| Title: | An Integrative Tool for ChIP- And RNA-Seq Based Primary Transcripts Detection and Quantification |
|---|---|
| Description: | The differences in the RNA types being sequenced have an impact on the resulting sequencing profiles. mRNA-seq data is enriched with reads derived from exons, while GRO-, nucRNA- and chrRNA-seq demonstrate a substantial broader coverage of both exonic and intronic regions. The presence of intronic reads in GRO-seq type of data makes it possible to use it to computationally identify and quantify all de novo continuous regions of transcription distributed across the genome. This type of data, however, is more challenging to interpret and less common practice compared to mRNA-seq. One of the challenges for primary transcript detection concerns the simultaneous transcription of closely spaced genes, which needs to be properly divided into individually transcribed units. The R package transcriptR combines RNA-seq data with ChIP-seq data of histone modifications that mark active Transcription Start Sites (TSSs), such as, H3K4me3 or H3K9/14Ac to overcome this challenge. The advantage of this approach over the use of, for example, gene annotations is that this approach is data driven and therefore able to deal also with novel and case specific events. Furthermore, the integration of ChIP- and RNA-seq data allows the identification all known and novel active transcription start sites within a given sample. |
| Authors: | Armen R. Karapetyan <[email protected]> |
| Maintainer: | Armen R. Karapetyan <[email protected]> |
| License: | GPL-3 |
| Version: | 1.35.0 |
| Built: | 2024-11-19 04:44:15 UTC |
| Source: | https://github.com/bioc/transcriptR |
Add feature(s) to the ChipDataSet object.
addFeature(object, feature) ## S4 method for signature 'ChipDataSet' addFeature(object, feature)addFeature(object, feature) ## S4 method for signature 'ChipDataSet' addFeature(object, feature)
object |
A |
feature |
|
The user specified feature(s) will be added to the slot features
of the provided ChipDataSet object.
Armen R. Karapetyan
constructCDS predictTssOverlap
### Load ChipDataSet object data(cds) ### As an example create some fake data N <- length(getPeaks(cds)) fake.data <- rnorm(n = N) addFeature(object = cds, feature = list(fake = fake.data)) ### View newly added feature getPeaks(cds)### Load ChipDataSet object data(cds) ### As an example create some fake data N <- length(getPeaks(cds)) fake.data <- rnorm(n = N) addFeature(object = cds, feature = list(fake = fake.data)) ### View newly added feature getPeaks(cds)
annot is an object of GRanges class, containing
genomic coordinates of the genes located on human chromosome 15 (chr15:63261757-84081194).
annotannot
GRanges object
GRanges object
Annotate detected transcripts by the available reference annotations based on genomic overlap.
annotateTranscripts(object, annot, min.overlap = 0.3) ## S4 method for signature 'TranscriptionDataSet,GRanges' annotateTranscripts(object, annot, min.overlap = 0.3)annotateTranscripts(object, annot, min.overlap = 0.3) ## S4 method for signature 'TranscriptionDataSet,GRanges' annotateTranscripts(object, annot, min.overlap = 0.3)
object |
A |
annot |
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min.overlap |
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Genomic overlap between transcript and annotation is calculated as the mean of two proportions: 1) proportion of the transcript length overlapping annotation; 2) proportion of the annotation length overlapping transcript. This approach levels off differences in length between transcript and annotation and, thus better suitable for cases in which the length of either transcript or annotation is much longer than of compared element.
If there is an overlap between transcript and annotation, the ID of the associated annotation will be linked to the transcript.
An extra column annotation.overlap will be added to the metadata
portion of the GRanges object which is
stored in the transcripts slot of the provided
TranscriptionDataSet object.
Armen R. Karapetyan
### Load TranscriptionDataSet object data(tds) ### Load reference annotations (knownGene from UCSC) data(annot) ### Detect transcripts detectTranscripts(object = tds, coverage.cutoff = 5, gap.dist = 4000, estimate.params = TRUE, combine.by.annot = FALSE, annot = annot) ### Annotate detected transcripts annotateTranscripts(object = tds, annot = annot) ### View detected transcripts and associated annotations getTranscripts(tds)### Load TranscriptionDataSet object data(tds) ### Load reference annotations (knownGene from UCSC) data(annot) ### Detect transcripts detectTranscripts(object = tds, coverage.cutoff = 5, gap.dist = 4000, estimate.params = TRUE, combine.by.annot = FALSE, annot = annot) ### Annotate detected transcripts annotateTranscripts(object = tds, annot = annot) ### View detected transcripts and associated annotations getTranscripts(tds)
The function divides closely spaced transcripts into individually transcribed units using the detected active transcription start sites.
breakTranscriptsByPeaks(tdsObj, cdsObj, estimate.params = TRUE) ## S4 method for signature 'TranscriptionDataSet,ChipDataSet' breakTranscriptsByPeaks(tdsObj, cdsObj, estimate.params = TRUE)breakTranscriptsByPeaks(tdsObj, cdsObj, estimate.params = TRUE) ## S4 method for signature 'TranscriptionDataSet,ChipDataSet' breakTranscriptsByPeaks(tdsObj, cdsObj, estimate.params = TRUE)
tdsObj |
A |
cdsObj |
A |
estimate.params |
|
One of the challenges for primary transcript detection concerns the
simultaneous transcription of closely spaced genes, which needs to be
properly divided into individually transcribed units. transcriptR
combines RNA-seq data with ChIP-seq data of histone modifications that
mark active Transcription Start Sites (TSSs), such as, H3K4me3 or H3K9/14Ac
to overcome this challenge. The advantage of this approach over the use of,
for example, gene annotations is that this approach is data driven and
therefore able to deal also with novel and case specific events. Furthermore,
the integration of ChIP- and RNA-seq data allows the identification all
known and novel active transcription start sites within a given sample.
Transcription initiation within a peak region is investigated by comparing
RNA-seq read densities upstream and downstream of empirically determined TSSs.
Closely spaced transcripts are divided into individually transcribed units
using the detected active TSSs.
The slot transcripts of the provided
TranscriptionDataSet object will be updated by the
GRanges object, containing transcripts and,
if estimated, corresponding expression levels.
Armen R. Karapetyan
### Load TranscriptionDataSet object data(tds) ### Load ChipDataSet object data(cds) ### Load reference annotations (knownGene from UCSC) data(annot) ### Detect transcripts detectTranscripts(object = tds, coverage.cutoff = 5, gap.dist = 4000, estimate.params = TRUE, combine.by.annot = FALSE, annot = annot) ### Classify peaks on gene associated and background predictTssOverlap(object = cds, feature = "pileup", p = 0.75) ### Predict peak 'strand' predictStrand(cdsObj = cds, tdsObj = tds, coverage.cutoff = 5, quant.cutoff = 0.1, win.size = 2500) ### If `estimate.params = TRUE`, FPKM and coverage density will be re-calculated breakTranscriptsByPeaks(tdsObj = tds, cdsObj = cds, estimate.params = TRUE) ### View detected transcripts getTranscripts(tds)### Load TranscriptionDataSet object data(tds) ### Load ChipDataSet object data(cds) ### Load reference annotations (knownGene from UCSC) data(annot) ### Detect transcripts detectTranscripts(object = tds, coverage.cutoff = 5, gap.dist = 4000, estimate.params = TRUE, combine.by.annot = FALSE, annot = annot) ### Classify peaks on gene associated and background predictTssOverlap(object = cds, feature = "pileup", p = 0.75) ### Predict peak 'strand' predictStrand(cdsObj = cds, tdsObj = tds, coverage.cutoff = 5, quant.cutoff = 0.1, win.size = 2500) ### If `estimate.params = TRUE`, FPKM and coverage density will be re-calculated breakTranscriptsByPeaks(tdsObj = tds, cdsObj = cds, estimate.params = TRUE) ### View detected transcripts getTranscripts(tds)
ChipDataSet object.cds is an object of ChipDataSet class, containing
H3K4me3 active histone mark ChIP-seq peaks from human chromosome 15
(chr15:63261757-84081194) profiled in prostate cancer LNCaP cells.
cdscds
ChipDataSet object
ChipDataSet object
The ChipDataSet is a S4 class to store input values, intermediate
calculations and results of ChIP-seq peaks analysis.
peaksGRanges. ChIP-seq peaks.
regionGRanges. Genomic region(s) to extract peaks from.
genomicAnnotationData.Frame. Genomic distribution of the peaks
at distinct genomic features (TSSs, exons, introns, intergenic regions).
featuresData.Frame. Estimated characteristics (features) of the
peaks.
tssOverlapPredictionList. Prediction of the gene associated
peaks. The following elements are reported:
'predicted.tssOverlap' - predicted class (yes - gene associated; no - background) and probability of a ChIP-seq peak being classified as gene associated.
'confusionMatrix' - cross-tabulation of observed and predicted classes with associated statistics.
'logFitSummary' - statistical significance of the predictors used in the logistic regression.
'roc' - results of the receiver operating characteristic analysis.
strandPredictionList. Prediction of the peak strandedness. The
following elements are reported:
'predicted.strand' - predicted ChIP-seq peak strand.
'probability.cutoff' - probability cutoff for q2.
'results.plus' - intermediate calculations for the forward DNA strand.
'results.minus' - intermediate calculations for the reverse DNA strand.
Armen R. Karapetyan
constructCDS
predictTssOverlap
predictStrand
The function constructs an object of class ChipDataSet, which
is a container for holding processed sequencing data and the results of
all downstream analyses. All the slots of the created object are filled
during the workflow by applying specific functions to the object directly.
constructCDS(peaks, reads, region, TxDb, tssOf = c("gene", "transcript"), tss.region = c(-2000, 2000), reduce.peaks = FALSE, gapwidth = 1000, fragment.size, unique = TRUE, swap.strand = FALSE, param = NULL)constructCDS(peaks, reads, region, TxDb, tssOf = c("gene", "transcript"), tss.region = c(-2000, 2000), reduce.peaks = FALSE, gapwidth = 1000, fragment.size, unique = TRUE, swap.strand = FALSE, param = NULL)
peaks |
A path to a file with peaks. The file needs to have at least 3 columns (tab-separated): chromosome, start (peak), end (peak). The 4th column - name (peak id) is optional. |
reads |
A path to a BAM file with sequencing reads. |
region |
|
TxDb |
|
tssOf |
|
tss.region |
A numeric vector of length two, which specifies the size of TSS region. Default: -2kb to 2kb. |
reduce.peaks |
|
gapwidth |
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fragment.size |
|
unique |
|
swap.strand |
|
param |
|
The function constructCDS initializes a
ChipDataSet object, by providing the paths to the input
files and information relevant to the ChIP-seq library preparation
procedure. During the object construction the following steps are
executed:
The peak information is converted into the object of
GRanges class.
The genomic distribution of the peaks is evaluated (exonic, intronic, intergenic, TSSs).
Each peak in the data set is functionally characterized:
length - the length of a peak (in base pairs).
fragments - total number of fragments overlapping
a peak region.
density - number of fragments per base pair of
the peak length.
pileup - highest fragment pileup in each peak
region.
tssOverlap - overlap (binary, yes/no) of the
peak with the annotated TSS region.
The estimated features are used to predict which of the peaks are gene associated in the analysis downstream.
As many peak-calling algorithms tend to divide broader peaks into the several narrower closely spaced peaks, it is advised to merge these end-to-end peaks to decrease the number of false positives and prevent unnecessary truncation of transcripts in the downstream analysis.
An object of class ChipDataSet.
Armen R. Karapetyan
### Load ChipDataSet object data(cds) ### View a short summary of the object cds### Load ChipDataSet object data(cds) ### View a short summary of the object cds
The function constructs an object of class TranscriptionDataSet,
which is a container for holding processed sequencing data and the results of
all downstream analyses. All the slots of the created object are filled
during the workflow by applying specific functions to the object directly.
constructTDS(file, region, fragment.size = 250, unique = FALSE, paired.end = FALSE, swap.strand = FALSE, param = NULL)constructTDS(file, region, fragment.size = 250, unique = FALSE, paired.end = FALSE, swap.strand = FALSE, param = NULL)
file |
A path to a BAM file with sequencing reads. |
region |
|
fragment.size |
|
unique |
|
paired.end |
|
swap.strand |
|
param |
|
The slots fragments, fragmentSize, region,
coveragePlus, coverageMinus are filled during the object
construction. The fragments holds information about genomic
coordinates of the sequenced fragments (reads extended to the fragmento
size). coveragePlus and coverageMinus for each position in
the genome counts the number of fragments that cover it (for the details,
see coverage). region holds information about
the region used for fragments extraction.
An object of class TranscriptionDataSet.
Armen R. Karapetyan
### Load TranscriptionDataSet object data(tds) ### View a short summary of the object tds### Load TranscriptionDataSet object data(tds) ### View a short summary of the object tds
The function dissects transcribed regions (transcripts) genome-wide and performs expression level quantification.
detectTranscripts(object, coverage.cutoff, gap.dist, estimate.params = TRUE, total.reads, combine.by.annot = FALSE, annot) ## S4 method for signature 'TranscriptionDataSet' detectTranscripts(object, coverage.cutoff, gap.dist, estimate.params = TRUE, total.reads, combine.by.annot = FALSE, annot)detectTranscripts(object, coverage.cutoff, gap.dist, estimate.params = TRUE, total.reads, combine.by.annot = FALSE, annot) ## S4 method for signature 'TranscriptionDataSet' detectTranscripts(object, coverage.cutoff, gap.dist, estimate.params = TRUE, total.reads, combine.by.annot = FALSE, annot)
object |
A |
coverage.cutoff |
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gap.dist |
|
estimate.params |
|
total.reads |
|
combine.by.annot |
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annot |
|
The function uses two parameters to identify transcribed regions:
coverage.cutoff and gap.dist as calculated by the
estimateBackground and estimateGapDistance,
respectively and stored in the TranscriptionDataSet object.
Alternatively, the user may specify his/her own values to be passed to
the function. By increasing the gap.dist, fewer transcripts of
longer size will be identified, and an increase in the coverage.cutoff
will result in fewer transcripts of shorter size (a typical transcript tends
to have a lower fragments coverage at the 3' end, and thus, the
coverage.cutoff value will have an impact on the resulting length of
the detected transcript).
If estimate.params is set TRUE, the following metrics are estimated for
each transcript:
length - transcript length (in base pairs).
bases.covered - the number of bases covered by the
sequencing fragments.
coverage - the proportion of transcript length covered by
fragments. Value in the range (0, 1].
fragments - total number of fragments per transcript.
fpkm - Fragments Per Kilobase of transcript per Million
mapped reads.
The coverage is a measure of how densely the transcript is covered by
the sequencing fragments. Modestly/highly expressed transcripts will have
a value close to 1, whereas lowly expressed transcripts will have a
value close to 0, indicating the sparse distribution of sequencing
fragments along the transcript body.
The slot transcripts of the provided
TranscriptionDataSet object will be updated by the
GRanges object, containing detected
transcripts and, if estimated, corresponding expression levels.
Armen R. Karapetyan
### Load TranscriptionDataSet object data(tds) ### Load reference annotations (knownGene from UCSC) data(annot) ### Detect transcripts detectTranscripts(object = tds, coverage.cutoff = 5, gap.dist = 4000, estimate.params = TRUE, combine.by.annot = FALSE, annot = annot) ### View detected transcripts getTranscripts(tds)### Load TranscriptionDataSet object data(tds) ### Load reference annotations (knownGene from UCSC) data(annot) ### Detect transcripts detectTranscripts(object = tds, coverage.cutoff = 5, gap.dist = 4000, estimate.params = TRUE, combine.by.annot = FALSE, annot = annot) ### View detected transcripts getTranscripts(tds)
Gene expression is a stochastic process, which often results in substantial
expression noise. To obtain a putative set of transcribed regions, it is
necessary to identify those regions that are expressed significantly above
the background level. Using a Poisson-based approach for estimating
the noise distribution from the frequency of the transcribed regions with the
low fragments coverage, estimateBackground function returns a
coverage cutoff value for a specific
False Discovery Rate (FDR).
estimateBackground(object, fdr.cutoff = 0.05) ## S4 method for signature 'TranscriptionDataSet' estimateBackground(object, fdr.cutoff = 0.05)estimateBackground(object, fdr.cutoff = 0.05) ## S4 method for signature 'TranscriptionDataSet' estimateBackground(object, fdr.cutoff = 0.05)
object |
A |
fdr.cutoff |
|
The slots coverageCutoffFdr and coverageCutoff
of the provided TranscriptionDataSet object will be updated by
the FDR cutoff value used in the calculations and by the corresponding
estimated coverage cutoff value, respectively.
Armen R. Karapetyan
### Load TranscriptionDataSet object data(tds) ### Estimate coverage cutoff at different FDR levels estimateBackground(object = tds, fdr = 0.01)### Load TranscriptionDataSet object data(tds) ### Estimate coverage cutoff at different FDR levels estimateBackground(object = tds, fdr = 0.01)
The ultimate goal of transcriptR is to identify continuous regions
of transcription. However, in some areas of the genome it is not possible
to detect transcription, because of the presence of the low mappability
regions and (high copy number) repeats. Sequencing reads can not be uniquely
mapped to these positions, leading to the formation of gaps in otherwise
continuous coverage profiles and segmentation of transcribed regions into
multiple smaller fragments. The gap distance describes the maximum allowed
distance between adjacent fragments to be merged into one transcript. To
choose the optimal value for the gap distance, the detected transcripts
should largely be in agreement with available reference annotations.
To accomplish this, the function is build on the methodology proposed by
Hah et al. (Cell, 2011).
In brief, the two types of erros are defined:
dissected error - the ratio of annotations that is segmented
into two or more fragments.
merged error - the ratio of non-overlapping annotations that
merged by mistake in the experimental data.
There is an interdependence between two types of errors. Increasing the gap
distance decreases the dissected error, by detecting fewer, but longer
transcripts, while the merged error will increase as more detected
transcripts will span multiple annotations. The gap distance with the lowest
sum of two error types is chosen as the optimal value.
estimateGapDistance(object, annot, coverage.cutoff, filter.annot = TRUE, fpkm.quantile = 0.25, gap.dist.range = seq(from = 0, to = 10000, by = 100)) ## S4 method for signature 'TranscriptionDataSet,GRanges' estimateGapDistance(object, annot, coverage.cutoff, filter.annot = TRUE, fpkm.quantile = 0.25, gap.dist.range = seq(from = 0, to = 10000, by = 100))estimateGapDistance(object, annot, coverage.cutoff, filter.annot = TRUE, fpkm.quantile = 0.25, gap.dist.range = seq(from = 0, to = 10000, by = 100)) ## S4 method for signature 'TranscriptionDataSet,GRanges' estimateGapDistance(object, annot, coverage.cutoff, filter.annot = TRUE, fpkm.quantile = 0.25, gap.dist.range = seq(from = 0, to = 10000, by = 100))
object |
A |
annot |
|
coverage.cutoff |
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filter.annot |
|
fpkm.quantile |
|
gap.dist.range |
A numeric vector specifying a range of gap distances to test. By default, the range is from 0 to 10000 with a step of 100. |
The slot gapDistanceTest of the provided
TranscriptionDataSet object will be updated by the
data.frame, containing estimated error rates for each
tested gap distance (see getTestedGapDistances, for the
details).
Armen R. Karapetyan
Hah N, Danko CG, Core L, Waterfall JJ, Siepel A, Lis JT, Kraus WL. A rapid, extensive, and transient transcriptional response to estrogen signaling in breast cancer cells. Cell. 2011.
constructTDS plotErrorRate
getTestedGapDistances
### Load TranscriptionDataSet object data(tds) ### Load reference annotations (knownGene from UCSC) data(annot) ### Estimate gap distance minimazing error rate ### Define the range of gap distances to test gdr <- seq(from = 0, to = 10000, by = 1000) estimateGapDistance(object = tds, annot = annot, coverage.cutoff = 5, filter.annot = FALSE, gap.dist.range = gdr) ### View estimated gap distance tds### Load TranscriptionDataSet object data(tds) ### Load reference annotations (knownGene from UCSC) data(annot) ### Estimate gap distance minimazing error rate ### Define the range of gap distances to test gdr <- seq(from = 0, to = 10000, by = 1000) estimateGapDistance(object = tds, annot = annot, coverage.cutoff = 5, filter.annot = FALSE, gap.dist.range = gdr) ### View estimated gap distance tds
RNA-seq coverage profiles for both forward and reverse DNA strand can
be visualized separately in the
UCSC genome browser using exportCoverage.
This function can generate tracks in
BigWig and
bedGraph formats,
which can be uploaded to the genome browser as custom tracks.
exportCoverage(object, file, type, strand, color, filter.by.coverage.cutoff = FALSE, coverage.cutoff = NULL, rpm = FALSE, total.reads) ## S4 method for signature 'TranscriptionDataSet' exportCoverage(object, file, type, strand, color, filter.by.coverage.cutoff = FALSE, coverage.cutoff = NULL, rpm = FALSE, total.reads)exportCoverage(object, file, type, strand, color, filter.by.coverage.cutoff = FALSE, coverage.cutoff = NULL, rpm = FALSE, total.reads) ## S4 method for signature 'TranscriptionDataSet' exportCoverage(object, file, type, strand, color, filter.by.coverage.cutoff = FALSE, coverage.cutoff = NULL, rpm = FALSE, total.reads)
object |
A |
file |
|
type |
|
strand |
|
color |
Object of class "integer" representing the track color (as from col2rgb). Only works with tracks of type "bedGraph". Default: c(0L, 0L, 255L). |
filter.by.coverage.cutoff |
|
coverage.cutoff |
|
rpm |
|
total.reads |
|
There is an option to filter coverage profiles by the coverage cutoff
value, either estimated for a specific FDR via
estimateBackground or a user specified value. By default, the
coverage cutoff value stored in the TranscriptionDataSet object
is used. In order to make an informed decision about a proper FDR level, it
is useful to explore the output at different FDR levels and determine the
optimal cutoff value.
A file in either BigWig or bedGraph format.
Armen R. Karapetyan
estimateBackground
UCSC genome browser
BigWig
### Load TranscriptionDataSet object data(tds) ### Look at the coverage profile of the regions expressed above the background level # exportCoverage(object = tds, file = "plus.bg", type = "bedGraph", strand = "+", # filter.by.coverage.cutoff = TRUE, coverage.cutoff = 3, rpm = FALSE) ### Or check the raw coverage (all expressed regions) # exportCoverage(object = tds, file = "plus_raw.bg", type = "bedGraph", # strand = "+", filter.by.coverage.cutoff = FALSE, rpm = FALSE)### Load TranscriptionDataSet object data(tds) ### Look at the coverage profile of the regions expressed above the background level # exportCoverage(object = tds, file = "plus.bg", type = "bedGraph", strand = "+", # filter.by.coverage.cutoff = TRUE, coverage.cutoff = 3, rpm = FALSE) ### Or check the raw coverage (all expressed regions) # exportCoverage(object = tds, file = "plus_raw.bg", type = "bedGraph", # strand = "+", filter.by.coverage.cutoff = FALSE, rpm = FALSE)
Retrieve a cross-tabulation of observed and predicted classes (prediction of gene associated peaks) with associated statistics.
getConfusionMatrix(object) ## S4 method for signature 'ChipDataSet' getConfusionMatrix(object)getConfusionMatrix(object) ## S4 method for signature 'ChipDataSet' getConfusionMatrix(object)
object |
A |
An object of Confusion Matrix class. For the details see
caret package.
Armen R. Karapetyan
predictTssOverlap confusionMatrix
### Load ChipDataSet object data(cds) getConfusionMatrix(cds)### Load ChipDataSet object data(cds) getConfusionMatrix(cds)
Retrieve genomic distribution of ChIP-seq peaks at distinct genomic features (exons, introns, TSSs, intergenic regions)
getGenomicAnnot(object) ## S4 method for signature 'ChipDataSet' getGenomicAnnot(object)getGenomicAnnot(object) ## S4 method for signature 'ChipDataSet' getGenomicAnnot(object)
object |
A |
A simple quality check of the supplied ChIP-seq peaks can be performed by investigating their genomic distribution. Ideally, these peaks should demonstrate substantial enrichment at TSS regions. Enrichment of the peaks at a given genomic feature (e.g. TSS) is defined as the ratio between the observed and expected number of peaks. The expected number of peaks is calculated from the proportion of the genome covered by the given genomic feature.
A four column Data.Frame, storing information about observed and
expected number of peaks at distinct genomic features.
Armen R. Karapetyan
### Load ChipDataSet object data(cds) getGenomicAnnot(cds)### Load ChipDataSet object data(cds) getGenomicAnnot(cds)
Retrieve ChIP-seq peak information from the ChipDataSet object.
getPeaks(object) ## S4 method for signature 'ChipDataSet' getPeaks(object)getPeaks(object) ## S4 method for signature 'ChipDataSet' getPeaks(object)
object |
A |
A GRanges object.
Armen R. Karapetyan
constructCDS
predictTssOverlap
predictStrand
### Load ChipDataSet object data(cds) getPeaks(cds)### Load ChipDataSet object data(cds) getPeaks(cds)
Retrieve significance of each predictor used in the classification model fit (prediction of gene associated peaks).
getPredictorSignificance(object) ## S4 method for signature 'ChipDataSet' getPredictorSignificance(object)getPredictorSignificance(object) ## S4 method for signature 'ChipDataSet' getPredictorSignificance(object)
object |
A |
A vector of p-values.
Armen R. Karapetyan
### Load ChipDataSet object data(cds) getPredictorSignificance(cds)### Load ChipDataSet object data(cds) getPredictorSignificance(cds)
Retrieve estimated P(q2) threshold, used to select peaks with a putative transcription initiation event.
getProbTreshold(object) ## S4 method for signature 'ChipDataSet' getProbTreshold(object)getProbTreshold(object) ## S4 method for signature 'ChipDataSet' getProbTreshold(object)
object |
A |
Estimated P(q2) threshold.
Armen R. Karapetyan
### Load ChipDataSet object data(cds) getProbTreshold(cds)### Load ChipDataSet object data(cds) getProbTreshold(cds)
Retrieve all internal calculations performed by predictStrand
function.
getQuadProb(object, strand) ## S4 method for signature 'ChipDataSet' getQuadProb(object, strand)getQuadProb(object, strand) ## S4 method for signature 'ChipDataSet' getQuadProb(object, strand)
object |
A |
strand |
|
A nine column Data.Frame, where each row corresponds to
a ChIP-seq peak and each column keeps one of the intermediate calculations:
max.cov - maximum coverage of the RNA-seq fragments
inside the peak region.
pass.cov.treshold - whether the max.cov exceeds
the coverage.cutoff, either user defined or estimated from
RNA-seq data by estimateBackground function call and
stored in TranscriptionDataSet object.
q1q2.sepline.coord - genomic coordinate corresponding to
the transcription start position inside the peak region.
q1.coord - genomic coordinates of q1.
q2.coord - genomic coordinates of q2.
q1.count - total number of fragments in q1.
q2.count - total number of fragments in q2.
q1.prob - probability of a fragment being sampled from
the q1.
q2.prob - probability of a fragment being sampled from
the q2.
Armen R. Karapetyan
### Load ChipDataSet object data(cds) ### Load TranscriptionDataSet object data(tds) head(getQuadProb(cds, strand = "+")) head(getQuadProb(cds, strand = "-"))### Load ChipDataSet object data(cds) ### Load TranscriptionDataSet object data(tds) head(getQuadProb(cds, strand = "+")) head(getQuadProb(cds, strand = "-"))
Retrieve a data.frame, containing a range of tested gap distances
and estimated error rates.
getTestedGapDistances(object) ## S4 method for signature 'TranscriptionDataSet' getTestedGapDistances(object)getTestedGapDistances(object) ## S4 method for signature 'TranscriptionDataSet' getTestedGapDistances(object)
object |
A |
A data.frame containing estimated error rates (dissected,
merged and sum of two errors) and corresponding gap distances.
Armen R. Karapetyan
### Load TranscriptionDataSet object data(tds) head(getTestedGapDistances(tds))### Load TranscriptionDataSet object data(tds) head(getTestedGapDistances(tds))
Retrieve transcripts information from the TranscriptionDataSet
object.
getTranscripts(object, min.length, min.fpkm, min.coverage) ## S4 method for signature 'TranscriptionDataSet' getTranscripts(object, min.length, min.fpkm, min.coverage)getTranscripts(object, min.length, min.fpkm, min.coverage) ## S4 method for signature 'TranscriptionDataSet' getTranscripts(object, min.length, min.fpkm, min.coverage)
object |
A |
min.length |
|
min.fpkm |
|
min.coverage |
|
The coverage is a measure of how densely the transcript is covered by the sequencing fragments. Modestly/highly expressed transcripts will have a value close to 1, whereas lowly expressed transcripts will have a value close to 0, indicating the sparse distribution of sequencing fragments along the transcript body.
A GRanges object.
Armen R. Karapetyan
### Load TranscriptionDataSet object data(tds) ### View detected transcripts getTranscripts(tds)### Load TranscriptionDataSet object data(tds) ### View detected transcripts getTranscripts(tds)
A convenient way to explore output of the predictions made on the ChIP peaks is to visualize them in the UCSC genome browser. The peaksToBed function returns a file in BED format, which can be uploaded directly to the genome browser. To improve the visual perception, peaks are color-coded by the predicted strand.
peaksToBed(object, file, strand.pred.color = c("blue", "red", "green4", "black"), gene.associated.peaks = TRUE) ## S4 method for signature 'ChipDataSet' peaksToBed(object, file, strand.pred.color = c("blue", "red", "green4", "black"), gene.associated.peaks = TRUE)peaksToBed(object, file, strand.pred.color = c("blue", "red", "green4", "black"), gene.associated.peaks = TRUE) ## S4 method for signature 'ChipDataSet' peaksToBed(object, file, strand.pred.color = c("blue", "red", "green4", "black"), gene.associated.peaks = TRUE)
object |
A |
file |
|
strand.pred.color |
|
gene.associated.peaks |
|
A file in the BED format.
Armen R. Karapetyan
constructCDS
predictTssOverlap
predictStrand
UCSC genome browser
BED
### Load ChipDataSet object data(cds) ### Load TranscriptionDataSet object data(tds) ### Classify peaks on gene associated and background predictTssOverlap(object = cds, feature = "pileup", p = 0.75) ### Predict peak 'strandedness' predictStrand(cdsObj = cds, tdsObj = tds, coverage.cutoff = 5, quant.cutoff = 0.1, win.size = 2500) # peaksToBed(object = cds, file = "peaks.bed", gene.associated.peaks = TRUE)### Load ChipDataSet object data(cds) ### Load TranscriptionDataSet object data(tds) ### Classify peaks on gene associated and background predictTssOverlap(object = cds, feature = "pileup", p = 0.75) ### Predict peak 'strandedness' predictStrand(cdsObj = cds, tdsObj = tds, coverage.cutoff = 5, quant.cutoff = 0.1, win.size = 2500) # peaksToBed(object = cds, file = "peaks.bed", gene.associated.peaks = TRUE)
A simple helper function that plot results of
estimateGapDistance function call.
plotErrorRate(object, color = c("#1B9E77", "#D95F02", "#7570B3"), xlab = "Gap distance (kb)", ylab = "Error rate", ...) ## S4 method for signature 'TranscriptionDataSet' plotErrorRate(object, color = c("#1B9E77", "#D95F02", "#7570B3"), xlab = "Gap distance (kb)", ylab = "Error rate", ...)plotErrorRate(object, color = c("#1B9E77", "#D95F02", "#7570B3"), xlab = "Gap distance (kb)", ylab = "Error rate", ...) ## S4 method for signature 'TranscriptionDataSet' plotErrorRate(object, color = c("#1B9E77", "#D95F02", "#7570B3"), xlab = "Gap distance (kb)", ylab = "Error rate", ...)
object |
A |
color |
|
xlab |
|
ylab |
|
... |
Further arguments passed to plot. |
The tested gap distances are plotted on the x-axis and corresponding
error rates on the y-axis. Three curved lines depict the two error
types calculated by estimateGapDistance and the sum of
both errors. The vertical dashed line depicts the gap distance with
the smallest sum of two errors.
plot
Armen R. Karapetyan
### Load TranscriptionDataSet object data(tds) ### Load reference annotations (knownGene from UCSC) data(annot) ### Estimate gap distance minimazing error rate ### Define the range of gap distances to test gdr <- seq(from = 0, to = 10000, by = 1000) estimateGapDistance(object = tds, annot = annot, coverage.cutoff = 5, filter.annot = FALSE, gap.dist.range = gdr) plotErrorRate(object = tds, lwd = 2)### Load TranscriptionDataSet object data(tds) ### Load reference annotations (knownGene from UCSC) data(annot) ### Estimate gap distance minimazing error rate ### Define the range of gap distances to test gdr <- seq(from = 0, to = 10000, by = 1000) estimateGapDistance(object = tds, annot = annot, coverage.cutoff = 5, filter.annot = FALSE, gap.dist.range = gdr) plotErrorRate(object = tds, lwd = 2)
Visualize the relations between predictors and response variable ('tssOverlap').
plotFeatures(object, plot.type = c("box", "density"), feature, ncol, xlab, ylab, color = c("#E41A1C", "#377EB8"), alpha = 1) ## S4 method for signature 'ChipDataSet' plotFeatures(object, plot.type = c("box", "density"), feature, ncol, xlab, ylab, color = c("#E41A1C", "#377EB8"), alpha = 1)plotFeatures(object, plot.type = c("box", "density"), feature, ncol, xlab, ylab, color = c("#E41A1C", "#377EB8"), alpha = 1) ## S4 method for signature 'ChipDataSet' plotFeatures(object, plot.type = c("box", "density"), feature, ncol, xlab, ylab, color = c("#E41A1C", "#377EB8"), alpha = 1)
object |
A |
plot.type |
One of ["box", "density"]. Default: "box" |
feature |
Feature to plot. By default, all the features are plotted. |
ncol |
|
xlab |
|
ylab |
|
color |
A character vector of length two. Default: ["#E41A1C","#377EB8"]. |
alpha |
Color transparency. In a range [0, 1]. Default: 1. |
In order to discriminate between functional or gene associated peaks and
non-functional or background peaks, each peak in the data set is
characterized by several features. Moreover, the user might supply her/his
own list of features with the addFeature. Prior to fitting
the logistic model, the relations between predictors and response variable
(tssOverlap) can be explored with plotFeatures. Based on the plots,
poor predictors can be excluded from the analysis to improve the model
fit.
ggplot2 object.
Armen R. Karapetyan
### Load ChipDataSet object data(cds) ### The data can be plotted in two ways ### As a boxplot plotFeatures(object = cds, plot.type = "box") ### Or as a density plot plotFeatures(object = cds, plot.type = "density") ### Additionally, only the subset of features can be shown plotFeatures(object = cds, plot.type = "box", feature = c("pileup", "length")) ### The position of the graphs on the plot, can be adjusted by 'ncol' argument plotFeatures(object = cds, plot.type = "box", ncol = 2)### Load ChipDataSet object data(cds) ### The data can be plotted in two ways ### As a boxplot plotFeatures(object = cds, plot.type = "box") ### Or as a density plot plotFeatures(object = cds, plot.type = "density") ### Additionally, only the subset of features can be shown plotFeatures(object = cds, plot.type = "box", feature = c("pileup", "length")) ### The position of the graphs on the plot, can be adjusted by 'ncol' argument plotFeatures(object = cds, plot.type = "box", ncol = 2)
Visualize genomic distribution of ChIP-seq peaks.
plotGenomicAnnot(object, plot.type = c("distrib", "enrich"), xlab, ylab, color) ## S4 method for signature 'ChipDataSet' plotGenomicAnnot(object, plot.type = c("distrib", "enrich"), xlab, ylab, color)plotGenomicAnnot(object, plot.type = c("distrib", "enrich"), xlab, ylab, color) ## S4 method for signature 'ChipDataSet' plotGenomicAnnot(object, plot.type = c("distrib", "enrich"), xlab, ylab, color)
object |
A |
plot.type |
|
xlab |
|
ylab |
|
color |
A character vector of length four, specifying colors for distinct genomic features (TSSs, exons, introns, intergenic regions). |
Genomic distribution of the peaks can be visualized in two ways, either by observing the total number of peaks overlapping given genomic feature or by looking at the enrichment levels.
ggplot2 object.
Armen R. Karapetyan
### Load ChipDataSet object data(cds) ### Plot the total number of peaks overlapping distinct genomic features plotGenomicAnnot(object = cds, plot.type = "distrib") ### Plot enrichment of the peaks at a given genomic feature (e.g. TSS) plotGenomicAnnot(object = cds, plot.type = "enrich")### Load ChipDataSet object data(cds) ### Plot the total number of peaks overlapping distinct genomic features plotGenomicAnnot(object = cds, plot.type = "distrib") ### Plot enrichment of the peaks at a given genomic feature (e.g. TSS) plotGenomicAnnot(object = cds, plot.type = "enrich")
Visualize the performance of the classification model fit (prediction of the gene associated peaks).
plotROC(object, ...) ## S4 method for signature 'ChipDataSet' plotROC(object, ...)plotROC(object, ...) ## S4 method for signature 'ChipDataSet' plotROC(object, ...)
object |
A |
... |
Further arguments passed to plot. |
The plotROC is a simple wrapper for the plot function
implemented in pROC package.
The ROC curve is created by plotting the true positive rate (sensitivity) against the false positive rate (1 - specificity) at various threshold settings. The closer the curve follows the left-hand border and then the top border of the ROC space, the more accurate the test. The area under the curve (AUC) is a measure of accuracy.
ROC plot.
Armen R. Karapetyan
### Load ChipDataSet object data(cds) ### Classify peaks on gene associated and background predictTssOverlap(object = cds, feature = "pileup", p = 0.75) ### Visualize the performance of the model fit plotROC(object = cds, col = "red3", grid = TRUE, auc.polygon = TRUE)### Load ChipDataSet object data(cds) ### Classify peaks on gene associated and background predictTssOverlap(object = cds, feature = "pileup", p = 0.75) ### Visualize the performance of the model fit plotROC(object = cds, col = "red3", grid = TRUE, auc.polygon = TRUE)
The function evaluates transcription initiation within a peak region by comparing RNA-seq read densities upstream and downstream of an empirically determined transcription start sites. Putative transcription of both forward and reverse genomic strands is tested and the results are stored with each ChIP-seq peak.
predictStrand(cdsObj, tdsObj, coverage.cutoff, quant.cutoff = 0.1, win.size = 2500, prob.cutoff) ## S4 method for signature 'ChipDataSet,TranscriptionDataSet' predictStrand(cdsObj, tdsObj, coverage.cutoff, quant.cutoff = 0.1, win.size = 2500, prob.cutoff)predictStrand(cdsObj, tdsObj, coverage.cutoff, quant.cutoff = 0.1, win.size = 2500, prob.cutoff) ## S4 method for signature 'ChipDataSet,TranscriptionDataSet' predictStrand(cdsObj, tdsObj, coverage.cutoff, quant.cutoff = 0.1, win.size = 2500, prob.cutoff)
cdsObj |
A |
tdsObj |
A |
coverage.cutoff |
|
quant.cutoff |
|
win.size |
|
prob.cutoff |
|
RNA-seq data is incorporated to find direct evidence of active transcription from every putatively gene associated peak. In order to do this, we determine the 'strandedness' of the ChIP-seq peaks, using strand specific RNA-seq data. The following assumptions are made in order to retrieve the peak 'strandedness':
The putatively gene associated ChIP-seq peaks are commonly associated with transcription initiation.
This transcription initiation occurs within the ChIP peak region.
When a ChIP peak is associated with a transcription initiation event, we expect to see a strand-specific increase in RNA-seq fragment count downstream the transcription initiation site.
Each peak in the data set is tested for association with transcription initiation on both strands of DNA. Steps 1-5 are performed for both forward and reverse DNA strand separately and step 6 combines the data from both strands. If the peak is identified as associated with the transcription on both strands, than it is considered to be a bidirectional.
ChIP peak 'strandedness' prediction steps:
Identify a location within the ChIP-seq peak near the transcription start site. This is accomplished by calculating the cumulative distribution of RNA-seq fragments within a peak region. The position is determined where 100% - 'quant.cutoff' * 100% of RNA-seq fragments are located downstream. This approach performs well on both gene-poor and gene-dense regions where transcripts may overlap.
Two equally sized regions are defined (q1 and q2), flanking the position identified in (1) on both sides. RNA-seq fragments are counted in each region.
ChIP peaks with an RNA-seq fragment coverage below an estimated threshold are discarded from the analysis.
The probability is calculated for RNA-seq fragments to be sampled from either q1 or q2. Based on the assumptions we stated above, a ChIP peak that is associated with transcription initiation should have more reads in q2 (downstream of the transcription start position) compared to q1, and subsequently, the probability of a fragment being sampled from q2 would be higher.
ChIP-seq peaks are divided into gene associated and background based on the prediction.
Iteratively, the optimal P(q2) threshold is identified, which balances out the False Discovery Rate (FDR) and False Negative Rate (FNR). Peaks with the P(q2) exceeding the estimated threshold are considered to be associated with the transcription initiation event.
The slot strandPrediction of the provided
ChipDataSet object will be updated by the the following
elements: 'predicted.strand', 'probability.cutoff', 'results.plus' and
'results.minus'.
Armen R. Karapetyan
### Load TranscriptionDataSet object data(tds) ### Load ChipDataSet object data(cds) ### Classify peaks on gene associated and background predictTssOverlap(object = cds, feature = "pileup", p = 0.75) ### Predict peak 'strand' predictStrand(cdsObj = cds, tdsObj = tds, coverage.cutoff = 5, quant.cutoff = 0.1, win.size = 2500) ### View a short summary of the 'strand' prediction cds ### View 'strand' prediction getPeaks(cds)### Load TranscriptionDataSet object data(tds) ### Load ChipDataSet object data(cds) ### Classify peaks on gene associated and background predictTssOverlap(object = cds, feature = "pileup", p = 0.75) ### Predict peak 'strand' predictStrand(cdsObj = cds, tdsObj = tds, coverage.cutoff = 5, quant.cutoff = 0.1, win.size = 2500) ### View a short summary of the 'strand' prediction cds ### View 'strand' prediction getPeaks(cds)
The function classifies ChIP-seq peaks on gene associated and background using classification model based on a logistic regression.
predictTssOverlap(object, feature, p = 0.8) ## S4 method for signature 'ChipDataSet' predictTssOverlap(object, feature, p = 0.8)predictTssOverlap(object, feature, p = 0.8) ## S4 method for signature 'ChipDataSet' predictTssOverlap(object, feature, p = 0.8)
object |
A |
feature |
|
p |
|
In order to improve the accuracy of the model the data is internally partitioned into a training and testing data sets. A repeated 10-Fold Cross-Validation is used to calculate performance measures on the training data set and to prevent over-fitting.
The model fit and validation is internally acomplised by the functions implemented in the caret package.
The slot tssOverlapPrediction of the provided
TranscriptionDataSet object will be updated by the the following
elements: 'predicted.tssOverlap', 'confusionMatrix', 'logFitSummary' and
'roc'.
Armen R. Karapetyan
ChipDataSet
constructCDS
caret
### Load ChipDataSet object data(cds) predictTssOverlap(object = cds, feature = "pileup", p = 0.75) ### View a short summary of the gene associated peaks prediction cds ### View peaks and associated prediction getPeaks(cds)### Load ChipDataSet object data(cds) predictTssOverlap(object = cds, feature = "pileup", p = 0.75) ### View a short summary of the gene associated peaks prediction cds ### View peaks and associated prediction getPeaks(cds)
Show method for objects of class ChipDataSet and TranscriptionDataSet
## S4 method for signature 'TranscriptionDataSet' show(object) ## S4 method for signature 'ChipDataSet' show(object)## S4 method for signature 'TranscriptionDataSet' show(object) ## S4 method for signature 'ChipDataSet' show(object)
object |
A |
Displays an overview of the TranscriptionDataSet or ChipDataSet object.
### Load TranscriptionDataSet object data(tds) ### View a short summary of the object tds### Load TranscriptionDataSet object data(tds) ### View a short summary of the object tds
TranscriptionDataSet object.tds is an object of TranscriptionDataSet class, containing
nuclear RNA-seq data for human chromosome 15 (chr15:63261757-84081194),
profiled in prostate cancer LNCaP cells.
tdstds
TranscriptionDataSet object
TranscriptionDataSet object
The TranscriptionDataSet is a S4 class to store input values,
intermediate calculations and results of the transcripts detection and
quantification analysis.
bamFileCharacter. Path to a BAM file.
fragmentsGRanges. Sequencing reads
extended to the fragment size.
fragmentSizeNumeric. Fragment size in base pairs (bp).
regionGRanges. Genomic region(s) to
extract reads from.
coveragePlusRleList. Fragment coverage profile
for the forward DNA strand.
coverageMinusRleList. Fragment coverage profile
for the reverse DNA strand.
coverageCutoffNumeric. Background coverage cutoff value.
coverageCutoffFdrNumeric. False Discovery Rate (FDR) used to
estimate background coverage cutoff.
gapDistanceTestData.Frame. Tested gap distances and
corresponding error rates.
gapDistanceTestCovCutoffNumeric. Coverage cutoff value used for
the gap distance estimation.
transcriptsGRanges. Identified transcripts.
transcriptsCovCutoffNumeric. Coverage cutoff value used for the
transcripts detection.
transcriptsGapDistNumeric. Gap distance value used for the
transcripts detection.
transcriptsNormalizationNumeric. Total number of reads used for
normalization when calculating FPKM.
Armen R. Karapetyan
A convenient graphical way to explore the identified transcripts
is to visualize them in the
UCSC genome browser.
The transcriptsToBed function returns a file in
BED
format, which can be directly uploaded to the genome browser.
To improve the visual perception, transcripts are color-coded by
DNA strand orientation.
transcriptsToBed(object, file, strand.color = c("blue", "red")) ## S4 method for signature 'GRanges' transcriptsToBed(object, file, strand.color = c("blue", "red"))transcriptsToBed(object, file, strand.color = c("blue", "red")) ## S4 method for signature 'GRanges' transcriptsToBed(object, file, strand.color = c("blue", "red"))
object |
A |
file |
|
strand.color |
A character vector of length two, specifying color for each DNA strand. Default: c("blue", "red"). |
A file in the BED format.
Armen R. Karapetyan
estimateBackground
UCSC genome browser
BED
### Load TranscriptionDataSet object data(tds) ### Load reference annotations (knownGene from UCSC) data(annot) ### Detect transcripts detectTranscripts(object = tds, coverage.cutoff = 5, gap.dist = 4000, estimate.params = TRUE, combine.by.annot = FALSE, annot = annot) ### View detected transcripts trx <- getTranscripts(tds) ### Export to BED # transcriptsToBed(object = trx, file = "transcripts.bed", # strand.color = c("blue", "red"))### Load TranscriptionDataSet object data(tds) ### Load reference annotations (knownGene from UCSC) data(annot) ### Detect transcripts detectTranscripts(object = tds, coverage.cutoff = 5, gap.dist = 4000, estimate.params = TRUE, combine.by.annot = FALSE, annot = annot) ### View detected transcripts trx <- getTranscripts(tds) ### Export to BED # transcriptsToBed(object = trx, file = "transcripts.bed", # strand.color = c("blue", "red"))