annotatePeakInBatch: Obtain the distance to the nearest TSS, miRNA, and/or exon...
In jianhong/ChIPpeakAnno: Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments, or any experiments that result in large number of genomic interval data
annotatePeakInBatch R Documentation
Obtain the distance to the nearest TSS, miRNA, and/or exon for a list of
peaks
Description
Obtain the distance to the nearest TSS, miRNA, exon et al for a list of peak
locations leveraging IRanges and biomaRt package
Usage
annotatePeakInBatch(
myPeakList,
mart,
featureType = c("TSS", "miRNA", "Exon"),
AnnotationData,
output = c("nearestLocation", "overlapping", "both", "shortestDistance", "inside",
"upstream&inside", "inside&downstream", "upstream", "downstream",
"upstreamORdownstream", "nearestBiDirectionalPromoters"),
multiple = c(TRUE, FALSE),
maxgap = -1L,
PeakLocForDistance = c("start", "middle", "end", "endMinusStart"),
FeatureLocForDistance = c("TSS", "middle", "start", "end", "geneEnd"),
select = c("all", "first", "last", "arbitrary"),
ignore.strand = TRUE,
bindingRegion = NULL,
...
)
Arguments
myPeakList
A GRanges object
mart
A mart object, used if AnnotationData is not supplied, see
useMart of bioMaRt package for details
featureType
A charcter vector used with mart argument if
AnnotationData is not supplied; choose from "TSS", "miRNA" or "Exon"
AnnotationData
A GRanges or
annoGR object. It can be obtained from the function getAnnotation or
customized annotation of class GRanges containing additional variable:
strand (1 or + for plus strand and -1 or - for minus strand). Pre-compliled
annotations, such as TSS.human.NCBI36, TSS.mouse.NCBIM37, TSS.rat.RGSC3.4
and TSS.zebrafish.Zv8, are provided by this package (attach them with data()
function). Another method to provide annotation data is to obtain through
biomaRt in real time by using the mart and featureType option
output
- nearestLocation (default)
will output the nearest features calculated
as PeakLoc - FeatureLocForDistance; when selected, the output can consist of
both "strictly nearest features (non-overlapping)" and "overlapping features"
as long as they are the nearest
- overlapping
will output overlapping features with maximum gap
specified as maxgap between peak range and feature range; it is possible for
a peak to be annotated with zero ("NA" will be returned) or multiple
overlapping features if exist
- both
will output all the nearest features as well as any features
that overlap with the peak that is not the nearest
- shortestDistance
will output the features with the shortest distance;
the "shortest distance" is determined from either ends of the feature to
either ends of the peak
- upstream&inside
will output all upstream and overlapping features with
maximum gap
- inside&downstream
will output all downstream and overlapping features
with maximum gap
- upstream
will output all upstream features with maximum gap
- downstream
will output all downstream features with maximum gap
- upstreamORdownstream
will output all upstream features with maximum
gap or downstream with maximum gap
- nearestBiDirectionalPromoters
will use annoPeaks to
annotate peaks. Nearest promoters from both direction of the peaks (strand
is considered). It will report bidirectional promoters if there are
promoters in both directions in the given region (defined by bindingRegion).
Otherwise, it will report the closest promoter in one direction.
multiple
Not applicable when output is nearest. TRUE: output multiple
overlapping features for each peak. FALSE: output at most one overlapping
feature for each peak. This parameter is kept for backward compatibility,
please use select.
maxgap
The maximum gap that is allowed between 2 ranges for
the ranges to be considered as overlapping. The gap between 2 ranges
is the number of positions that separate them. The gap between 2
adjacent ranges is 0. By convention when one range has its start or end
strictly inside the other (i.e. non-disjoint ranges), the gap is
considered to be -1.
PeakLocForDistance
Specify the location of peak for calculating
distance,i.e., middle means using middle of the peak to calculate distance
to feature, start means using start of the peak to calculate the distance to
feature, endMinusStart means using the end of the peak to calculate the
distance to features on plus strand and the start of the peak to calculate
the distance to features on minus strand. To be compatible with previous
version, by default using start
FeatureLocForDistance
Specify the location of feature for calculating
distance,i.e., middle means using middle of the feature to calculate
distance of peak to feature, start means using start of the feature to
calculate the distance to feature, TSS means using start of feature when
feature is on plus strand and using end of feature when feature is on minus
strand, geneEnd means using end of feature when feature is on plus strand
and using start of feature when feature is on minus strand. To be compatible
with previous version, by default using TSS
select
"all" may return multiple overlapping peaks, "first" will
return the first overlapping peak, "last" will return the last overlapping
peak and "arbitrary" will return one of the overlapping peaks.
ignore.strand
When set to TRUE, the strand information is ignored in
the annotation. Unless you have stranded peaks and you are interested in
annotating peaks to the features in the same strand only, you should just
use the default setting ignore.strand = TRUE.
bindingRegion
Annotation range used for annoPeaks, which is a
vector with two integer values, default to c (-5000, 5000). The first one
must be no bigger than 0. And the sec ond one must be no less than 1. Once
bindingRegion is defined, annotation will based on annoPeaks. Here is
how to use it together with the parameter output and FeatureLocForDistance.
To obtain peaks with nearest bi-directional promoters within
5kb upstream and 3kb downstream of TSS, set output =
"nearestBiDirectionalPromoters" and bindingRegion = c(-5000, 3000)
To
obtain peaks within 5kb upstream and up to 3kb downstream of TSS within the
gene body, set output="overlapping", FeatureLocForDistance="TSS" and
bindingRegion = c(-5000, 3000)
To obtain peaks up to 5kb upstream
within the gene body and 3kb downstream of gene/Exon End, set
output="overlapping", FeatureLocForDistance="geneEnd" and bindingRegion =
c(-5000, 3000)
To obtain peaks from 5kb upstream to 3kb downstream of
genes/Exons, set output="overlapping", bindingType = "fullRange" and
bindingRegion = c(-5000, 3000)
For details, see annoPeaks.
...
Parameters could be passed to annoPeaks
Value
An object of GRanges with slot
start holding the start position of the peak, slot end holding the end
position of the peak, slot space holding the chromosome location where the
peak is located, slot rownames holding the id of the peak. In addition, the
following variables are included.
list("feature")
id of the feature
such as ensembl gene ID
list("insideFeature")
upstream: peak resides
upstream of the feature; downstream: peak resides downstream of the feature;
inside: peak resides inside the feature; overlapStart: peak overlaps with
the start of the feature; overlapEnd: peak overlaps with the end of the
feature; includeFeature: peak include the feature entirely
list("distancetoFeature")
distance to the nearest feature such as
transcription start site. By default, the distance is calculated as the
distance between the start of the binding site and the TSS that is the gene
start for genes located on the forward strand and the gene end for genes
located on the reverse strand. The user can specify the location of peak and
location of feature for calculating this
list("start_position")
start position of the feature such as gene
list("end_position")
end position of the feature such as the gene
list("strand")
1 or + for positive strand and -1 or - for negative
strand where the feature is located
list("shortestDistance")
The
shortest distance from either end of peak to either end the feature.
list("fromOverlappingOrNearest")
Relevant only when output is set to
"both". If "nearestLocation": indicates this feature's start (feature's end
for features from minus strand) is the closest to the peak start ("strictly
nearest" or "nearest overlapping"); if "Overlapping": indicates this feature
overlaps with this peak although it is not the nearest (non-nearest
overlapping)
Author(s)
Lihua Julie Zhu, Jianhong Ou
References
1. Zhu L.J. et al. (2010) ChIPpeakAnno: a Bioconductor package
to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 2010,
11:237doi:10.1186/1471-2105-11-237
2. Zhu L (2013). "Integrative analysis of ChIP-chip and ChIP-seq dataset."
In Lee T and Luk ACS (eds.), Tilling Arrays, volume 1067, chapter 4, pp.
-19. Humana Press. http://dx.doi.org/10.1007/978-1-62703-607-8_8
See Also
getAnnotation, findOverlappingPeaks,
makeVennDiagram, addGeneIDs, peaksNearBDP,
summarizePatternInPeaks, annoGR, annoPeaks
Examples
## example 1: annotate myPeakList by TxDb or EnsDb.
data(myPeakList)
library(ensembldb)
library(EnsDb.Hsapiens.v75)
annoData <- annoGR(EnsDb.Hsapiens.v75)
annotatePeak = annotatePeakInBatch(myPeakList[1:6], AnnotationData=annoData)
annotatePeak
## example 2: annotate myPeakList (GRanges)
## with TSS.human.NCBI36 (Granges)
data(TSS.human.NCBI36)
annotatedPeak = annotatePeakInBatch(myPeakList[1:6],
AnnotationData=TSS.human.NCBI36)
annotatedPeak
## example 3: you have a list of transcription factor biding sites from
## literature and are interested in determining the extent of the overlap
## to the list of peaks from your experiment. Prior calling the function
## annotatePeakInBatch, need to represent both dataset as GRanges
## where start is the start of the binding site, end is the end of the
## binding site, names is the name of the binding site, space and strand
## are the chromosome name and strand where the binding site is located.
myexp <- GRanges(seqnames=c(6,6,6,6,5,4,4),
IRanges(start=c(1543200,1557200,1563000,1569800,
167889600,100,1000),
end=c(1555199,1560599,1565199,1573799,
167893599,200,1200),
names=c("p1","p2","p3","p4","p5","p6", "p7")),
strand="+")
literature <- GRanges(seqnames=c(6,6,6,6,5,4,4),
IRanges(start=c(1549800,1554400,1565000,1569400,
167888600,120,800),
end=c(1550599,1560799,1565399,1571199,
167888999,140,1400),
names=c("f1","f2","f3","f4","f5","f6","f7")),
strand=rep(c("+", "-"), c(5, 2)))
annotatedPeak1 <- annotatePeakInBatch(myexp,
AnnotationData=literature)
pie(table(annotatedPeak1$insideFeature))
annotatedPeak1
### use toGRanges or rtracklayer::import to convert BED or GFF format
### to GRanges before calling annotatePeakInBatch
test.bed <- data.frame(space=c("4", "6"),
start=c("100", "1000"),
end=c("200", "1100"),
name=c("peak1", "peak2"))
test.GR = toGRanges(test.bed)
annotatePeakInBatch(test.GR, AnnotationData = literature)
library(testthat)
peak <- GRanges(seqnames = "chr1",
IRanges(start = 24736757, end=24737528,
names = "testPeak"))
data(TSS.human.GRCh37)
TSS.human.GRCh37[names(TSS.human.GRCh37)== "ENSG00000001461"]
# GRanges object with 1 range and 1 metadata column:
# seqnames ranges strand | description
#<Rle> <IRanges> <Rle> | <character>
# ENSG00000001461 1 24742285-24799466 + | NIPA-like domain con..
peak
#GRanges object with 1 range and 0 metadata columns:
# seqnames ranges strand
#<Rle> <IRanges> <Rle>
# testPeak chr1 24736757-24737528 *
TSS.human.GRCh37[names(TSS.human.GRCh37)== "ENSG00000001460"]
#GRanges object with 1 range and 1 metadata column:
# seqnames ranges strand | description
#<Rle> <IRanges> <Rle> | <character>
# ENSG00000001460 1 24683490-24743424 - | UPF0490 protein C1or..
ap <- annotatePeakInBatch(peak, Annotation=TSS.human.GRCh37,
PeakLocForDistance = "start")
stopifnot(ap$feature=="ENSG00000001461")
ap <- annotatePeakInBatch(peak, Annotation=TSS.human.GRCh37,
PeakLocForDistance = "end")
stopifnot(ap$feature=="ENSG00000001461")
ap <- annotatePeakInBatch(peak, Annotation=TSS.human.GRCh37,
PeakLocForDistance = "middle")
stopifnot(ap$feature=="ENSG00000001461")
ap <- annotatePeakInBatch(peak, Annotation=TSS.human.GRCh37,
PeakLocForDistance = "endMinusStart")
stopifnot(ap$feature=="ENSG00000001461")
## Let's calculate the distances between the peak and the TSS of the genes
## in the annotation file used for annotating the peaks.
## Please note that we need to compute the distance using the annotation
## file TSS.human.GRCh37.
## If you would like to use TxDb.Hsapiens.UCSC.hg19.knownGene,
## then you will need to annotate the peaks
## using TxDb.Hsapiens.UCSC.hg19.knownGene as well.
### using start
start(peak) -start(TSS.human.GRCh37[names(TSS.human.GRCh37)==
"ENSG00000001461"]) #picked
#[1] -5528
start(peak) -end(TSS.human.GRCh37[names(TSS.human.GRCh37)==
"ENSG00000001460"])
#[1] -6667
#### using middle
(start(peak) + end(peak))/2 -
start(TSS.human.GRCh37[names(TSS.human.GRCh37)== "ENSG00000001461"])
#[1] -5142.5
(start(peak) + end(peak))/2 -
end(TSS.human.GRCh37[names(TSS.human.GRCh37)== "ENSG00000001460"])
# [1] 49480566
end(peak) -start(TSS.human.GRCh37[names(TSS.human.GRCh37)==
"ENSG00000001461"]) #picked
# [1] -4757
end(peak) -end(TSS.human.GRCh37[names(TSS.human.GRCh37)==
"ENSG00000001460"])
# [1] -5896
#### using endMinusStart
end(peak) - start(TSS.human.GRCh37[names(TSS.human.GRCh37)==
"ENSG00000001461"]) ## picked
# [1] -4575
start(peak) -end(TSS.human.GRCh37[names(TSS.human.GRCh37)==
"ENSG00000001460"])
#[1] -6667
###### using txdb object to annotate the peaks
library(org.Hs.eg.db)
select(org.Hs.eg.db, key="STPG1", keytype="SYMBOL",
columns=c("ENSEMBL", "ENTREZID", "SYMBOL"))
# SYMBOL ENSEMBL ENTREZID
# STPG1 ENSG00000001460 90529
select(org.Hs.eg.db, key= "ENSG00000001461", keytype="ENSEMBL",
columns=c("ENSEMBL", "ENTREZID", "SYMBOL"))
#ENSEMBL ENTREZID SYMBOL
# ENSG00000001461 57185 NIPAL3
require(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb.ann <- genes(TxDb.Hsapiens.UCSC.hg19.knownGene)
STPG1 <- select(org.Hs.eg.db, key="STPG1", keytype="SYMBOL",
columns=c( "SYMBOL", "ENSEMBL", "ENTREZID"))[1,3]
NIPAL3 <- select(org.Hs.eg.db, key="NIPAL3", keytype="SYMBOL",
columns=c( "SYMBOL", "ENSEMBL", "ENTREZID"))[1,3]
ap <- annotatePeakInBatch(peak, Annotation=txdb.ann,
PeakLocForDistance = "start")
expect_equal(ap$feature, STPG1)
ap <- annotatePeakInBatch(peak, Annotation=txdb.ann,
PeakLocForDistance = "end")
expect_equal(ap$feature, STPG1)
ap <- annotatePeakInBatch(peak, Annotation=txdb.ann,
PeakLocForDistance = "middle")
expect_equal(ap$feature, STPG1)
ap <- annotatePeakInBatch(peak, Annotation=txdb.ann,
PeakLocForDistance = "endMinusStart")
expect_equal(ap$feature, NIPAL3)
txdb.ann[NIPAL3]
txdb.ann[txdb.ann$gene_id == NIPAL3]
# GRanges object with 1 range and 1 metadata column:
# seqnames ranges strand | gene_id
# <Rle> <IRanges> <Rle> | <character>
# 57185 chr1 24742245-24799473 + | 57185
#-------
txdb.ann[txdb.ann$gene_id == STPG1]
# GRanges object with 1 range and 1 metadata column:
# seqnames ranges strand | gene_id
# <Rle> <IRanges> <Rle> | <character>
# 90529 chr1 24683489-24741587 - | 90529
jianhong/ChIPpeakAnno documentation built on Nov. 1, 2024, 8:55 a.m.
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| annotatePeakInBatch | R Documentation |
Obtain the distance to the nearest TSS, miRNA, and/or exon for a list of peaks
Description
Obtain the distance to the nearest TSS, miRNA, exon et al for a list of peak locations leveraging IRanges and biomaRt package
Usage
annotatePeakInBatch(
myPeakList,
mart,
featureType = c("TSS", "miRNA", "Exon"),
AnnotationData,
output = c("nearestLocation", "overlapping", "both", "shortestDistance", "inside",
"upstream&inside", "inside&downstream", "upstream", "downstream",
"upstreamORdownstream", "nearestBiDirectionalPromoters"),
multiple = c(TRUE, FALSE),
maxgap = -1L,
PeakLocForDistance = c("start", "middle", "end", "endMinusStart"),
FeatureLocForDistance = c("TSS", "middle", "start", "end", "geneEnd"),
select = c("all", "first", "last", "arbitrary"),
ignore.strand = TRUE,
bindingRegion = NULL,
...
)
Arguments
myPeakList |
A GRanges object |
mart |
A mart object, used if AnnotationData is not supplied, see useMart of bioMaRt package for details |
featureType |
A charcter vector used with mart argument if AnnotationData is not supplied; choose from "TSS", "miRNA" or "Exon" |
AnnotationData |
A GRanges or annoGR object. It can be obtained from the function getAnnotation or customized annotation of class GRanges containing additional variable: strand (1 or + for plus strand and -1 or - for minus strand). Pre-compliled annotations, such as TSS.human.NCBI36, TSS.mouse.NCBIM37, TSS.rat.RGSC3.4 and TSS.zebrafish.Zv8, are provided by this package (attach them with data() function). Another method to provide annotation data is to obtain through biomaRt in real time by using the mart and featureType option |
output |
|
multiple |
Not applicable when output is nearest. TRUE: output multiple overlapping features for each peak. FALSE: output at most one overlapping feature for each peak. This parameter is kept for backward compatibility, please use select. |
maxgap |
The maximum gap that is allowed between 2 ranges for the ranges to be considered as overlapping. The gap between 2 ranges is the number of positions that separate them. The gap between 2 adjacent ranges is 0. By convention when one range has its start or end strictly inside the other (i.e. non-disjoint ranges), the gap is considered to be -1. |
PeakLocForDistance |
Specify the location of peak for calculating distance,i.e., middle means using middle of the peak to calculate distance to feature, start means using start of the peak to calculate the distance to feature, endMinusStart means using the end of the peak to calculate the distance to features on plus strand and the start of the peak to calculate the distance to features on minus strand. To be compatible with previous version, by default using start |
FeatureLocForDistance |
Specify the location of feature for calculating distance,i.e., middle means using middle of the feature to calculate distance of peak to feature, start means using start of the feature to calculate the distance to feature, TSS means using start of feature when feature is on plus strand and using end of feature when feature is on minus strand, geneEnd means using end of feature when feature is on plus strand and using start of feature when feature is on minus strand. To be compatible with previous version, by default using TSS |
select |
"all" may return multiple overlapping peaks, "first" will return the first overlapping peak, "last" will return the last overlapping peak and "arbitrary" will return one of the overlapping peaks. |
ignore.strand |
When set to TRUE, the strand information is ignored in the annotation. Unless you have stranded peaks and you are interested in annotating peaks to the features in the same strand only, you should just use the default setting ignore.strand = TRUE. |
bindingRegion |
Annotation range used for annoPeaks, which is a vector with two integer values, default to c (-5000, 5000). The first one must be no bigger than 0. And the sec ond one must be no less than 1. Once bindingRegion is defined, annotation will based on annoPeaks. Here is how to use it together with the parameter output and FeatureLocForDistance.
For details, see annoPeaks. |
... |
Parameters could be passed to annoPeaks |
Value
An object of GRanges with slot start holding the start position of the peak, slot end holding the end position of the peak, slot space holding the chromosome location where the peak is located, slot rownames holding the id of the peak. In addition, the following variables are included.
list("feature") |
id of the feature such as ensembl gene ID |
list("insideFeature") |
upstream: peak resides upstream of the feature; downstream: peak resides downstream of the feature; inside: peak resides inside the feature; overlapStart: peak overlaps with the start of the feature; overlapEnd: peak overlaps with the end of the feature; includeFeature: peak include the feature entirely |
list("distancetoFeature") |
distance to the nearest feature such as transcription start site. By default, the distance is calculated as the distance between the start of the binding site and the TSS that is the gene start for genes located on the forward strand and the gene end for genes located on the reverse strand. The user can specify the location of peak and location of feature for calculating this |
list("start_position") |
start position of the feature such as gene |
list("end_position") |
end position of the feature such as the gene |
list("strand") |
1 or + for positive strand and -1 or - for negative strand where the feature is located |
list("shortestDistance") |
The shortest distance from either end of peak to either end the feature. |
list("fromOverlappingOrNearest") |
Relevant only when output is set to "both". If "nearestLocation": indicates this feature's start (feature's end for features from minus strand) is the closest to the peak start ("strictly nearest" or "nearest overlapping"); if "Overlapping": indicates this feature overlaps with this peak although it is not the nearest (non-nearest overlapping) |
Author(s)
Lihua Julie Zhu, Jianhong Ou
References
1. Zhu L.J. et al. (2010) ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 2010, 11:237doi:10.1186/1471-2105-11-237
2. Zhu L (2013). "Integrative analysis of ChIP-chip and ChIP-seq dataset." In Lee T and Luk ACS (eds.), Tilling Arrays, volume 1067, chapter 4, pp. -19. Humana Press. http://dx.doi.org/10.1007/978-1-62703-607-8_8
See Also
getAnnotation, findOverlappingPeaks, makeVennDiagram, addGeneIDs, peaksNearBDP, summarizePatternInPeaks, annoGR, annoPeaks
Examples
## example 1: annotate myPeakList by TxDb or EnsDb.
data(myPeakList)
library(ensembldb)
library(EnsDb.Hsapiens.v75)
annoData <- annoGR(EnsDb.Hsapiens.v75)
annotatePeak = annotatePeakInBatch(myPeakList[1:6], AnnotationData=annoData)
annotatePeak
## example 2: annotate myPeakList (GRanges)
## with TSS.human.NCBI36 (Granges)
data(TSS.human.NCBI36)
annotatedPeak = annotatePeakInBatch(myPeakList[1:6],
AnnotationData=TSS.human.NCBI36)
annotatedPeak
## example 3: you have a list of transcription factor biding sites from
## literature and are interested in determining the extent of the overlap
## to the list of peaks from your experiment. Prior calling the function
## annotatePeakInBatch, need to represent both dataset as GRanges
## where start is the start of the binding site, end is the end of the
## binding site, names is the name of the binding site, space and strand
## are the chromosome name and strand where the binding site is located.
myexp <- GRanges(seqnames=c(6,6,6,6,5,4,4),
IRanges(start=c(1543200,1557200,1563000,1569800,
167889600,100,1000),
end=c(1555199,1560599,1565199,1573799,
167893599,200,1200),
names=c("p1","p2","p3","p4","p5","p6", "p7")),
strand="+")
literature <- GRanges(seqnames=c(6,6,6,6,5,4,4),
IRanges(start=c(1549800,1554400,1565000,1569400,
167888600,120,800),
end=c(1550599,1560799,1565399,1571199,
167888999,140,1400),
names=c("f1","f2","f3","f4","f5","f6","f7")),
strand=rep(c("+", "-"), c(5, 2)))
annotatedPeak1 <- annotatePeakInBatch(myexp,
AnnotationData=literature)
pie(table(annotatedPeak1$insideFeature))
annotatedPeak1
### use toGRanges or rtracklayer::import to convert BED or GFF format
### to GRanges before calling annotatePeakInBatch
test.bed <- data.frame(space=c("4", "6"),
start=c("100", "1000"),
end=c("200", "1100"),
name=c("peak1", "peak2"))
test.GR = toGRanges(test.bed)
annotatePeakInBatch(test.GR, AnnotationData = literature)
library(testthat)
peak <- GRanges(seqnames = "chr1",
IRanges(start = 24736757, end=24737528,
names = "testPeak"))
data(TSS.human.GRCh37)
TSS.human.GRCh37[names(TSS.human.GRCh37)== "ENSG00000001461"]
# GRanges object with 1 range and 1 metadata column:
# seqnames ranges strand | description
#<Rle> <IRanges> <Rle> | <character>
# ENSG00000001461 1 24742285-24799466 + | NIPA-like domain con..
peak
#GRanges object with 1 range and 0 metadata columns:
# seqnames ranges strand
#<Rle> <IRanges> <Rle>
# testPeak chr1 24736757-24737528 *
TSS.human.GRCh37[names(TSS.human.GRCh37)== "ENSG00000001460"]
#GRanges object with 1 range and 1 metadata column:
# seqnames ranges strand | description
#<Rle> <IRanges> <Rle> | <character>
# ENSG00000001460 1 24683490-24743424 - | UPF0490 protein C1or..
ap <- annotatePeakInBatch(peak, Annotation=TSS.human.GRCh37,
PeakLocForDistance = "start")
stopifnot(ap$feature=="ENSG00000001461")
ap <- annotatePeakInBatch(peak, Annotation=TSS.human.GRCh37,
PeakLocForDistance = "end")
stopifnot(ap$feature=="ENSG00000001461")
ap <- annotatePeakInBatch(peak, Annotation=TSS.human.GRCh37,
PeakLocForDistance = "middle")
stopifnot(ap$feature=="ENSG00000001461")
ap <- annotatePeakInBatch(peak, Annotation=TSS.human.GRCh37,
PeakLocForDistance = "endMinusStart")
stopifnot(ap$feature=="ENSG00000001461")
## Let's calculate the distances between the peak and the TSS of the genes
## in the annotation file used for annotating the peaks.
## Please note that we need to compute the distance using the annotation
## file TSS.human.GRCh37.
## If you would like to use TxDb.Hsapiens.UCSC.hg19.knownGene,
## then you will need to annotate the peaks
## using TxDb.Hsapiens.UCSC.hg19.knownGene as well.
### using start
start(peak) -start(TSS.human.GRCh37[names(TSS.human.GRCh37)==
"ENSG00000001461"]) #picked
#[1] -5528
start(peak) -end(TSS.human.GRCh37[names(TSS.human.GRCh37)==
"ENSG00000001460"])
#[1] -6667
#### using middle
(start(peak) + end(peak))/2 -
start(TSS.human.GRCh37[names(TSS.human.GRCh37)== "ENSG00000001461"])
#[1] -5142.5
(start(peak) + end(peak))/2 -
end(TSS.human.GRCh37[names(TSS.human.GRCh37)== "ENSG00000001460"])
# [1] 49480566
end(peak) -start(TSS.human.GRCh37[names(TSS.human.GRCh37)==
"ENSG00000001461"]) #picked
# [1] -4757
end(peak) -end(TSS.human.GRCh37[names(TSS.human.GRCh37)==
"ENSG00000001460"])
# [1] -5896
#### using endMinusStart
end(peak) - start(TSS.human.GRCh37[names(TSS.human.GRCh37)==
"ENSG00000001461"]) ## picked
# [1] -4575
start(peak) -end(TSS.human.GRCh37[names(TSS.human.GRCh37)==
"ENSG00000001460"])
#[1] -6667
###### using txdb object to annotate the peaks
library(org.Hs.eg.db)
select(org.Hs.eg.db, key="STPG1", keytype="SYMBOL",
columns=c("ENSEMBL", "ENTREZID", "SYMBOL"))
# SYMBOL ENSEMBL ENTREZID
# STPG1 ENSG00000001460 90529
select(org.Hs.eg.db, key= "ENSG00000001461", keytype="ENSEMBL",
columns=c("ENSEMBL", "ENTREZID", "SYMBOL"))
#ENSEMBL ENTREZID SYMBOL
# ENSG00000001461 57185 NIPAL3
require(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb.ann <- genes(TxDb.Hsapiens.UCSC.hg19.knownGene)
STPG1 <- select(org.Hs.eg.db, key="STPG1", keytype="SYMBOL",
columns=c( "SYMBOL", "ENSEMBL", "ENTREZID"))[1,3]
NIPAL3 <- select(org.Hs.eg.db, key="NIPAL3", keytype="SYMBOL",
columns=c( "SYMBOL", "ENSEMBL", "ENTREZID"))[1,3]
ap <- annotatePeakInBatch(peak, Annotation=txdb.ann,
PeakLocForDistance = "start")
expect_equal(ap$feature, STPG1)
ap <- annotatePeakInBatch(peak, Annotation=txdb.ann,
PeakLocForDistance = "end")
expect_equal(ap$feature, STPG1)
ap <- annotatePeakInBatch(peak, Annotation=txdb.ann,
PeakLocForDistance = "middle")
expect_equal(ap$feature, STPG1)
ap <- annotatePeakInBatch(peak, Annotation=txdb.ann,
PeakLocForDistance = "endMinusStart")
expect_equal(ap$feature, NIPAL3)
txdb.ann[NIPAL3]
txdb.ann[txdb.ann$gene_id == NIPAL3]
# GRanges object with 1 range and 1 metadata column:
# seqnames ranges strand | gene_id
# <Rle> <IRanges> <Rle> | <character>
# 57185 chr1 24742245-24799473 + | 57185
#-------
txdb.ann[txdb.ann$gene_id == STPG1]
# GRanges object with 1 range and 1 metadata column:
# seqnames ranges strand | gene_id
# <Rle> <IRanges> <Rle> | <character>
# 90529 chr1 24683489-24741587 - | 90529
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