| Title: | High Throughput Chromosome Conformation Capture analysis |
|---|---|
| Description: | The HiTC package was developed to explore high-throughput 'C' data such as 5C or Hi-C. Dedicated R classes as well as standard methods for quality controls, normalization, visualization, and further analysis are also provided. |
| Authors: | Nicolas Servant |
| Maintainer: | Nicolas Servant <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.51.0 |
| Built: | 2024-11-18 04:43:02 UTC |
| Source: | https://github.com/bioc/HiTC |
Binning of 'C' contact map
binningC(x, binsize=100000, bin.adjust=TRUE, upa=TRUE, method=c("sum", "median","mean"), use.zero=TRUE, step=1, optimize.by = c("speed", "memory"))binningC(x, binsize=100000, bin.adjust=TRUE, upa=TRUE, method=c("sum", "median","mean"), use.zero=TRUE, step=1, optimize.by = c("speed", "memory"))
x |
object that inherits from class |
binsize |
size of the bin to consider for windowing |
bin.adjust |
logical; adjust the size of the bin to the size of the genomic region |
upa |
logical; unique primer assignment. Allow one primer to belong to one or several bins |
method |
the method used to combine the counts. Must be ‘mean’, ‘median’ or ‘sum’ |
use.zero |
logical; use the zero values in the |
step |
numeric; binning step size in |
optimize.by |
"speed" will use faster methods but more RAM, and "memory" will be slower, but require less RAM |
bin.adjust allows to work with bins of exactly the same size. Otherwise,
the last bin is usually smaller than the others.
This function aims at changing the resolution of both 5C or Hi-C
data. In case of 5C data (i.e. raw, not binned data), the contacts between all
pairs of primers will be summarized per genomic bins (the median of all
pairwise primers is usually used). In case of binned data
(as Hi-C maps), the function can generate smaller resolution maps by
aggregating bins. For instance, going from a 40kb resolution to a 1Mb
resolution.
The method is used to combine the counts in a bin, must be ‘mean’, ‘median’ or ‘sum’.
The step parameter allows to choose the overlap between the
bins. A step of 2 means a 50% overlap between two bins, a step
of 3 means a 60% overlap between two bins, etc.
An HTCexp-class object with binned intraction data. In this
case, the genomic intervals are converted into bins of fixed size.
The contact matrix is symetric.
N. Servant, B. Lajoie
data(Nora_5C) ## Data binning 100kb, with a 1/3 overlap E14.bin <- binningC(E14$chrXchrX, binsize=100000, step=3) show(E14.bin) ## Move to a lower resolution map E14.bin2 <- binningC(E14.bin, binsize=500000, step=1) show(E14.bin2)data(Nora_5C) ## Data binning 100kb, with a 1/3 overlap E14.bin <- binningC(E14$chrXchrX, binsize=100000, step=3) show(E14.bin) ## Move to a lower resolution map E14.bin2 <- binningC(E14.bin, binsize=500000, step=1) show(E14.bin2)
Quality Control for high-throughput 'C' experiment
CQC(x, cis.trans.ratio = TRUE, hist.interac=TRUE, scat.interac.dist=TRUE, hist.dist=TRUE, trim.range=0.98, winsize=NA, dev.new=TRUE)CQC(x, cis.trans.ratio = TRUE, hist.interac=TRUE, scat.interac.dist=TRUE, hist.dist=TRUE, trim.range=0.98, winsize=NA, dev.new=TRUE)
x |
object that inherits from class
|
cis.trans.ratio |
logical; barplot of percentage of inter-intrachromsomal interactions |
hist.interac |
logical; histogram of the interaction frequency |
scat.interac.dist |
logical; scatter plot of interaction count versus the genomic distance between two elements |
hist.dist |
logical; histogram of the distance between the 'x' and 'y' intervals |
trim.range |
remove the extreme values by trimming the counts. Only use for plotting functions. [0,1] |
winsize |
used for the scat.interac.dist. If specify, the data are windowed before plotting |
dev.new |
if true, each graphs is plotted in a new device |
If x is a HTClist object, all HTCexp objects are merged.
The zero values are not used to compute the descriptive statistics and to display the data.
If trim.range are lower than 1. The highest values (quantile probability is equal to trim.range) are discarded.
Create quality plots and return a matrix with some simple statistics on all, cis and trans data.
N. Servant, B. Lajoie
data(Nora_5C) ## Quality Control CQC(E14)data(Nora_5C) ## Quality Control CQC(E14)
Calculate the directionality index as proposed by Dixon et al. 2012
directionalityIndex(x, winup = 2e+06, windown = 2e+06)directionalityIndex(x, winup = 2e+06, windown = 2e+06)
x |
|
winup |
size of upstream window |
windown |
size of downstrean window |
Calculate the directionality index as proposed by Dixon et al. This index is then used to call topological domains in Hi-C/5C data.
A numeric vector
N. Servant
require(HiCDataHumanIMR90) data(Dixon2012_IMR90) hox <- extractRegion(hic_imr90_40$chr6chr6, chr="chr6", from=50e6, to=58e6) di<-directionalityIndex(hox)require(HiCDataHumanIMR90) data(Dixon2012_IMR90) hox <- extractRegion(hic_imr90_40$chr6chr6, chr="chr6", from=50e6, to=58e6) di<-directionalityIndex(hox)
Transform matrix of counts data into discrete matrix
discretize(x, nb.lev=4, quant=TRUE)discretize(x, nb.lev=4, quant=TRUE)
x |
data matrix |
nb.lev |
number of discretization level |
quant |
logical; use quantile distribution or split data into equals 'nb.lev' levels |
A discrete matrix
N. Servant
quantile
## Not run: data(Nora_5C) ## Data binning E14bin<-binningC(E14$chrXchrX) ## Discretize matrix dismat<-discretize(intdata(E14bin)) mapC(dismat) ## End(Not run)## Not run: data(Nora_5C) ## Data binning E14bin<-binningC(E14$chrXchrX) ## Discretize matrix dismat<-discretize(intdata(E14bin)) mapC(dismat) ## End(Not run)
HTCexp object to my5C website formatExport HTCexp object to my5C website format
export.my5C(x, file, genome="mm9", per.chromosome=FALSE)export.my5C(x, file, genome="mm9", per.chromosome=FALSE)
x |
object that inherits from class |
file |
character; the prefix of the output file |
genome |
The genome version. This information is only used for the 'mat' export format. See details |
per.chromosome |
logical; export each contact maps in a different file (i.e one per chromosome pair) |
A tab-delimited matrix file is generated with the row and colnames
defined as follow as in the my5C web tool :
REV_2|mm9|chrX:98831149-98834145
N. Servant
## Not run: data(Nora_5C) ## Data binning E14.bin<-binningC(E14$chrXchrX) ## Export the new intervals definition export.my5C(E14.bin, file="E14my5C") ## End(Not run)## Not run: data(Nora_5C) ## Data binning E14.bin<-binningC(E14$chrXchrX) ## Export the new intervals definition export.my5C(E14.bin, file="E14my5C") ## End(Not run)
HTCexp objectExport HTCexp object to tab format
exportC(x, file, per.chromosome=FALSE, use.names=FALSE, header=FALSE)exportC(x, file, per.chromosome=FALSE, use.names=FALSE, header=FALSE)
x |
object that inherits from class |
file |
character; the basename of the output file |
per.chromosome |
logical; export each contact maps in a different files (i.e one per chromosome pair) |
use.names |
if TRUE, keep the original row/colnames of the contact matrix |
header |
if TRUE, add an header with the package version and the date |
Three output files will be created ; 2 BED files for each genomic
intervals, and one tab file.
The standard format for 5C/Hi-C data is the following :
** One list file (tab delimited)
bin1 bin2 x12
bin1 bin3 x13
...
** The BED file(s) describing the intervals ('xgi.bed' and 'ygi.bed'
are usually the same for Hi-C but can be different for 5C data)
chr1 1 1000000 bin1
chr1 1000001 2000000 bin2
...
Note that this format is particularly interesting for sparse
data as only non null values are stored.
If per.chromosome=FALSE, the data will be exported in one genome scaled file.
N. Servant
## Not run: data(Nora_5C) ## Data binning E14.bin<-binningC(E14$chrXchrX) ## Export the new intervals definition exportC(E14.bin, file="E14") ## End(Not run)## Not run: data(Nora_5C) ## Data binning E14.bin<-binningC(E14$chrXchrX) ## Export the new intervals definition exportC(E14.bin, file="E14") ## End(Not run)
Extract a subset of the HTCexp object based on genomic ranges
extractRegion(x, MARGIN, chr, from, to, exact=FALSE)extractRegion(x, MARGIN, chr, from, to, exact=FALSE)
x |
object that inherits from class |
MARGIN |
a vector giving the subscripts which the function will be applied over as in 'apply' function. E.g., '1' for the 'x' intervals, and '2' for the 'y' intervals, 'c(1, 2)' indicates 'x' and 'y' intervals. |
chr |
character; the chromosome of the genomic region |
from |
numeric; start of the genomic region |
to |
numeric; end of the genomic region |
exact |
logical; exact genomic region |
By default, only the intervals fully included in the genomic ranges are returned. If exact is true, the overlapping intervals are also used, and forced to start/end at the specified position. If no intervals are overlapping, an interval with NA values is added.
A HTCexp object
N. Servant
data(Nora_5C) ## Focus on the genomic region chrX:98000000-100000000 E14sub<-extractRegion(E14$chrXchrX, c(1,2), chr="chrX", from=98000000, to=100000000) show(E14sub)data(Nora_5C) ## Focus on the genomic region chrX:98000000-100000000 E14sub<-extractRegion(E14$chrXchrX, c(1,2), chr="chrX", from=98000000, to=100000000) show(E14sub)
Performs the annotation of all restriction sites of a given genome (i.e. GC content, mappability, effective fragment length)
getAnnotatedRestrictionSites(resSite="AAGCTT", overhangs5=1, chromosomes=NULL, genomePack="BSgenome.Mmusculus.UCSC.mm9", mappability=NULL, wingc=200, winmap=500)getAnnotatedRestrictionSites(resSite="AAGCTT", overhangs5=1, chromosomes=NULL, genomePack="BSgenome.Mmusculus.UCSC.mm9", mappability=NULL, wingc=200, winmap=500)
resSite |
the sequence of the restriction site to look for. Default is HindIII restriction site |
overhangs5 |
5' overhangs on the DNA resulted from the cutting |
chromosomes |
vector of chromosome number to focus on. Default all the chromosomes for the specified genome. |
genomePack |
name of the genome package to access the DNA sequence |
wingc |
size of the window upstream and downstream the restriction site used to calculate the GC content |
mappability |
a |
winmap |
size of the window upstream and downstream the restriction site used to calculate the mappability |
This function automatically annotate all the restriction sites of a given chromosome. The mappability is optional but strongly advice for Hi-C contact map normalization. This information can be easily download from public ressources like ftp://hgdownload.cse.ucsc.edu/gbdb/mm9/bbi/.
Returns a GRanges object annotation data upstream (U) and
downstream (D) the restriction sites.
N. Servant
## Not run: ## Mappability data From http://hgdownload.cse.ucsc.edu/goldenPath/hg18/encodeDCC/wgEncodeMapability/ map_hg18<- import("wgEncodeCrgMapabilityAlign100mer.bw",format="BigWig") ## 1- Example of restriction sites annnotation cutSites <- getAnnotatedRestrictionSites(resSite="AAGCTT", overhangs5=1, chromosomes="chr1", genomePack="BSgenome.Hsapiens.UCSC.hg18", wingc=200, mappability=map_hg18, winmap=500) ## End(Not run)## Not run: ## Mappability data From http://hgdownload.cse.ucsc.edu/goldenPath/hg18/encodeDCC/wgEncodeMapability/ map_hg18<- import("wgEncodeCrgMapabilityAlign100mer.bw",format="BigWig") ## 1- Example of restriction sites annnotation cutSites <- getAnnotatedRestrictionSites(resSite="AAGCTT", overhangs5=1, chromosomes="chr1", genomePack="BSgenome.Hsapiens.UCSC.hg18", wingc=200, mappability=map_hg18, winmap=500) ## End(Not run)
The expected interaction is defined as the linear relationship between the interaction counts and the distance between two loci. See details for additional informations.
getExpectedCounts(x, method=c("mean","loess"), asList=FALSE, ...)getExpectedCounts(x, method=c("mean","loess"), asList=FALSE, ...)
x |
object that inherits from class |
method |
method used to estimate the expected counts based on the genomic distance. See details |
asList |
return the results as a list. Otherwise, return an
object of class |
... |
arguments for mean or loess method, see below |
The expected value is the interaction frequency between two loci that one would expect based on a sole dependency on the genomic proximity of these fragments in the linear genome. This can be estimated using two different methods, mean or loess.
The first method (default) is simply based on the mean counts of each diagonal. If logbin is false, the expected counts will be estimated for each bin of the contact maps. If logbin is true, the binsize will change according to the distance to the diagonal. Short (resp. long) distance will be estimated with smaller (larger) bins. This method works for all resolutions.
The second method is based on a Lowess regression model and works usually fine with low resolution data (250Kb to 1Mb). At higher resolution, the lowess regression might be difficult to fit. The lowess smoothing has two parameters : span and bin. The span corresponds to the fraction of the data used for smoothing. Instead of computing the local polynomial fitting at each data point, a window of size delta (bin parameter) is applied on the data and a linear interpolation is used to fill in the fitted values within the window. The default is 1% of the range of x. If delta=0 all but identical x values are estimated independently. The variance is then estimated using the same span and bin parameter, at each interpolation points. The points inside a window are weighted so that nearby points get the most weight (tricube weight function).
A list with the expected interaction map and the estimated variance
if true logarithm based bins are used. In practice, it means that the bin size will change as we move away from the diagonal
multiplicative factor between each bin. Use if logbin is true
fraction of low counts to filter before normalizing. default: 0.05.
fraction of the data used for smoothing at each x point.
interpolation parameter
logical, calculate the variance
logical, display lowess smoothing and variance estimation points
N. Servant, B. Lajoie
HTCexp-class,normPerExpected,
normPerExpected, lowess
data(Nora_5C) ## Estimate expected interaction from distance between intervals E14.exp<-getExpectedCounts(E14$chrXchrX, method="loess", stdev=TRUE, plot=FALSE) mapC(E14.exp)data(Nora_5C) ## Estimate expected interaction from distance between intervals E14.exp<-getExpectedCounts(E14$chrXchrX, method="loess", stdev=TRUE, plot=FALSE) mapC(E14.exp)
Generate pearson correlation map, usually used to call chromosomal compartments
getPearsonMap(x, normPerExpected=TRUE, center=TRUE, ...)getPearsonMap(x, normPerExpected=TRUE, center=TRUE, ...)
x |
object that inherits from class |
normPerExpected |
normalized by expected interaction using the loess calculation of distance dependency. see normPerExpected |
center |
default=true. center the observed/expected map before calculating the Pearson correlation |
... |
additional parameters passed to |
The function returns an HTCexp object with Pearson correlation
map. This is usually the first step of the Principal Component
Analysis (see pca.hic).
Centering the rows of the observed/expected matrix allows to avoid
bias to due to ranges of interaction counts.
If true, the correlation of small values should be as valuable as
correlation of large values
A HTCexp object
N. Servant, B. Lajoie, R. McCord
## Get Lieberman-Aiden Hi-C data exDir <- system.file("extdata", package="HiTC") l <- sapply(list.files(exDir, pattern=paste("HIC_gm06690_"), full.names=TRUE), import.my5C) hiC <- HTClist(l) ## get Pearson correlation map pm <- getPearsonMap(hiC$chr14chr14) mapC(HTClist(pm), maxrange=1, col.pos=c("black","red"), col.neg=c("black","blue"))## Get Lieberman-Aiden Hi-C data exDir <- system.file("extdata", package="HiTC") l <- sapply(list.files(exDir, pattern=paste("HIC_gm06690_"), full.names=TRUE), import.my5C) hiC <- HTClist(l) ## get Pearson correlation map pm <- getPearsonMap(hiC$chr14chr14) mapC(HTClist(pm), maxrange=1, col.pos=c("black","red"), col.neg=c("black","blue"))
Performs the detection of restriction sites on a given genome and convert this information as a list of restriction fragments.
getRestrictionFragmentsPerChromosome(resSite="AAGCTT", overhangs5=1, chromosomes=NULL, genomePack="BSgenome.Mmusculus.UCSC.mm9")getRestrictionFragmentsPerChromosome(resSite="AAGCTT", overhangs5=1, chromosomes=NULL, genomePack="BSgenome.Mmusculus.UCSC.mm9")
resSite |
the sequence of the restriction site to look for. Default is HindIII restriction site |
overhangs5 |
5' overhangs on the DNA resulted from the cutting |
chromosomes |
vector of chromosome number to focus on. Default all the chromosomes for the specified genome. |
genomePack |
name of the genome package to access the DNA sequence |
Returns a GRanges object with all restriction fragments for a
given genome/chromosome.
N. Servant
normLGF, setGenomicFeatures, getAnnotatedRestrictionSites
## Not run: ## Mappability data From http://hgdownload.cse.ucsc.edu/goldenPath/hg18/encodeDCC/wgEncodeMapability/ map_hg18<- import("wgEncodeCrgMapabilityAlign100mer.bw",format="BigWig") ## 1- Get the list of restriction fragments for Human hg18 after HindIII digestion resFrag <- getRestrictionFragmentsPerChromosome(resSite="AAGCTT", overhangs5=1, chromosomes="chrX", genomePack="BSgenome.Hsapiens.UCSC.hg18") ## End(Not run)## Not run: ## Mappability data From http://hgdownload.cse.ucsc.edu/goldenPath/hg18/encodeDCC/wgEncodeMapability/ map_hg18<- import("wgEncodeCrgMapabilityAlign100mer.bw",format="BigWig") ## 1- Get the list of restriction fragments for Human hg18 after HindIII digestion resFrag <- getRestrictionFragmentsPerChromosome(resSite="AAGCTT", overhangs5=1, chromosomes="chrX", genomePack="BSgenome.Hsapiens.UCSC.hg18") ## End(Not run)
A class for representing high throughput Chromosome Conformation Capture data from next-generation sequencing experiments.
The normPerExpected method estimates the expected interactions based on a the dependency on the genomic
proximity between two loci. See getExpectedCounts function for details.
The normPerTrans method is based on the assumption that all trans contacts should be the same. Thus, the cis contacts can be normalized by the interaction level of trans data.
The xtrans trans map has to share its 'xgi' ranges with the
cis map, and the ytrans has to share its 'ygi' ranges with
the cismap.
The method is used to combine the normalization factor from x
and y ranges. Must be ‘sum’, ‘mult’ or ‘mean’.
Objects can be created either by:
calls of the form
new("HTCexp", intdata, GRanges, GRanges).
using the auxiliary function HTCexp and supplying
contact Matrix with x and y intervals definition.
The forceSymmetric option can used to force intra-chromosomal
contact maps to be stored as symmetrical matrix.
intdata:Dense or Sparse Matrix, holding the interaction level between each pairs of 'x-y' intervals. The 'y' intervals must be in rows, and the 'x' in columns.
ygi:Genomic ranges of y intervals;
see class granges for details
xgi:Genomic ranges of x intervals;
see class granges for details
Combines 'x' and the signature("HTCexp")
objects in '...' together. The results is an object of class signature("HTCList")
signature("HTCexp"): a more
detailed output of the experiment than provided by show.
comparison of two signature("HTCexp")
objects. Perform the division of the two contact matrices on the
common 'x' and 'y' intervals. The operation is done only on the
common intervals of both objects. If one of the two objects has a count
to zero, the divided value will be NA
return the intdata Matrix counts. Note that
triangular matrices are always returned as symmetric matrices.
Defunct. See exportC method
return TRUE if the data are binned. The method tests if the 'x' and 'y' genome intervals are the same, if 90% of the bins have the same size and if the full genomic range is covered
force the interaction data to 'symmetricMatrix'
force the interaction data to triangular, ie. symmetric. Lower triangle of the matrix is set to zero, therefore reducing the size of the data in memory
return TRUE if the current
signature("HTCexp") object contains intrachromosomal contact data
return TRUE if the contact map is
symmetrical, i.e inherits the symmetricMatrix class
normalize the contact matrix by the total number of reads of the matrix.
normalize the contact matrix by the expected number of reads based on the distance between two loci. See details.
Defunct. See normPerExpected method
Normalize cis contact map based on the trans interactions. See details
visualization method; Display an heatmap of the
contact data. Refer to the documentation of
mapC for more details of the plotting function
return the genomic range of the
signature("HTCexp") object
Defunct. See seqlevels method
return the sequence levels of the signature("HTCexp") object
summarized output of the experiment, with informations about the data dimension and the genomic region studied
comparison of two signature("HTCexp")
objects. Perform the substraction of the two contact matrices on the
common 'x' and 'y' intervals. The operation is done only on the
common intervals of both objects. If one of the two objects has a count
to zero, the divided value will be NA
return descriptive summary statistics about the contact map
return the xgi GRanges object defining the
x intervals
return the ygi GRanges object defining the
y intervals
return both xgi and ygi objects as a
GRangesList object
Nicolas Servant
GRanges-class,GRangesList-class,Matrix-class
data(Nora_5C) ## HTCexp descriptio show(E14) detail(E14) ## Is binned data ? isBinned(E14$chrXchrX) ## Is a inter or intrachromsomal experiment ? isIntraChrom(E14$chrXchrX) ## Bin the data E14.bin <- binningC(E14$chrXchrX, binsize=100000, step=3) ## Divide by expected interaction counts E14norm<-normPerExpected(E14.bin) ## Operation on HTCexp object E14_d_MEF<-divide(normPerReads(E14$chrXchrX), normPerReads(MEF$chrXchrX)) E14_s_MEF<-substract(normPerReads(E14$chrXchrX), normPerReads(MEF$chrXchrX)) ## Overlap with genomic annotation require(rtracklayer) gene <- import(file.path(system.file("extdata", package="HiTC"),"refseq_mm9_chrX_98831149_103425150.bed"), format="bed") plot(E14$chrXchrX, tracks=list(RefSeqGene=gene)) ## Not run: ## normPerTrans data normalization applied on \href{http://genome.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeUmassDekker5C}{ENCODE data}. ENCODE=import.my5C("./ENM-GM12878-R1.matrix") ## Look at raw contact map mapC(ENCODE$chr7chr7) ## look at normalize by trans contact map mapC(normPerTrans(ENCODE$chr7chr7, xtrans=ENCODE$chr7chr5, ytrans=ENCODE$chr5chr7)) ## End(Not run) ## Not run: ## Export exportC(E14$chrXchrX, con="E14.csv") ## End(Not run)data(Nora_5C) ## HTCexp descriptio show(E14) detail(E14) ## Is binned data ? isBinned(E14$chrXchrX) ## Is a inter or intrachromsomal experiment ? isIntraChrom(E14$chrXchrX) ## Bin the data E14.bin <- binningC(E14$chrXchrX, binsize=100000, step=3) ## Divide by expected interaction counts E14norm<-normPerExpected(E14.bin) ## Operation on HTCexp object E14_d_MEF<-divide(normPerReads(E14$chrXchrX), normPerReads(MEF$chrXchrX)) E14_s_MEF<-substract(normPerReads(E14$chrXchrX), normPerReads(MEF$chrXchrX)) ## Overlap with genomic annotation require(rtracklayer) gene <- import(file.path(system.file("extdata", package="HiTC"),"refseq_mm9_chrX_98831149_103425150.bed"), format="bed") plot(E14$chrXchrX, tracks=list(RefSeqGene=gene)) ## Not run: ## normPerTrans data normalization applied on \href{http://genome.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeUmassDekker5C}{ENCODE data}. ENCODE=import.my5C("./ENM-GM12878-R1.matrix") ## Look at raw contact map mapC(ENCODE$chr7chr7) ## look at normalize by trans contact map mapC(normPerTrans(ENCODE$chr7chr7, xtrans=ENCODE$chr7chr5, ytrans=ENCODE$chr5chr7)) ## End(Not run) ## Not run: ## Export exportC(E14$chrXchrX, con="E14.csv") ## End(Not run)
A class for representing a list of high throughput Chromosome Conformation Capture data from next-generation sequencing experiments.
A signature("HTClist") is composed of a list of contact maps,
representing the chromosomal interactions between pair of
chromosomes.
The expected number of maps for a complete
signature("HTClist") object should be equal to
'lchrs+(lchrs*(lchrs-1)/2)'. In this case, the chr1-chr2 map is stored
once, but the dataset is complete.
If a signature("HTClist") object is composed of all pairwise
interaction maps, it means that the chr1-chr2 AND the chr2-chr1 maps
will be stored. This way of storing the data is less memory efficient
but can ease the use of some genome-wide algorithm.
Note that the getCombinedContact method should be used
carefully. This method merges all single contact maps in one
genome-wide map, therefore creating a very big matrix requiring memory
space. However, this method is useful for many genome-wide analysis.
The normPerExpected method applied to HTClist object is only
available with the 'mean' method (see getExpectedCounts for
details). In this case, the mean counts per distance are calculated
over all intra-chromosomal maps.
The HTClist represents a list of HTCexp objects and can
be created as follow :
HTClist(...) : Creates a HTClist object using HTCexp objects supplied in '...'
Combines a signature("HTClist") object 'x'
with signature("HTClist") or signature("HTCexp")
objects in '...'. The results is an object of class signature("HTCList")
signature("HTClist"): a more
detailed output of the experiment than provided by show.
return a signature("HTClist") with
all the pairwise contact maps
return a signature("HTClist") with
half of the pairwise contact maps
Logical; true if 'x' contains all intra and interchromosomal maps
Logical; true if 'x' contains all interchromosomal pairs, i.e. chr1chr2 and chr2chr1
applies 'isBinned' to each element in 'x'
applies 'isIntraChrom' to each element in 'x'
merge all contact maps in a single big matrix
merge all x and y intervals in single GRangesList object, or in a single GRanges object if merge=TRUE
normalized by genomic distance all intra-chromosomal maps. See details
applies 'range' to each element in 'x'
return the reduce range of all elements in 'x'
reduce a HTClist object to the list of provided chromosomes. Intra/Interchromosomal maps are returned accoring to the cis and trans args. If extend = True, all maps involving one of the chromosomes are returned
return the sequence levels of all elements in 'x'
coercion to simple list object
get the names of the elements
summarized output of the experiment, with informations about the data dimension
return descriptive summary statistics for each interaction map
Get elements i from x. Can be the
positional index or its name.
Nicolas Servant
GRangesList-class, HTCexp-class
exDir <- system.file("extdata", package="HiTC") l <- sapply(list.files(exDir, pattern=paste("HIC_gm06690_"), full.names=TRUE), import.my5C) hiC <- HTClist(l) names(hiC) ## Methods ranges(hiC) range(hiC) isComplete(hiC) isPairwise(hiC) isBinned(hiC) isIntraChrom(hiC) seqlevels(hiC)exDir <- system.file("extdata", package="HiTC") l <- sapply(list.files(exDir, pattern=paste("HIC_gm06690_"), full.names=TRUE), import.my5C) hiC <- HTClist(l) names(hiC) ## Methods ranges(hiC) range(hiC) isComplete(hiC) isPairwise(hiC) isBinned(hiC) isIntraChrom(hiC) seqlevels(hiC)
Import data from my5C webtool
import.my5C(file, allPairwise=FALSE, rm.trans=FALSE, lazyload=FALSE)import.my5C(file, allPairwise=FALSE, rm.trans=FALSE, lazyload=FALSE)
file |
input file from the my5C webtool |
allPairwise |
logical; generate all pairwise chromosomal contact maps, i.e chr1-chr2, chr2-chr1 |
rm.trans |
if true, only intra-chromosomal maps are loaded |
lazyload |
logical; force the intra-chromosomal contact maps to be stored as triangular matrix |
This function allows data import from the the my5C webtool.
The matrix format is a tab-delimited format, corresponding to the
contact map. The rownames and columnames are splitted to created
the genome intervals (example : REV_2|mm9|chrX:98831149-98834145).
The allPairwise option is not necessary in case of symetric design. Otherwise, it will return all the pairwise contact maps.
The matrix will be stored as a matrix inheriting from Matrix
class. If forcesymmetrical=TRUE, the intrachromosomal matrix as coerced to
symmetricMatrix class allowing a much more efficient memory usage.
A HTClist object
N. Servant
HTClist-class,HTCexp-class,importC,
Matrix-class, symmetricMatrix-class
exDir <- system.file("extdata", package="HiTC") ## Load my5C matrix format hiC<-import.my5C(file.path(exDir,"HIC_gm06690_chr14_chr14_1000000_obs.txt")) detail(hiC)exDir <- system.file("extdata", package="HiTC") ## Load my5C matrix format hiC<-import.my5C(file.path(exDir,"HIC_gm06690_chr14_chr14_1000000_obs.txt")) detail(hiC)
Import 5C or Hi-C data from list file
importC(con, xgi.bed, ygi.bed=NULL, allPairwise=FALSE, rm.trans=FALSE, lazyload=FALSE)importC(con, xgi.bed, ygi.bed=NULL, allPairwise=FALSE, rm.trans=FALSE, lazyload=FALSE)
con |
input csv file. See details |
xgi.bed |
BED file describing the 'x' Intervals (i.e. column names) of the contact map |
ygi.bed |
BED file describing the 'y' intervals (i.e. row names) of the contact map |
allPairwise |
logical; generate all pairwise chromosomal contact maps, i.e chr1-chr2, chr2-chr1 |
rm.trans |
if true, returns only intra-chromosomal maps |
lazyload |
logical; see details |
This function import high-throughput data from a tab list file.
The standard format for 5C/Hi-C data is the following :
** One list file (tab delimited)
bin1 bin2 x12
bin1 bin3 x13
...
** The BED file(s) describing the intervals ('xgi.bed' and 'ygi.bed'
are usually the same for Hi-C but can be different for 5C data)
chr1 1 1000000 bin1
chr1 1000001 2000000 bin2
...
Note that this format is particularly interesting for sparse data as only non null values are stored.
The lazyload option allow to reduce the memory size of imported object. Therefore, only half of inter-chromosomal maps are stored. And intra-chromosomal maps are stored as sparse triangular matrix. Note that even if the contact maps are stored as triangular matrix, the indata method always returns a symmetrical map.
A HTClist object
N. Servant
exportC,import.my5C, HTCexp-class
## Not run: data(Nora_5C) ## Data binning E14.bin<-binningC(E14$chrXchrX) ## Export the new intervals definition exportC(E14.bin, file="E14") ##Import importC(con="E14.count", xgi="E14_xgi.bed", ygi="E14_ygi.bed") ## End(Not run)## Not run: data(Nora_5C) ## Data binning E14.bin<-binningC(E14$chrXchrX) ## Export the new intervals definition exportC(E14.bin, file="E14") ##Import importC(con="E14.count", xgi="E14_xgi.bed", ygi="E14_ygi.bed") ## End(Not run)
Compute the distance of intrachromosomal contacts of a 'C' experiment
intervalsDist(x, use.zero=TRUE)intervalsDist(x, use.zero=TRUE)
x |
object that inherits from class |
use.zero |
if FALSE, the distance for non interacting regions (zero counts) are not reported |
If and are the two sets of intervals and
and , the start and end of an interval, the distance is calculated as :
Only intrachromsomal contact maps can be use for this operation.
A matrix of distances between genomic intervals
N. Servant
data(Nora_5C) ## Calculate distances between primers/intervals d<-intervalsDist(E14$chrXchrX)data(Nora_5C) ## Calculate distances between primers/intervals d<-intervalsDist(E14$chrXchrX)
Visualize 'C' contact map
This function implements the plot method for objects
of class HTCexp and HTClist.
By default, the trim.range value is fixed so that the 98th percentile (resp. 2th percentile) of each interaction matrix is discarded. It therefore allow to remove the extreme values from the matrix, but each map is plotted independently. If the maxrange argument is set, data higher that this threshold will be fixed to the maxrange value for all maps. In addition, color ranges are ajusted in a way that all maps are plotted within the same color range allowing visual maps comparison.
The HTCexp and HTClist are not represented in the same
way. The heatmap view is used to display the HTClist objects in
two dimension. This view is mainly useful to have an overview of the
data, as Hi-C data.
The triangle view is used for HTCexp only and represent
the top-right part the interaction matrix. If two HTCexp
objects are specified, they will be displayed in order to compare both
contact maps. The two maps have to be binned to ensure comparison
between genomic ranges.
Annotation tracks can be added to both views. In case of binned data, the exact genomic positions of each features are takken into account. Otherwise, the 'C' intervals which overlap with the annotation features are colored.
Returns NULL; this function is called for the side-effect of
creating the plot.
HTCexp and HTClist objectsobject that inherits from class HTCexp or HTClist
List of GRanges objects of data to display as annotation track(s)
the minimum range of values used to define the color palette
the maximum range of values used to define the color palette
define the maxrange and minrange values using the percentile of the interaction matrix
logical; plot the zero values
logical; show the NA values in gray
logical; do you want to log the data before plotting the heatmap
color for (low,mid,high) positive contact counts. Must be a vectore of size 3. mid can be NA
color for (low,mid,high) negative contact counts. Must be a vectore of size 3. mid can be NA
color for NA values
logical; add a grid on the heatmap
character; add a title to the HTCexp plot(s)
logical; display the contact values on the matrix. Useful for small matrices
HTCexp objects onlyoptional. object that inherits from class HTCexp.
HTClist objects onlylogical; display the names of the intervals. Useful for small matrices
N. Servant, B. Lajoie
data(Nora_5C) ## Contact map ## HTClist view mapC(E14) ## HTCexp view mapC(E14$chrXchrX) ## Play with contrast and color mapC(E14$chrXchrX, maxrange=100, col.pos=c("black","red","yellow")) ## Add annotation and change view require(rtracklayer) exDir <- system.file("extdata", package="HiTC") gene <- import(file.path(exDir,"refseq_mm9_chrX_98831149_103425150.bed"), format="bed") mapC(E14$chrXchrX, tracks=list(Refseq=gene)) ## Compare two samples mapC(binningC(E14$chrXchrX), binningC(MEF$chrXchrX), tracks=list(Refseq=gene))data(Nora_5C) ## Contact map ## HTClist view mapC(E14) ## HTCexp view mapC(E14$chrXchrX) ## Play with contrast and color mapC(E14$chrXchrX, maxrange=100, col.pos=c("black","red","yellow")) ## Add annotation and change view require(rtracklayer) exDir <- system.file("extdata", package="HiTC") gene <- import(file.path(exDir,"refseq_mm9_chrX_98831149_103425150.bed"), format="bed") mapC(E14$chrXchrX, tracks=list(Refseq=gene)) ## Compare two samples mapC(binningC(E14$chrXchrX), binningC(MEF$chrXchrX), tracks=list(Refseq=gene))
5C data described by Nora et al. (2012)
data(Nora_5C)data(Nora_5C)
Contains two HTClist objects (E14 and MEF). Each of them
containing the ChrX intrachromosomal maps as a HTCexp object.
This 5C dataset published by Nora et al
(GSE35721),
contains two different samples, a male undifferentiated ES cells (E14,
GSM873935) and a mouse embryonic fibroblasts (MEF, GSM873924). This
dataset is mainly used to describe the available functionalities of the
HiTC package.
The data provided with the package are count data.
http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE35721
Nora EP, Lajoie BR, Schulz EG, Giorgetti L et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 2012 Apr 11;485(7398):381-5. PMID: 22495304
data(Nora_5C) show(E14) show(MEF)data(Nora_5C) show(E14) show(MEF)
Iterative correction leverages the unique pairwise genome-wide structure of Hi-C data to decompose the data into a set of biases and a map of relative contact probabilities between any two genomic loci, achieving equal visibility across all genomic regions.
normICE(x, max_iter=50, eps=1e-4, sparse.filter=0.02)normICE(x, max_iter=50, eps=1e-4, sparse.filter=0.02)
x |
object that inherits from class |
max_iter |
maximum number of iteration |
eps |
the relative increment in the results before declaring convergence |
sparse.filter |
Define which pourcentage of bins with high sparsity should be force to zero |
The normalization of Hi-C data is based on matrix balancing algorithm which consists of iteratively estimating the matrix bias using the l1 norm. The method implemented here is the Sinkhorn-Knopp algorithm as used in the Imakaev et al. paper. Note that the original method is applied on the genome-wide Hi-C map, but that the method could be applied on intra-chromosomal maps at high resolution.
Returns a HTCexp object with a corrected contact map.
N. Servant, N. Varoqaux
Imakaev M, Fudenberg G, McCord RP, Naumova N, Goloborodko A, Lajoie BR, Dekker J, Mirny LA. Iterative correction of Hi-C data reveals hallmarks of chromosome organization.Nat Methods. 2012 Oct;9(10):999-1003.
## Not run: ##Lieberman data exDir <- system.file("extdata", package="HiTC") l <- sapply(list.files(exDir, pattern=paste("HIC_gm06690_"), full.names=TRUE), import.my5C) hiC <- HTClist(l) hiC <- hiC[isIntraChrom(hiC)] ## Run ICE hiC_iced <- HTClist(lapply(hiC, normICE)) ## End(Not run)## Not run: ##Lieberman data exDir <- system.file("extdata", package="HiTC") l <- sapply(list.files(exDir, pattern=paste("HIC_gm06690_"), full.names=TRUE), import.my5C) hiC <- HTClist(l) hiC <- hiC[isIntraChrom(hiC)] ## Run ICE hiC_iced <- HTClist(lapply(hiC, normICE)) ## End(Not run)
Parametric model to remove systematic biases in the raw contact maps
normLGF(x, family=c("poisson", "nb"))normLGF(x, family=c("poisson", "nb"))
x |
object that inherits from class |
family |
parametric model to fit (poisson or nb) |
This function implements the HiCNorm method proposed by Hu et al. Briefly, the method uses a generalized linear model to correct the systematic biases (effective fragment length, GC content, mappability) in a Hi-C contact map.
Returns a HTCexp object with a normalized contact map.
N. Servant, M. Hu, S. Selvaraj
Hu M, Deng K, Selvaraj S, Qin Z, Ren B, Liu JS. HiCNorm: removing biases in Hi-C data via Poisson regression. Bioinformatics. 2012;28(23):3131-3.
getAnnotatedRestrictionSites, setGenomicFeatures
## Not run: require(HiTC) require(BSgenome.Hsapiens.UCSC.hg18) ##Lieberman data exDir <- system.file("extdata", package="HiTC") l <- sapply(list.files(exDir, pattern=paste("HIC_gm06690_"), full.names=TRUE), import.my5C) hiC <- HTClist(l) hiC <- hiC[isIntraChrom(hiC)] names(hiC) ## Mappability data From http://hgdownload.cse.ucsc.edu/goldenPath/hg18/encodeDCC/wgEncodeMapability/ map_hg18<- import("wgEncodeCrgMapabilityAlign100mer.bw", format="BigWig") ## Get the genomic feature of the chromosome 12 hiC_annot <- HTClist(lapply(hiC, setGenomicFeatures, resSite="AAGCTT", overhangs5=1, genomePack="BSgenome.Hsapiens.UCSC.hg18", wingc=200, mappability=map_hg18, winmap=500)) hiC_annot$chr12chr12 ## Normalize the data hiCnorm <- HTClist(lapply(hiC_annot, normLGF)) ## End(Not run)## Not run: require(HiTC) require(BSgenome.Hsapiens.UCSC.hg18) ##Lieberman data exDir <- system.file("extdata", package="HiTC") l <- sapply(list.files(exDir, pattern=paste("HIC_gm06690_"), full.names=TRUE), import.my5C) hiC <- HTClist(l) hiC <- hiC[isIntraChrom(hiC)] names(hiC) ## Mappability data From http://hgdownload.cse.ucsc.edu/goldenPath/hg18/encodeDCC/wgEncodeMapability/ map_hg18<- import("wgEncodeCrgMapabilityAlign100mer.bw", format="BigWig") ## Get the genomic feature of the chromosome 12 hiC_annot <- HTClist(lapply(hiC, setGenomicFeatures, resSite="AAGCTT", overhangs5=1, genomePack="BSgenome.Hsapiens.UCSC.hg18", wingc=200, mappability=map_hg18, winmap=500)) hiC_annot$chr12chr12 ## Normalize the data hiCnorm <- HTClist(lapply(hiC_annot, normLGF)) ## End(Not run)
Perform Principle Component Analysis on Hi-C contact map
pca.hic(x, normPerExpected=TRUE, npc=2, asGRangesList=TRUE, gene.gr=NULL, ...)pca.hic(x, normPerExpected=TRUE, npc=2, asGRangesList=TRUE, gene.gr=NULL, ...)
x |
object that inherits from class |
normPerExpected |
normalized by expected interaction using the loess calculation of distance dependency. see normPerExpected |
npc |
numeric; number of first principal component to return |
asGRangesList |
if TRUE a GRangesList object is returned where the scores represent the eigenvector |
gene.gr |
object of class |
... |
additional parameters passed to |
This method was apply by Lieberman-Aiden et al. 2009 to correlate the annotation profiles (genes, ChIP-seq, etc.) with the topological domains observed in Hi-C (see Fig3G of Lieberman-Aiden et al. 2009)
A list with the eigen vector(s) of the npc first principal
component(s), and their importance
N. Servant, B. Lajoie, R. McCord
## Get Lieberman-Aiden Hi-C data exDir <- system.file("extdata", package="HiTC") l <- sapply(list.files(exDir, pattern=paste("HIC_gm06690_"), full.names=TRUE), import.my5C) hiC <- HTClist(l) ## Performed PCA pr<-pca.hic(hiC$chr14chr14, npc=1, asGRangesList=TRUE)## Get Lieberman-Aiden Hi-C data exDir <- system.file("extdata", package="HiTC") l <- sapply(list.files(exDir, pattern=paste("HIC_gm06690_"), full.names=TRUE), import.my5C) hiC <- HTClist(l) ## Performed PCA pr<-pca.hic(hiC$chr14chr14, npc=1, asGRangesList=TRUE)
Remove primers intervals from HTCexp object
removeIntervals(x, ids)removeIntervals(x, ids)
x |
object that inherits from class |
ids |
character; vector of primers Ids to remove from the object |
A HTCexp object without the discarded intervals
N. Servant
data(Nora_5C) ## Remove intervals from a HTCexp object removeIntervals(E14$chrXchrX, ids=c("5C_938_XIC-3_REV_2", "5C_938_XIC-3_REV_4"))data(Nora_5C) ## Remove intervals from a HTCexp object removeIntervals(E14$chrXchrX, ids=c("5C_938_XIC-3_REV_2", "5C_938_XIC-3_REV_4"))
Annotate a Hi-C contact map with the genomic local features (i.e. GC content, mappability, effective fragment length)
setGenomicFeatures(x, cutSites, minFragMap=.5, effFragLen=1000)setGenomicFeatures(x, cutSites, minFragMap=.5, effFragLen=1000)
x |
|
cutSites |
|
minFragMap |
Minimum Fragment Mappbility. All fragments with a lower mappability are not used for the annotation. |
effFragLen |
Efective Fragment Length. Size of specific fragment ligation |
The function require the restriction sites annotation as provided by
the getAnnotatedRestrictionSites function.
The restriction sites are first filtered according to their
mappability. This threshold has to be defined according to the data
pre-processing. All remaining restriction sites are then intersected
with the genomic bins of the contact map. All restriction sites
included within a bin are averaged.
The effective fragment length is defined as the size of specific
ligation product. (See Yaffe and Tanay, 2011). In this paper, the
authors define specific ligation as sum of distance to cutter sites
(d1+d2) <= 500 bp. Such criterion implies that d1<=500 bp and d2 <=
500 bp. So for each fragment end, only reads mapped within 500 bp to
cutter sites are used for downstream analysis.
All defults paramters correspond to the ones used in the HiCNorm method.
Returns a HTCexp object with local genomic features annotations.
N. Servant
## Not run: require(BSgenome.Hsapiens.UCSC.hg18) require(rtracklayer) ##Lieberman data exDir <- system.file("extdata", package="HiTC") l <- sapply(list.files(exDir, pattern=paste("HIC_gm06690_"), full.names=TRUE), import.my5C) hiC <- HTClist(l) hiC <- hiC[isIntraChrom(hiC)] names(hiC) ## Mappability data From http://hgdownload.cse.ucsc.edu/goldenPath/hg18/encodeDCC/wgEncodeMapability/ map_hg18<- import("wgEncodeCrgMapabilityAlign100mer.bw", format="BigWig") ## Get the genomic feature of the HiC chr12 data cutSites <- getAnnotatedRestrictionSites(resSite="AAGCTT", overhangs5=1, chromosomes=seqlevels(hiC), genomePack="BSgenome.Hsapiens.UCSC.hg18", wingc=200, mappability=map_hg18, winmap=500) chr12_annot <- setGenomicFeatures(hiC$chr12chr12, cutSites) ## End(Not run)## Not run: require(BSgenome.Hsapiens.UCSC.hg18) require(rtracklayer) ##Lieberman data exDir <- system.file("extdata", package="HiTC") l <- sapply(list.files(exDir, pattern=paste("HIC_gm06690_"), full.names=TRUE), import.my5C) hiC <- HTClist(l) hiC <- hiC[isIntraChrom(hiC)] names(hiC) ## Mappability data From http://hgdownload.cse.ucsc.edu/goldenPath/hg18/encodeDCC/wgEncodeMapability/ map_hg18<- import("wgEncodeCrgMapabilityAlign100mer.bw", format="BigWig") ## Get the genomic feature of the HiC chr12 data cutSites <- getAnnotatedRestrictionSites(resSite="AAGCTT", overhangs5=1, chromosomes=seqlevels(hiC), genomePack="BSgenome.Hsapiens.UCSC.hg18", wingc=200, mappability=map_hg18, winmap=500) chr12_annot <- setGenomicFeatures(hiC$chr12chr12, cutSites) ## End(Not run)
Set x and y interval of the HTCexp object and update the contact map accordingly
setIntervalScale(x, xgi, ygi, upa=TRUE, method=c("sum","median","mean"), use.zero=TRUE, optimize.by = c("speed", "memory"))setIntervalScale(x, xgi, ygi, upa=TRUE, method=c("sum","median","mean"), use.zero=TRUE, optimize.by = c("speed", "memory"))
x |
object that inherits from class |
ygi |
y intervals;
see class |
xgi |
x intervals;
see class |
upa |
logical; unique primer assignment. Allow one primer to belong to one or several bins |
method |
the method used to combine the counts. Must be ‘mean’, ‘median’ or ‘sum’ |
use.zero |
logical; use the zero values in the |
optimize.by |
"speed" will use faster methods but more RAM, and "memory" will be slower, but require less RAM |
Define new contact map based on the specified xgi and ygi intervals.
This function has to be used carefully and can has important impact on
the contact map.
It is important to note that the setIntervalScale function is different from the binningC function in the way that the output
is not symetrical.
A HTCexp object
N. Servant
data(Nora_5C) E14.bin<-binningC(E14$chrXchrX) ## I have two HTCexp samples defined with different intervals. show(E14.bin) show(MEF$chrXchrX) ## How to compare them ? ## One idea is to force the intervals definition of one object using the ## intervals of the other. setIntervalScale(MEF$chrXchrX, xgi=x_intervals(E14.bin), ygi=y_intervals(E14.bin))data(Nora_5C) E14.bin<-binningC(E14$chrXchrX) ## I have two HTCexp samples defined with different intervals. show(E14.bin) show(MEF$chrXchrX) ## How to compare them ? ## One idea is to force the intervals definition of one object using the ## intervals of the other. setIntervalScale(MEF$chrXchrX, xgi=x_intervals(E14.bin), ygi=y_intervals(E14.bin))