,
Ming Li [ctb],
Qianwen Wang [ctb],
Yun Yan [ctb],
Hervé Pagès [ctb],
Michael Kluge [ctb],
Thomas Schwarzl [ctb],
Zhougeng Xu [ctb],
Chun-Hui Gao [ctb] | Title: | ChIPseeker for ChIP peak Annotation, Comparison, and Visualization |
|---|---|
| Description: | This package implements functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statstical methods for estimate the significance of overlap among ChIP peak data sets, and incorporate GEO database for user to compare the own dataset with those deposited in database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, and overlap of peaks or genes. |
| Authors: | Guangchuang Yu [aut, cre] ,
Ming Li [ctb],
Qianwen Wang [ctb],
Yun Yan [ctb],
Hervé Pagès [ctb],
Michael Kluge [ctb],
Thomas Schwarzl [ctb],
Zhougeng Xu [ctb],
Chun-Hui Gao [ctb] |
| Maintainer: | Guangchuang Yu <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.43.0 |
| Built: | 2024-11-29 05:01:11 UTC |
| Source: | https://github.com/bioc/ChIPseeker |
capture name of variable
.(..., .env = parent.frame()).(..., .env = parent.frame())
... |
expression |
.env |
environment |
expression
x <- 1 eval(.(x)[[1]])x <- 1 eval(.(x)[[1]])
Annotate peaks
annotatePeak( peak, tssRegion = c(-3000, 3000), TxDb = NULL, level = "transcript", assignGenomicAnnotation = TRUE, genomicAnnotationPriority = c("Promoter", "5UTR", "3UTR", "Exon", "Intron", "Downstream", "Intergenic"), annoDb = NULL, addFlankGeneInfo = FALSE, flankDistance = 5000, sameStrand = FALSE, ignoreOverlap = FALSE, ignoreUpstream = FALSE, ignoreDownstream = FALSE, overlap = "TSS", verbose = TRUE, columns = c("ENTREZID", "ENSEMBL", "SYMBOL", "GENENAME") )annotatePeak( peak, tssRegion = c(-3000, 3000), TxDb = NULL, level = "transcript", assignGenomicAnnotation = TRUE, genomicAnnotationPriority = c("Promoter", "5UTR", "3UTR", "Exon", "Intron", "Downstream", "Intergenic"), annoDb = NULL, addFlankGeneInfo = FALSE, flankDistance = 5000, sameStrand = FALSE, ignoreOverlap = FALSE, ignoreUpstream = FALSE, ignoreDownstream = FALSE, overlap = "TSS", verbose = TRUE, columns = c("ENTREZID", "ENSEMBL", "SYMBOL", "GENENAME") )
peak |
peak file or GRanges object |
tssRegion |
Region Range of TSS |
TxDb |
TxDb or EnsDb annotation object |
level |
one of transcript and gene |
assignGenomicAnnotation |
logical, assign peak genomic annotation or not |
genomicAnnotationPriority |
genomic annotation priority |
annoDb |
annotation package |
addFlankGeneInfo |
logical, add flanking gene information from the peaks |
flankDistance |
distance of flanking sequence |
sameStrand |
logical, whether find nearest/overlap gene in the same strand |
ignoreOverlap |
logical, whether ignore overlap of TSS with peak |
ignoreUpstream |
logical, if True only annotate gene at the 3' of the peak. |
ignoreDownstream |
logical, if True only annotate gene at the 5' of the peak. |
overlap |
one of 'TSS' or 'all', if overlap="all", then gene overlap with peak will be reported as nearest gene, no matter the overlap is at TSS region or not. |
verbose |
print message or not |
columns |
names of columns to be obtained from database |
data.frame or GRanges object with columns of:
all columns provided by input.
annotation: genomic feature of the peak, for instance if the peak is located in 5'UTR, it will annotated by 5'UTR. Possible annotation is Promoter-TSS, Exon, 5' UTR, 3' UTR, Intron, and Intergenic.
geneChr: Chromosome of the nearest gene
geneStart: gene start
geneEnd: gene end
geneLength: gene length
geneStrand: gene strand
geneId: entrezgene ID
distanceToTSS: distance from peak to gene TSS
if annoDb is provided, extra column will be included:
ENSEMBL: ensembl ID of the nearest gene
SYMBOL: gene symbol
GENENAME: full gene name
G Yu
plotAnnoBar plotAnnoPie plotDistToTSS
## Not run: require(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene peakfile <- system.file("extdata", "sample_peaks.txt", package="ChIPseeker") peakAnno <- annotatePeak(peakfile, tssRegion=c(-3000, 3000), TxDb=txdb) peakAnno ## End(Not run)## Not run: require(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene peakfile <- system.file("extdata", "sample_peaks.txt", package="ChIPseeker") peakAnno <- annotatePeak(peakfile, tssRegion=c(-3000, 3000), TxDb=txdb) peakAnno ## End(Not run)
convert csAnno object to data.frame
## S3 method for class 'csAnno' as.data.frame(x, row.names = NULL, optional = FALSE, ...)## S3 method for class 'csAnno' as.data.frame(x, row.names = NULL, optional = FALSE, ...)
x |
csAnno object |
row.names |
row names |
optional |
should be omitted. |
... |
additional parameters |
data.frame
Guangchuang Yu https://guangchuangyu.github.io
convert csAnno object to GRanges
as.GRanges(x)as.GRanges(x)
x |
csAnno object |
GRanges object
Guangchuang Yu https://guangchuangyu.github.io
check_upstream_and_downstream
check_upstream_and_downstream(upstream, downstream)check_upstream_and_downstream(upstream, downstream)
upstream |
upstream |
downstream |
downstream |
Combine csAnno Object
combine_csAnno(x, ...)combine_csAnno(x, ...)
x |
csAnno object |
... |
csAnno objects |
https://github.com/YuLab-SMU/ChIPseeker/issues/157
csAnno object
plot peak coverage
covplot( peak, weightCol = NULL, xlab = "Chromosome Size (bp)", ylab = "", title = "ChIP Peaks over Chromosomes", chrs = NULL, xlim = NULL, lower = 1, fill_color = "black" )covplot( peak, weightCol = NULL, xlab = "Chromosome Size (bp)", ylab = "", title = "ChIP Peaks over Chromosomes", chrs = NULL, xlim = NULL, lower = 1, fill_color = "black" )
peak |
peak file or GRanges object |
weightCol |
weight column of peak |
xlab |
xlab |
ylab |
ylab |
title |
title |
chrs |
selected chromosomes to plot, all chromosomes by default |
xlim |
ranges to plot, default is whole chromosome |
lower |
lower cutoff of coverage signal |
fill_color |
specify the color/palette for the plot. Order matters |
ggplot2 object
G Yu
Class "csAnno" This class represents the output of ChIPseeker Annotation
annoannotation
tssRegionTSS region
leveltranscript or gene
hasGenomicAnnotationlogical
detailGenomicAnnotationGenomic Annotation in detail
annoStatannotation statistics
peakNumnumber of peaks
Guangchuang Yu https://guangchuangyu.github.io
download all BED files of a particular genome version
downloadGEObedFiles(genome, destDir = getwd())downloadGEObedFiles(genome, destDir = getwd())
genome |
genome version |
destDir |
destination folder |
G Yu
download BED supplementary files of a list of GSM accession numbers
downloadGSMbedFiles(GSM, destDir = getwd())downloadGSMbedFiles(GSM, destDir = getwd())
GSM |
GSM accession numbers |
destDir |
destination folder |
G Yu
dropAnno
dropAnno(csAnno, distanceToTSS_cutoff = 10000)dropAnno(csAnno, distanceToTSS_cutoff = 10000)
csAnno |
output of annotatePeak |
distanceToTSS_cutoff |
distance to TSS cutoff |
drop annotation exceeding distanceToTSS_cutoff
csAnno object
Guangchuang Yu
calcuate overlap significant of ChIP experiments based on their nearest gene annotation
enrichAnnoOverlap( queryPeak, targetPeak, TxDb = NULL, pAdjustMethod = "BH", chainFile = NULL, distanceToTSS_cutoff = NULL )enrichAnnoOverlap( queryPeak, targetPeak, TxDb = NULL, pAdjustMethod = "BH", chainFile = NULL, distanceToTSS_cutoff = NULL )
queryPeak |
query bed file |
targetPeak |
target bed file(s) or folder containing bed files |
TxDb |
TxDb |
pAdjustMethod |
pvalue adjustment method |
chainFile |
chain file for liftOver |
distanceToTSS_cutoff |
restrict nearest gene annotation by distance cutoff |
data.frame
G Yu
calculate overlap significant of ChIP experiments based on the genome coordinations
enrichPeakOverlap( queryPeak, targetPeak, TxDb = NULL, pAdjustMethod = "BH", nShuffle = 1000, chainFile = NULL, pool = TRUE, mc.cores = detectCores() - 1, verbose = TRUE )enrichPeakOverlap( queryPeak, targetPeak, TxDb = NULL, pAdjustMethod = "BH", nShuffle = 1000, chainFile = NULL, pool = TRUE, mc.cores = detectCores() - 1, verbose = TRUE )
queryPeak |
query bed file or GRanges object |
targetPeak |
target bed file(s) or folder that containing bed files or a list of GRanges objects |
TxDb |
TxDb |
pAdjustMethod |
pvalue adjustment method |
nShuffle |
shuffle numbers |
chainFile |
chain file for liftOver |
pool |
logical, whether pool target peaks |
mc.cores |
number of cores, see mclapply |
verbose |
logical |
data.frame
G Yu
getting status of annotation
getAnnoStat(x)getAnnoStat(x)
x |
csAnno object |
prepare a bioregion of selected feature
getBioRegion( TxDb = NULL, upstream = 1000, downstream = 1000, by = "gene", type = "start_site" )getBioRegion( TxDb = NULL, upstream = 1000, downstream = 1000, by = "gene", type = "start_site" )
TxDb |
TxDb |
upstream |
upstream from start site or end site |
downstream |
downstream from start site or end site |
by |
one of 'gene', 'transcript', 'exon', 'intron' , '3UTR' , '5UTR', 'UTR' |
type |
one of "start_site", "end_site", "body" |
this function combined previous functions getPromoters(), getBioRegion() and getGeneBody() in order to solve the following issues.
(1) https://github.com/GuangchuangYu/ChIPseeker/issues/16
(2) https://github.com/GuangchuangYu/ChIPseeker/issues/87
The getBioRegion() function can prevoid a region of interest from
txdb object. There are three kinds of regions, start_site,
end_site and body.
We take transcript region to expain the differences of these three regions. tx: chr1 1000 1400.
body region refers to the 1000-1400bp.
start_site region with upstream = 100, downstream = 100 refers to 900-1100bp.
end_site region with upstream = 100, downstream = 100 refers to 1300-1500bp.
GRanges object
Guangchuang Yu, Ming L
get gene annotation, symbol, gene name etc.
getGeneAnno(annoDb, geneID, type, columns)getGeneAnno(annoDb, geneID, type, columns)
annoDb |
annotation package |
geneID |
query geneID |
type |
gene ID type |
columns |
names of columns to be obtained from database |
data.frame
G Yu
get Genomic Annotation of peaks
getGenomicAnnotation( peaks, distance, tssRegion = c(-3000, 3000), TxDb, level, genomicAnnotationPriority, sameStrand = FALSE )getGenomicAnnotation( peaks, distance, tssRegion = c(-3000, 3000), TxDb, level, genomicAnnotationPriority, sameStrand = FALSE )
peaks |
peaks in GRanges object |
distance |
distance of peak to TSS |
tssRegion |
tssRegion, default is -3kb to +3kb |
TxDb |
TxDb object |
level |
one of gene or transcript |
genomicAnnotationPriority |
genomic Annotation Priority |
sameStrand |
whether annotate gene in same strand |
character vector
G Yu
get genome version statistics collecting from GEO ChIPseq data
getGEOgenomeVersion()getGEOgenomeVersion()
data.frame
G Yu
get subset of GEO information by genome version keyword
getGEOInfo(genome, simplify = TRUE)getGEOInfo(genome, simplify = TRUE)
genome |
genome version |
simplify |
simplify result or not |
data.frame
G Yu
accessing species statistics collecting from GEO database
getGEOspecies()getGEOspecies()
data.frame
G Yu
get index of features that closest to peak and calculate distance
getNearestFeatureIndicesAndDistances( peaks, features, sameStrand = FALSE, ignoreOverlap = FALSE, ignoreUpstream = FALSE, ignoreDownstream = FALSE, overlap = "TSS" )getNearestFeatureIndicesAndDistances( peaks, features, sameStrand = FALSE, ignoreOverlap = FALSE, ignoreUpstream = FALSE, ignoreDownstream = FALSE, overlap = "TSS" )
peaks |
peak in GRanges |
features |
features in GRanges |
sameStrand |
logical, whether find nearest gene in the same strand |
ignoreOverlap |
logical, whether ignore overlap of TSS with peak |
ignoreUpstream |
logical, if True only annotate gene at the 3' of the peak. |
ignoreDownstream |
logical, if True only annotate gene at the 5' of the peak. |
overlap |
one of "TSS" or "all" |
list
G Yu
prepare the promoter regions
getPromoters(TxDb = NULL, upstream = 1000, downstream = 1000, by = "gene")getPromoters(TxDb = NULL, upstream = 1000, downstream = 1000, by = "gene")
TxDb |
TxDb |
upstream |
upstream from TSS site |
downstream |
downstream from TSS site |
by |
one of gene or transcript |
GRanges object
get filenames of sample files
getSampleFiles()getSampleFiles()
list of file names
G Yu
calculate the tag matrix
getTagMatrix( peak, upstream, downstream, windows, type, by, TxDb = NULL, weightCol = NULL, nbin = NULL, verbose = TRUE, ignore_strand = FALSE )getTagMatrix( peak, upstream, downstream, windows, type, by, TxDb = NULL, weightCol = NULL, nbin = NULL, verbose = TRUE, ignore_strand = FALSE )
peak |
peak peak file or GRanges object |
upstream |
the distance of upstream extension |
downstream |
the distance of downstream extension |
windows |
a collection of region |
type |
one of "start_site", "end_site", "body" |
by |
one of 'gene', 'transcript', 'exon', 'intron', '3UTR' , '5UTR', or specified by users |
TxDb |
TxDb or self-made granges object, served as txdb |
weightCol |
column name of weight, default is NULL |
nbin |
the amount of nbines |
verbose |
print message or not |
ignore_strand |
ignore the strand information or not |
getTagMatrix() function can produce the matrix for visualization.
peak stands for the peak file.
window stands for a collection of regions that users want to look into.
Users can use window to capture the peak of interest.
There are two ways to input window.
The first way is that users can use
getPromoters()/getBioRegion()/makeBioRegionFromGranges() to
get window and put it into getTagMatrix().
The second way is that users can use getTagMatrix() to
call getPromoters()/getBioRegion()/makeBioRegionFromGranges(). In this way
users do not need to input window parameter but they need to input
txdb.
txdb is a set of packages contained annotation
of regions of different genomes. Users can
get the regions of interest through specific functions. These specific functions
are built in getPromoters()/getBioRegion(). Many regions can not be gain
through txdb, like insulator and enhancer regions.
Users can provide these regions in the form of granges object.
These self-made granges object will be passed to TxDb parameter and they will
be passed to makeBioRegionFromGranges() to produce the window.
In a word, TxDb parameter is a reference information. Users can
pass txdb object or self-made granges into it.
Details see getPromoters,getBioRegion and makeBioRegionFromGranges
upstream and downstream parameter have different usages:
(1) window parameter is provided,
if type == 'body', upstream and downstream can use to extend
the flank of body region.
if type == 'start_site'/'end_site', upstream and downstream do not
play a role in getTagMatrix() function.
(2) window parameter is missing,
if type == 'body', upstream and downstream can use to extend
the flank of body region.
if type == 'start_site'/'end_site', upstream and downstream refer to
the upstream and downstream of the start_site or the end_site.
weightCol refers to column in peak file. This column acts as a weight vaule. Details
see https://github.com/YuLab-SMU/ChIPseeker/issues/15
nbin refers to the number of bins. getTagMatrix() provide a binning method
to get the tag matrix.
tagMatrix
calculate the tagMatrix by binning the idea was derived from the function of deeptools https://deeptools.readthedocs.io/en/develop/content/tools/computeMatrix.html
getTagMatrix.binning.internal( peak, weightCol = NULL, windows, nbin = 800, upstream = NULL, downstream = NULL, ignore_strand = FALSE )getTagMatrix.binning.internal( peak, weightCol = NULL, windows, nbin = 800, upstream = NULL, downstream = NULL, ignore_strand = FALSE )
peak |
peak peak file or GRanges object |
weightCol |
weightCol column name of weight, default is NULL |
windows |
windows a collection of region with equal or not equal size, eg. promoter region, gene region. |
nbin |
the amount of nbines needed to be splited and it should not be more than min_body_length |
upstream |
rel object, NULL or actual number |
downstream |
rel object, NULL or actual number |
ignore_strand |
ignore the strand information or not |
tagMatrix
calculate the tag matrix
getTagMatrix.internal(peak, weightCol = NULL, windows, ignore_strand = FALSE)getTagMatrix.internal(peak, weightCol = NULL, windows, ignore_strand = FALSE)
peak |
peak file or GRanges object |
weightCol |
column name of weight, default is NULL |
windows |
a collection of region with equal size, eg. promoter region. |
ignore_strand |
ignore the strand information or not |
tagMatrix
G Yu
Nested function for getTagMatrix() to deal with multiple windows
getTagMatrix2( peak, upstream, downstream, windows_name, type, by, TxDb = NULL, weightCol = NULL, nbin = NULL, verbose = TRUE, ignore_strand = FALSE )getTagMatrix2( peak, upstream, downstream, windows_name, type, by, TxDb = NULL, weightCol = NULL, nbin = NULL, verbose = TRUE, ignore_strand = FALSE )
peak |
peak peak file or GRanges object |
upstream |
the distance of upstream extension |
downstream |
the distance of downstream extension |
windows_name |
the names of windows |
type |
one of "start_site", "end_site", "body" |
by |
one of 'gene', 'transcript', 'exon', 'intron', '3UTR' , '5UTR', or specified by users |
TxDb |
TxDb or self-made granges object, served as txdb |
weightCol |
column name of weight, default is NULL |
nbin |
the amount of nbines |
verbose |
print message or not |
ignore_strand |
ignore the strand information or not |
This is an internal function.
tagMatrix
internal function
getTagMatrix2.binning.internal( peak, weightCol = NULL, windows, windows_name, nbin = 800, upstream = NULL, downstream = NULL, ignore_strand = FALSE )getTagMatrix2.binning.internal( peak, weightCol = NULL, windows, windows_name, nbin = 800, upstream = NULL, downstream = NULL, ignore_strand = FALSE )
peak |
peak peak file or GRanges object |
weightCol |
column name of weight, default is NULL |
windows |
a collection of region |
windows_name |
the name of windows |
nbin |
the amount of nbines |
upstream |
the distance of upstream extension |
downstream |
the distance of downstream extension |
ignore_strand |
ignore the strand information or not |
getTagMatrix2.internal
getTagMatrix2.internal( peak, weightCol = NULL, windows, windows_name, ignore_strand = FALSE )getTagMatrix2.internal( peak, weightCol = NULL, windows, windows_name, ignore_strand = FALSE )
peak |
peak peak file or GRanges object |
weightCol |
column name of weight, default is NULL |
windows |
a collection of region |
windows_name |
the name of windows |
ignore_strand |
ignore the strand information or not |
make windows from granges object
makeBioRegionFromGranges(gr, by, type, upstream = 1000, downstream = 1000)makeBioRegionFromGranges(gr, by, type, upstream = 1000, downstream = 1000)
gr |
a grange object contain region of interest |
by |
specify be users, e.g. gene, insulator, enhancer |
type |
one of "start_site", "end_site", "body" |
upstream |
upstream from start site or end site, can be NULL if the type == 'body' |
downstream |
downstream from start site or end site, can be NULL if the type == 'body' |
makeBioRegionFromGranges() function can make bioregion from granges object.
The differences between makeBioRegionFromGranges() and getBioRegion() is that
getBioRegion() get the region object from txdb object but
makeBioRegionFromGranges() get the region from the granges object provided by users.
For example, txdb object do not contain insulator or enhancer regions. Users can
provide these regions through self-made granges object
There are three kinds of regions, start_site, end_site and body.
We take enhancer region to explain the differences of these three regions. enhancer: chr1 1000 1400.
body region refers to the 1000-1400bp.
start_site region with upstream = 100, downstream = 100 refers to 900-1100bp.
end_site region with upstream = 100, downstream = 100 refers to 1300-1500bp.
In makeBioRegionFromGranges(), upstream and downstream can be
NULL if the type == 'body'. by should be specified by users and
can not be omitted. by parameter will be used to made labels. type should also
be specified.
https://github.com/YuLab-SMU/ChIPseeker/issues/189
GRanges object
calculate the overlap matrix, which is useful for vennplot
overlap(Sets)overlap(Sets)
Sets |
a list of objects |
data.frame
G Yu
plot peak heatmap and profile in a picture
peak_Profile_Heatmap( peak, weightCol = NULL, TxDb = NULL, upstream = 1000, downstream = 1000, xlab = "", ylab = "", title = NULL, palette = NULL, verbose = TRUE, by = "gene", type = "start_site", nbin = NULL, ignore_strand = FALSE, windows_name = NULL, ncol = NULL, nrow = NULL, facet_label_text_size = 12, conf, facet = "row", free_y = TRUE, height_proportion = 4 )peak_Profile_Heatmap( peak, weightCol = NULL, TxDb = NULL, upstream = 1000, downstream = 1000, xlab = "", ylab = "", title = NULL, palette = NULL, verbose = TRUE, by = "gene", type = "start_site", nbin = NULL, ignore_strand = FALSE, windows_name = NULL, ncol = NULL, nrow = NULL, facet_label_text_size = 12, conf, facet = "row", free_y = TRUE, height_proportion = 4 )
peak |
peak file or GRanges object |
weightCol |
column name of weight |
TxDb |
TxDb object |
upstream |
upstream position |
downstream |
downstream position |
xlab |
xlab |
ylab |
ylab |
title |
title |
palette |
palette to be filled in,details see scale_colour_brewer |
verbose |
print message or not |
by |
one of 'gene', 'transcript', 'exon', 'intron' , '3UTR' , '5UTR', 'UTR' |
type |
one of "start_site", "end_site", "body" |
nbin |
the amount of nbines |
ignore_strand |
ignore the strand information or not |
windows_name |
the name for each window, which will also be showed in the picture as labels |
ncol |
the ncol of plotting a list of peak |
nrow |
the nrow of plotting a list of peak |
facet_label_text_size |
the size of facet label text |
conf |
confidence interval |
facet |
one of 'none', 'row' and 'column' |
free_y |
if TRUE, y will be scaled by AvgProf |
height_proportion |
the proportion of profiling picture and heatmap |
plot the heatmap of peaks
peakHeatmap( peak, weightCol = NULL, TxDb = NULL, upstream = 1000, downstream = 1000, xlab = "", ylab = "", title = NULL, palette = NULL, verbose = TRUE, by = "gene", type = "start_site", nbin = NULL, ignore_strand = FALSE, windows, ncol = NULL, nrow = NULL )peakHeatmap( peak, weightCol = NULL, TxDb = NULL, upstream = 1000, downstream = 1000, xlab = "", ylab = "", title = NULL, palette = NULL, verbose = TRUE, by = "gene", type = "start_site", nbin = NULL, ignore_strand = FALSE, windows, ncol = NULL, nrow = NULL )
peak |
peak file or GRanges object |
weightCol |
column name of weight |
TxDb |
TxDb object |
upstream |
upstream position |
downstream |
downstream position |
xlab |
xlab |
ylab |
ylab |
title |
title |
palette |
palette to be filled in,details see scale_colour_brewer |
verbose |
print message or not |
by |
one of 'gene', 'transcript', 'exon', 'intron' , '3UTR' , '5UTR', 'UTR' |
type |
one of "start_site", "end_site", "body" |
nbin |
the amount of nbines |
ignore_strand |
ignore the strand information or not |
windows |
a collection of region |
ncol |
the ncol of plotting a list of peak |
nrow |
the nrow of plotting a list of peak |
figure
G Yu
plot the heatmap of peaks align to a sets of regions
peakHeatmap_multiple_Sets( peak, weightCol = NULL, TxDb = NULL, upstream = 1000, downstream = 1000, xlab = "", ylab = "", title = NULL, palette = NULL, verbose = TRUE, by = "gene", type = "start_site", nbin = NULL, ignore_strand = FALSE, windows_name = NULL, ncol = NULL, nrow = NULL, facet_label_text_size = 12 )peakHeatmap_multiple_Sets( peak, weightCol = NULL, TxDb = NULL, upstream = 1000, downstream = 1000, xlab = "", ylab = "", title = NULL, palette = NULL, verbose = TRUE, by = "gene", type = "start_site", nbin = NULL, ignore_strand = FALSE, windows_name = NULL, ncol = NULL, nrow = NULL, facet_label_text_size = 12 )
peak |
peak file or GRanges object |
weightCol |
column name of weight |
TxDb |
TxDb object |
upstream |
upstream position |
downstream |
downstream position |
xlab |
xlab |
ylab |
ylab |
title |
title |
palette |
palette to be filled in,details see scale_colour_brewer |
verbose |
print message or not |
by |
one of 'gene', 'transcript', 'exon', 'intron' , '3UTR' , '5UTR', 'UTR' |
type |
one of "start_site", "end_site", "body" |
nbin |
the amount of nbines |
ignore_strand |
ignore the strand information or not |
windows_name |
the name for each window, which will also be showed in the picture as labels |
ncol |
the ncol of plotting a list of peak |
nrow |
the nrow of plotting a list of peak |
facet_label_text_size |
the size of facet label text |
figure
plotAnnoBar method for csAnno instance
plotAnnoBar( x, xlab = "", ylab = "Percentage(%)", title = "Feature Distribution", ... ) ## S4 method for signature 'list' plotAnnoBar( x, xlab = "", ylab = "Percentage(%)", title = "Feature Distribution", ... ) plotAnnoBar(x, xlab="", ylab='Percentage(%)',title="Feature Distribution", ...)plotAnnoBar( x, xlab = "", ylab = "Percentage(%)", title = "Feature Distribution", ... ) ## S4 method for signature 'list' plotAnnoBar( x, xlab = "", ylab = "Percentage(%)", title = "Feature Distribution", ... ) plotAnnoBar(x, xlab="", ylab='Percentage(%)',title="Feature Distribution", ...)
x |
|
xlab |
xlab |
ylab |
ylab |
title |
title |
... |
additional paramter |
plot
Guangchuang Yu https://guangchuangyu.github.io
plot feature distribution based on their chromosome region
plotAnnoBar.data.frame( anno.df, xlab = "", ylab = "Percentage(%)", title = "Feature Distribution", categoryColumn )plotAnnoBar.data.frame( anno.df, xlab = "", ylab = "Percentage(%)", title = "Feature Distribution", categoryColumn )
anno.df |
annotation stats |
xlab |
xlab |
ylab |
ylab |
title |
plot title |
categoryColumn |
category column |
plot chromosome region features
bar plot that summarize genomic features of peaks
Guangchuang Yu https://yulab-smu.top
plotAnnoPie method for csAnno instance
plotAnnoPie( x, ndigit = 2, cex = 0.9, col = NA, legend.position = "rightside", pie3D = FALSE, radius = 0.8, ... ) plotAnnoPie(x,ndigit=2,cex=0.9,col=NA,legend.position="rightside",pie3D=FALSE,radius=0.8,...)plotAnnoPie( x, ndigit = 2, cex = 0.9, col = NA, legend.position = "rightside", pie3D = FALSE, radius = 0.8, ... ) plotAnnoPie(x,ndigit=2,cex=0.9,col=NA,legend.position="rightside",pie3D=FALSE,radius=0.8,...)
x |
|
ndigit |
number of digit to round |
cex |
label cex |
col |
color |
legend.position |
topright or other. |
pie3D |
plot in 3D or not |
radius |
radius of the pie |
... |
extra parameter |
plot
Guangchuang Yu https://guangchuangyu.github.io
pieplot from peak genomic annotation
plotAnnoPie.csAnno( x, ndigit = 2, cex = 0.8, col = NA, legend.position = "rightside", pie3D = FALSE, radius = 0.8, ... )plotAnnoPie.csAnno( x, ndigit = 2, cex = 0.8, col = NA, legend.position = "rightside", pie3D = FALSE, radius = 0.8, ... )
x |
csAnno object |
ndigit |
number of digit to round |
cex |
label cex |
col |
color |
legend.position |
topright or other. |
pie3D |
plot in 3D or not |
radius |
radius of Pie |
... |
extra parameter |
pie plot of peak genomic feature annotation
Guangchuang Yu https://yulab-smu.top
## Not run: require(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene peakfile <- system.file("extdata", "sample_peaks.txt", package="chipseeker") peakAnno <- annotatePeak(peakfile, TxDb=txdb) plotAnnoPie(peakAnno) ## End(Not run)## Not run: require(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene peakfile <- system.file("extdata", "sample_peaks.txt", package="chipseeker") peakAnno <- annotatePeak(peakfile, TxDb=txdb) plotAnnoPie(peakAnno) ## End(Not run)
plot the profile of peaks
plotAvgProf( tagMatrix, xlim, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", conf, facet = "none", free_y = TRUE, origin_label = "TSS", verbose = TRUE, ... )plotAvgProf( tagMatrix, xlim, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", conf, facet = "none", free_y = TRUE, origin_label = "TSS", verbose = TRUE, ... )
tagMatrix |
tagMatrix or a list of tagMatrix |
xlim |
xlim |
xlab |
x label |
ylab |
y label |
conf |
confidence interval |
facet |
one of 'none', 'row' and 'column' |
free_y |
if TRUE, y will be scaled by AvgProf |
origin_label |
label of the center |
verbose |
print message or not |
... |
additional parameter |
ggplot object
G Yu; Y Yan
plot the profile of peaks by binning
plotAvgProf.binning( tagMatrix, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", conf, facet = "none", free_y = TRUE, upstream = NULL, downstream = NULL, label, ... )plotAvgProf.binning( tagMatrix, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", conf, facet = "none", free_y = TRUE, upstream = NULL, downstream = NULL, label, ... )
tagMatrix |
tagMatrix or a list of tagMatrix |
xlab |
x label |
ylab |
y label |
conf |
confidence interval |
facet |
one of 'none', 'row' and 'column' |
free_y |
if TRUE, y will be scaled |
upstream |
rel object reflects the percentage of flank extension, e.g rel(0.2) integer reflects the actual length of flank extension or TSS region NULL reflects the gene body with no extension |
downstream |
rel object reflects the percentage of flank extension, e.g rel(0.2) integer reflects the actual length of flank extension or TSS region NULL reflects the gene body with no extension |
label |
label |
... |
additional parameter |
ggplot object
plot the profile of peaks that align to flank sequences of TSS
plotAvgProf2( peak, weightCol = NULL, TxDb = NULL, upstream = 1000, downstream = 1000, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", conf, facet = "none", free_y = TRUE, verbose = TRUE, ignore_strand = FALSE, ... )plotAvgProf2( peak, weightCol = NULL, TxDb = NULL, upstream = 1000, downstream = 1000, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", conf, facet = "none", free_y = TRUE, verbose = TRUE, ignore_strand = FALSE, ... )
peak |
peak file or GRanges object |
weightCol |
column name of weight |
TxDb |
TxDb object |
upstream |
upstream position |
downstream |
downstream position |
xlab |
xlab |
ylab |
ylab |
conf |
confidence interval |
facet |
one of 'none', 'row' and 'column' |
free_y |
if TRUE, y will be scaled by AvgProf |
verbose |
print message or not |
ignore_strand |
ignore the strand information or not |
... |
additional parameter |
This function is the old function of plotPeakProf2. It can
only plot the start site region of gene.
ggplot object
G Yu, Ming L
plotDistToTSS method for csAnno instance
plotDistToTSS( x, distanceColumn = "distanceToTSS", xlab = "", ylab = "Binding sites (%) (5'->3')", title = "Distribution of transcription factor-binding loci relative to TSS", ... ) ## S4 method for signature 'list' plotDistToTSS( x, distanceColumn = "distanceToTSS", xlab = "", ylab = "Binding sites (%) (5'->3')", title = "Distribution of transcription factor-binding loci relative to TSS", distanceBreaks = c(0, 1000, 3000, 5000, 10000, 1e+05), palette = NULL, ... ) plotDistToTSS(x,distanceColumn="distanceToTSS", xlab="", ylab="Binding sites (%) (5'->3')", title="Distribution of transcription factor-binding loci relative to TSS",...)plotDistToTSS( x, distanceColumn = "distanceToTSS", xlab = "", ylab = "Binding sites (%) (5'->3')", title = "Distribution of transcription factor-binding loci relative to TSS", ... ) ## S4 method for signature 'list' plotDistToTSS( x, distanceColumn = "distanceToTSS", xlab = "", ylab = "Binding sites (%) (5'->3')", title = "Distribution of transcription factor-binding loci relative to TSS", distanceBreaks = c(0, 1000, 3000, 5000, 10000, 1e+05), palette = NULL, ... ) plotDistToTSS(x,distanceColumn="distanceToTSS", xlab="", ylab="Binding sites (%) (5'->3')", title="Distribution of transcription factor-binding loci relative to TSS",...)
x |
|
distanceColumn |
distance column name |
xlab |
xlab |
ylab |
ylab |
title |
title |
... |
additional parameter |
distanceBreaks |
breaks of distance, default is 'c(0, 1000, 3000, 5000, 10000, 100000)' |
palette |
palette name for coloring different distances. Run 'RColorBrewer::display.brewer.all()' to see all applicable values. |
plot
Guangchuang Yu https://guangchuangyu.github.io
plot feature distribution based on the distances to the TSS
plotDistToTSS.data.frame( peakDist, distanceColumn = "distanceToTSS", distanceBreaks = c(0, 1000, 3000, 5000, 10000, 1e+05), palette = NULL, xlab = "", ylab = "Binding sites (%) (5'->3')", title = "Distribution of transcription factor-binding loci relative to TSS", categoryColumn = ".id" )plotDistToTSS.data.frame( peakDist, distanceColumn = "distanceToTSS", distanceBreaks = c(0, 1000, 3000, 5000, 10000, 1e+05), palette = NULL, xlab = "", ylab = "Binding sites (%) (5'->3')", title = "Distribution of transcription factor-binding loci relative to TSS", categoryColumn = ".id" )
peakDist |
peak annotation |
distanceColumn |
column name of the distance from peak to nearest gene |
distanceBreaks |
default is 'c(0, 1000, 3000, 5000, 10000, 100000)' |
palette |
palette name for coloring different distances. Run 'RColorBrewer::display.brewer.all()' to see all applicable values. |
xlab |
x label |
ylab |
y lable |
title |
figure title |
categoryColumn |
category column, default is ".id" |
bar plot that summarize distance from peak to TSS of the nearest gene.
Guangchuang Yu https://guangchuangyu.github.io
## Not run: require(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene peakfile <- system.file("extdata", "sample_peaks.txt", package="ChIPseeker") peakAnno <- annotatePeak(peakfile, TxDb=txdb) plotDistToTSS(peakAnno) ## End(Not run)## Not run: require(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene peakfile <- system.file("extdata", "sample_peaks.txt", package="ChIPseeker") peakAnno <- annotatePeak(peakfile, TxDb=txdb) plotDistToTSS(peakAnno) ## End(Not run)
internal function for plotPeakProf_MultiWindows
plotMultiProf( tagMatrix, conf, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", facet = "none", free_y = TRUE, ... )plotMultiProf( tagMatrix, conf, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", facet = "none", free_y = TRUE, ... )
tagMatrix |
tagMatrix |
conf |
confidence interval |
xlab |
xlab |
ylab |
ylab |
facet |
one of 'none', 'row' and 'column' |
free_y |
if TRUE, y will be scaled by AvgProf |
... |
additional parameter |
internal function
plotMultiProf.binning( tagMatrix, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", conf, facet = "none", free_y = TRUE, upstream = NULL, downstream = NULL, label, ... )plotMultiProf.binning( tagMatrix, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", conf, facet = "none", free_y = TRUE, upstream = NULL, downstream = NULL, label, ... )
tagMatrix |
tagMatrix |
xlab |
xlab |
ylab |
ylab |
conf |
confidence interval |
facet |
one of 'none', 'row' and 'column' |
free_y |
if TRUE, y will be scaled by AvgProf |
upstream |
the upstream extension |
downstream |
the downstream extension |
label |
the label of the center |
... |
additional parameter |
internal function
plotMultiProf.binning.internal( tagMatrix, conf, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", facet = "none", free_y = TRUE, upstream = NULL, downstream = NULL, label, ... )plotMultiProf.binning.internal( tagMatrix, conf, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", facet = "none", free_y = TRUE, upstream = NULL, downstream = NULL, label, ... )
tagMatrix |
tagMatrix |
conf |
confidence interval |
xlab |
xlab |
ylab |
ylab |
facet |
one of 'none', 'row' and 'column' |
free_y |
if TRUE, y will be scaled by AvgProf |
upstream |
the upstream extension |
downstream |
the downstream extension |
label |
the label of the center |
... |
additional parameter |
internal function
plotMultiProf.normal( tagMatrix, xlim, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", conf, facet = "none", free_y = TRUE, origin_label = "TSS", verbose = TRUE, ... )plotMultiProf.normal( tagMatrix, xlim, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", conf, facet = "none", free_y = TRUE, origin_label = "TSS", verbose = TRUE, ... )
tagMatrix |
tagMatrix |
xlim |
xlim |
xlab |
xlab |
ylab |
ylab |
conf |
confidence interval |
facet |
one of 'none', 'row' and 'column' |
free_y |
if TRUE, y will be scaled by AvgProf |
origin_label |
the label of the center |
verbose |
print message or not |
... |
additional parameter |
internal function
plotMultiProf.normal.internal( tagMatrix, conf, xlim = c(-3000, 3000), xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", facet = "row", free_y = TRUE, origin_label, ... )plotMultiProf.normal.internal( tagMatrix, conf, xlim = c(-3000, 3000), xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", facet = "row", free_y = TRUE, origin_label, ... )
tagMatrix |
tagMatrix |
conf |
confidence interval |
xlim |
xlim |
xlab |
xlab |
ylab |
ylab |
facet |
one of 'none', 'row' and 'column' |
free_y |
if TRUE, y will be scaled by AvgProf |
origin_label |
the label of the center |
... |
additional parameter |
plot the profile of peaks
'
plotPeakProf_MultiWindows() is almost the same as plotPeakProf2(), having
the main difference of accepting two or more granges objects. Accepting more
granges objects can help compare the same peaks in different windows.
plotPeakProf( tagMatrix = NULL, peak, upstream, downstream, conf, by, type, windows_name = NULL, weightCol = NULL, TxDb = NULL, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", facet = "row", free_y = TRUE, verbose = TRUE, nbin = NULL, ignore_strand = FALSE, ... )plotPeakProf( tagMatrix = NULL, peak, upstream, downstream, conf, by, type, windows_name = NULL, weightCol = NULL, TxDb = NULL, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", facet = "row", free_y = TRUE, verbose = TRUE, nbin = NULL, ignore_strand = FALSE, ... )
tagMatrix |
tagMatrix or a list of tagMatrix |
peak |
peak file or GRanges object |
upstream |
upstream position |
downstream |
downstream position |
conf |
confidence interval |
by |
feature of interest |
type |
one of "start_site", "end_site", "body" |
windows_name |
the name for each window, which will also be showed in the picture as labels |
weightCol |
column name of weight |
TxDb |
TxDb object or self-made granges objects |
xlab |
xlab |
ylab |
ylab |
facet |
one of 'none', 'row' and 'column' |
free_y |
if TRUE, y will be scaled by AvgProf |
verbose |
print message or not |
nbin |
the amount of bines |
ignore_strand |
ignore the strand information or not |
... |
additional parameter |
TxDb parameter can accept txdb object.
But many regions can not be obtained by txdb object. In this case,
Users can provide self-made granges served the same role
as txdb object and pass to TxDb object.
by the features of interest.
(1) if users use txdb, by can be one of 'gene', 'transcript', 'exon',
'intron' , '3UTR' , '5UTR', 'UTR'. These features can be obtained by functions from txdb object.
(2) if users use self-made granges object, by can be everything. Because this by
will not pass to functions to get features, which is different from the case of using
txdb object. This by is only used to made labels showed in picture.
type means the property of the region. one of the "start site",
"end site" and "body".
upstream and downstream parameter have different usages:
(1) if type == 'body', upstream and downstream can use to extend
the flank of body region.
(2) if type == 'start_site'/'end_site', upstream and downstream refer to
the upstream and downstream of the start_site or the end_site.
weightCol refers to column in peak file. This column acts as a weight value. Details
see https://github.com/YuLab-SMU/ChIPseeker/issues/15
nbin refers to the number of bins. getTagMatrix() provide a binning method
to get the tag matrix.
There are two ways input a list of window.
(1) Users can input a list of self-made granges objects
(2) Users can input a list of by and only one type. In this way,
plotPeakProf_MultiWindows() can made a list of window from txdb object based on by and type.
Warning:
(1) All of these window should be the same type. It means users can only compare a list of "start site"/"end site"/"body region" with the same upstream and downstream.
(2) So it will be only one type and several by.
(3) Users can make window by txdb object or self-made granges object. Users can only
choose one of 'gene', 'transcript', 'exon', 'intron' , '3UTR' , '5UTR' or 'UTR' in the
way of using txdb object. User can input any by in the way of using
self-made granges object.
(4) Users can mingle the by designed for the two ways. plotPeakProf_MultiWindows can
accpet the hybrid by. But the above rules should be followed.
https://github.com/YuLab-SMU/ChIPseeker/issues/189
ggplot object
plot the profile of peaks in two or more windows
plotPeakProf_MultiWindows( peak, upstream, downstream, conf, by, type, windows_name = NULL, weightCol = NULL, TxDb = NULL, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", facet = "row", free_y = TRUE, verbose = TRUE, nbin = NULL, ignore_strand = FALSE, ... )plotPeakProf_MultiWindows( peak, upstream, downstream, conf, by, type, windows_name = NULL, weightCol = NULL, TxDb = NULL, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", facet = "row", free_y = TRUE, verbose = TRUE, nbin = NULL, ignore_strand = FALSE, ... )
peak |
peak file or GRanges object |
upstream |
upstream position |
downstream |
downstream position |
conf |
confidence interval |
by |
feature of interest |
type |
one of "start_site", "end_site", "body" |
windows_name |
the name for each window, which will also be showed in the picture as labels |
weightCol |
column name of weight |
TxDb |
TxDb object or self-made granges objects |
xlab |
xlab |
ylab |
ylab |
facet |
one of 'none', 'row' and 'column' |
free_y |
if TRUE, y will be scaled by AvgProf |
verbose |
print message or not |
nbin |
the amount of bines |
ignore_strand |
ignore the strand information or not |
... |
additional parameter |
This function comes from https://github.com/YuLab-SMU/ChIPseeker/issues/189
'
plotPeakProf_MultiWindows() is almost the same as plotPeakProf2(), having
the main difference of accepting two or more granges objects. Accepting more
granges objects can help compare the same peaks in different windows.
TxDb parameter can accept txdb object.
But many regions can not be obtained by txdb object. In this case,
Users can provide self-made granges served the same role
as txdb object and pass to TxDb object.
by the features of interest.
(1) if users use txdb, by can be one of 'gene', 'transcript', 'exon',
'intron' , '3UTR' , '5UTR', 'UTR'. These features can be obtained by functions from txdb object.
(2) if users use self-made granges object, by can be everything. Because this by
will not pass to functions to get features, which is different from the case of using
txdb object. This by is only used to made labels showed in picture.
type means the property of the region. one of the "start site",
"end site" and "body".
upstream and downstream parameter have different usages:
(1) if type == 'body', upstream and downstream can use to extend
the flank of body region.
(2) if type == 'start_site'/'end_site', upstream and downstream refer to
the upstream and downstream of the start_site or the end_site.
weightCol refers to column in peak file. This column acts as a weight value. Details
see https://github.com/YuLab-SMU/ChIPseeker/issues/15
nbin refers to the number of bins. getTagMatrix() provide a binning method
to get the tag matrix.
There are two ways input a list of window.
(1) Users can input a list of self-made granges objects
(2) Users can input a list of by and only one type. In this way,
plotPeakProf_MultiWindows() can made a list of window from txdb object based on by and type.
Warning:
(1) All of these window should be the same type. It means users can only compare a list of "start site"/"end site"/"body region" with the same upstream and downstream.
(2) So it will be only one type and several by.
(3) Users can make window by txdb object or self-made granges object. Users can only
choose one of 'gene', 'transcript', 'exon', 'intron' , '3UTR' , '5UTR' or 'UTR' in the
way of using txdb object. User can input any by in the way of using
self-made granges object.
(4) Users can mingle the by designed for the two ways. plotPeakProf_MultiWindows can
accpet the hybrid by. But the above rules should be followed.
ggplot object
plot the profile of peaks automatically
plotPeakProf2( peak, upstream, downstream, conf, by, type, weightCol = NULL, TxDb = NULL, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", facet = "none", free_y = TRUE, verbose = TRUE, nbin = NULL, ignore_strand = FALSE, ... )plotPeakProf2( peak, upstream, downstream, conf, by, type, weightCol = NULL, TxDb = NULL, xlab = "Genomic Region (5'->3')", ylab = "Peak Count Frequency", facet = "none", free_y = TRUE, verbose = TRUE, nbin = NULL, ignore_strand = FALSE, ... )
peak |
peak file or GRanges object |
upstream |
upstream position |
downstream |
downstream position |
conf |
confidence interval |
by |
e.g. 'gene', 'transcript', 'exon' or features of interest(e.g. "enhancer") |
type |
one of "start_site", "end_site", "body" |
weightCol |
column name of weight |
TxDb |
TxDb object, or self-made granges object |
xlab |
xlab |
ylab |
ylab |
facet |
one of 'none', 'row' and 'column' |
free_y |
if TRUE, y will be scaled by AvgProf |
verbose |
print message or not |
nbin |
the amount of nbines |
ignore_strand |
ignore the strand information or not |
... |
additional parameter |
peak stands for the peak file.
by the features of interest.
(1) if users use txdb, by can be one of 'gene', 'transcript', 'exon',
'intron' , '3UTR' , '5UTR', 'UTR'. These features can be obtained by functions from txdb object.
(2) if users use self-made granges object, by can be everything. Because this by
will not pass to functions to get features, which is different from the case of using
txdb object. This by is only used to made labels showed in picture.
type means the property of the region. one of the "start site",
"end site" and "body".
upstream and downstream parameter have different usages:
(1) if type == 'body', upstream and downstream can use to extend
the flank of body region.
(2) if type == 'start_site'/'end_site', upstream and downstream refer to
the upstream and downstream of the start_site or the end_site.
weightCol refers to column in peak file. This column acts as a weight vaule. Details
see https://github.com/YuLab-SMU/ChIPseeker/issues/15
nbin refers to the number of bins, providing a binning method
to get the tag matrix.
TxDb parameter can accept txdb object.
But many regions can not be obtained by txdb object. In this case,
Users can provide self-made granges served the same role
as txdb object and pass to TxDb object.
plotPeakProf2() is different from the plotPeakProf(). plotPeakProf2() do not
need to provide window parameter, which means plotPeakProf2() will call relevent
functions to make window automatically.
ggplot object
G Yu, Ming Li
read peak file and store in data.frame or GRanges object
readPeakFile(peakfile, as = "GRanges", ...)readPeakFile(peakfile, as = "GRanges", ...)
peakfile |
peak file |
as |
output format, one of GRanges or data.frame |
... |
additional parameter (pass to 'utils::read.delim()') |
peak information, in GRanges or data.frame object
G Yu
peakfile <- system.file("extdata", "sample_peaks.txt", package="ChIPseeker") peak.gr <- readPeakFile(peakfile, as="GRanges") peak.grpeakfile <- system.file("extdata", "sample_peaks.txt", package="ChIPseeker") peak.gr <- readPeakFile(peakfile, as="GRanges") peak.gr
annotate genomic regions to genes in many-to-many mapping
seq2gene(seq, tssRegion, flankDistance, TxDb, sameStrand = FALSE)seq2gene(seq, tssRegion, flankDistance, TxDb, sameStrand = FALSE)
seq |
genomic regions in GRanges object |
tssRegion |
TSS region |
flankDistance |
flanking search radius |
TxDb |
TranscriptDb object |
sameStrand |
logical whether find nearest/overlap gene in the same strand |
This funciton associates genomic regions with coding genes in a many-to-many mapping. It first maps genomic regions to host genes (either located in exon or intron), proximal genes (located in promoter regions) and flanking genes (located in upstream and downstream within user specify distance).
gene vector
Guangchuang Yu
## Not run: library(TxDb.Hsapiens.UCSC.hg19.knownGene) TxDb <- TxDb.Hsapiens.UCSC.hg19.knownGene file <- getSampleFiles()[[1]] # a bed file gr <- readPeakFile(file) genes <- seq2gene(gr, tssRegion=c(-1000, 1000), flankDistance = 3000, TxDb) ## End(Not run)## Not run: library(TxDb.Hsapiens.UCSC.hg19.knownGene) TxDb <- TxDb.Hsapiens.UCSC.hg19.knownGene file <- getSampleFiles()[[1]] # a bed file gr <- readPeakFile(file) genes <- seq2gene(gr, tssRegion=c(-1000, 1000), flankDistance = 3000, TxDb) ## End(Not run)
show method for csAnno instance
show(object)show(object)
object |
A |
message
Guangchuang Yu https://guangchuangyu.github.io
shuffle the position of peak
shuffle(peak.gr, TxDb)shuffle(peak.gr, TxDb)
peak.gr |
GRanges object |
TxDb |
TxDb |
GRanges object
G Yu
plot the heatmap of tagMatrix
tagHeatmap( tagMatrix, xlab = "", ylab = "", title = NULL, palette = "RdBu", nrow = NULL, ncol = NULL )tagHeatmap( tagMatrix, xlab = "", ylab = "", title = NULL, palette = "RdBu", nrow = NULL, ncol = NULL )
tagMatrix |
tagMatrix or a list of tagMatrix |
xlab |
xlab |
ylab |
ylab |
title |
title |
palette |
palette to be filled in,details see scale_colour_brewer |
nrow |
the nrow of plotting a list of peak |
ncol |
the ncol of plotting a list of peak |
figure
G Yu
upsetplot method generics
upsetplot(x, ...)upsetplot(x, ...)
x |
A |
... |
additional parameter |
plot
Guangchuang Yu https://guangchuangyu.github.io
vennpie method generics
vennpie(x, r = 0.2, cex = 1.2, ...) vennpie(x, r = 0.2, cex=1.2, ...)vennpie(x, r = 0.2, cex = 1.2, ...) vennpie(x, r = 0.2, cex=1.2, ...)
x |
A |
r |
initial radius |
cex |
value to adjust legend |
... |
additional parameter |
plot
Guangchuang Yu https://guangchuangyu.github.io
plot the overlap of a list of object
vennplot(Sets, by = "gplots", ...)vennplot(Sets, by = "gplots", ...)
Sets |
a list of object, can be vector or GRanges object |
by |
one of gplots, ggVennDiagram or Vennerable |
... |
extra parameters using ggVennDiagram. Details see ggVennDiagram |
There are two ways to plot, which users can specify through 'by'.
The first way is to use 'gplots' packages, by setting 'by = gplots'. This method is default method. The venn plot produced through this way has no color.
The second way is to use 'ggVennDiagram' packages, by setting 'by = ggVennDiagram'. The venn plot produced through this way has colors which can be defined by users using ggplot2 grammar e.g.(scale_fill_distiller()). And users can specify any details, like digital number, text size and showing percentage or not, by inputting '...' extra parameters.
venn plot that summarize the overlap of peaks from different experiments or gene annotation from different peak files.
G Yu
## example not run ## require(TxDb.Hsapiens.UCSC.hg19.knownGene) ## txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene ## peakfiles <- getSampleFiles() ## peakAnnoList <- lapply(peakfiles, annotatePeak) ## names(peakAnnoList) <- names(peakfiles) ## genes= lapply(peakAnnoList, function(i) as.data.frame(i)$geneId) ## vennplot(genes)## example not run ## require(TxDb.Hsapiens.UCSC.hg19.knownGene) ## txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene ## peakfiles <- getSampleFiles() ## peakAnnoList <- lapply(peakfiles, annotatePeak) ## names(peakAnnoList) <- names(peakfiles) ## genes= lapply(peakAnnoList, function(i) as.data.frame(i)$geneId) ## vennplot(genes)
vennplot for peak files
vennplot.peakfile(files, labels = NULL)vennplot.peakfile(files, labels = NULL)
files |
peak files |
labels |
labels for peak files |
figure
G Yu