp,1))

Package 'ChIPQC'

Help Index

ChIPQC - Quality metrics for ChIPseq data

Description

Details

Author(s)

References

See Also

Retrieve average peak profiles

Description

Methods

Author(s)

See Also

Examples

Construct a ChIPQCexperiment object

Description

Usage

Arguments

Details

Value

Note

Author(s)

See Also

Examples

Example data sets for ChIPQC package, each containing a ChIPQCexperiment object, as well as a pre-compiled blacklist for hg19.

Description

Usage

Format

Source

Examples

Deprecated functions in package ‘ChIPQC’

Description

Details

ChIPQCexperiment instances

Description

Slots

Extends

Methods

Author(s)

See Also

Examples

Generate a summary QC report

Description

Methods

Note

Author(s)

See Also

Class "ChIPQCsample"

Description

Objects from the Class

Constructor Function

Slots

Extends

Methods

Author(s)

References

See Also

Examples

Retrieve histogram data representing densities of coverage pileups

Description

Methods

Author(s)

See Also

Examples

Retrieve the cross coverage values for a range of shift sizes

Description

Methods

Author(s)

See Also

Examples

Retrieve duplication rates

Description

Methods

Author(s)

See Also

Examples

Retrieve numbers of duplicate reads.

Description

Methods

Author(s)

Construct a `ChIPQCexperiment` object

Example data sets for `ChIPQC` package, each containing a `ChIPQCexperiment` object, as well as a pre-compiled blacklist for hg19.

Class `"ChIPQCsample"`