| Title: | Bandwise normalization and batch correction of Hi-C data |
|---|---|
| Description: | Tools to normalize (several) Hi-C data from replicates. |
| Authors: | Kipper Fletez-Brant [cre, aut], Kasper Daniel Hansen [aut] |
| Maintainer: | Kipper Fletez-Brant <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.29.0 |
| Built: | 2024-11-01 06:13:56 UTC |
| Source: | https://github.com/bioc/bnbc |
Tools to normalize (several) Hi-C data from replicates.
The DESCRIPTION file:
| Package: | bnbc |
| Version: | 1.29.0 |
| Title: | Bandwise normalization and batch correction of Hi-C data |
| Description: | Tools to normalize (several) Hi-C data from replicates. |
| Authors@R: | c( person("Kipper", "Fletez-Brant", role = c("cre", "aut"), email = "[email protected]"), person(c("Kasper", "Daniel"), "Hansen", role = "aut")) |
| Depends: | R (>= 3.5.0), methods, BiocGenerics, SummarizedExperiment, GenomicRanges |
| Suggests: | BiocStyle, knitr, rmarkdown, RUnit, BSgenome.Hsapiens.UCSC.hg19 |
| Imports: | Rcpp (>= 0.12.12), IRanges, rhdf5, data.table, GenomeInfoDb, S4Vectors, matrixStats, preprocessCore, sva, parallel, EBImage, utils, HiCBricks |
| LinkingTo: | Rcpp |
| VignetteBuilder: | knitr |
| License: | Artistic-2.0 |
| URL: | https://github.com/hansenlab/bnbc |
| BugReports: | https://github.com/hansenlab/bnbc/issues |
| biocViews: | HiC, Preprocessing, Normalization, Software |
| Repository: | https://bioc.r-universe.dev |
| RemoteUrl: | https://github.com/bioc/bnbc |
| RemoteRef: | HEAD |
| RemoteSha: | c54d07171615a665a5f0308201efc7fecc7aa8cc |
| Author: | Kipper Fletez-Brant [cre, aut], Kasper Daniel Hansen [aut] |
| Maintainer: | Kipper Fletez-Brant <[email protected]> |
Index of help topics:
ContactGroup-class Class '"ContactGroup"'
band Get Band
bnbc Normalize Contact Matrices with BNBC
bnbc-package Bandwise normalization and batch correction of
Hi-C data
boxSmoother Smoothing Operations
cgApply Apply-type methods
cgEx Sample chr22 Data
getBandIdx Get Band Indices
getBandMatrix Get Band Matrix
getGenomeIdx Methods for manipulating cooler files
getGroupZeros Group Zero Operations
The package implements the bnbc method for normalizing Hi-C data across
samples. The name is short for band-wise normalization and batch
correction. The main workhorse is the bnbc function. We recommend
using smoothing and library size normalization first.
The package implements the ContactGroup class for storing
multiple Hi-C contact matrices. This is most naturally done with one
object per chromosome, which is ugly.
We also have functions for applying over a ContactGroup
(cgApply) and working with matrix bands band, getBandIdx.
Kipper Fletez-Brant [cre, aut], Kasper Daniel Hansen [aut]
Maintainer: Kipper Fletez-Brant <[email protected]>
Fletez-Brant et al. Distance-dependent between-sample normalization for Hi-C experiments. In preparation.
bnbc, ContactGroup, band,
cgApply.
data(cgEx) batches <- colData(cgEx)$Batch cgEx.cpm <- logCPM(cgEx) cgEx.smooth <- boxSmoother(cgEx, 5, mc.cores=1) cgEx.bnbc <- bnbc(cgEx.smooth, batches, 1e7, 4e4, bstart=2, nbands=4)data(cgEx) batches <- colData(cgEx)$Batch cgEx.cpm <- logCPM(cgEx) cgEx.smooth <- boxSmoother(cgEx, 5, mc.cores=1) cgEx.bnbc <- bnbc(cgEx.smooth, batches, 1e7, 4e4, bstart=2, nbands=4)
Get or set band from matrix.
band(mat, band.no) band(mat, band.no) <- valueband(mat, band.no) band(mat, band.no) <- value
mat |
A matrix. |
band.no |
Integer specifying which matrix band. |
value |
A scalar or vector equal in length to the matrix band. |
A matrix band is the set of elements in a matrix from a specific off-diagonal.
A matrix band in the form of a vector.
mat <- matrix(1:9, 3, 3) band(mat, band.no = 2) mat band(mat, band.no = 2) <- c(9,10) mat data(cgEx) tact.1 <- contacts(cgEx)[[1]] b2 <- band(tact.1, 2) band(tact.1, 2) <- b2mat <- matrix(1:9, 3, 3) band(mat, band.no = 2) mat band(mat, band.no = 2) <- c(9,10) mat data(cgEx) tact.1 <- contacts(cgEx)[[1]] b2 <- band(tact.1, 2) band(tact.1, 2) <- b2
Applies BNBC method to normalize contact matrices.
bnbc(cg, batch, threshold = NULL, step = NULL, qn = TRUE, nbands = NULL, mod = NULL, mean.only = FALSE, tol = 5, bstart = 2, verbose = TRUE)bnbc(cg, batch, threshold = NULL, step = NULL, qn = TRUE, nbands = NULL, mod = NULL, mean.only = FALSE, tol = 5, bstart = 2, verbose = TRUE)
cg |
A ContactGroup object. |
batch |
A single batch indicator variable. |
threshold |
The maximum distance interacting loci are allowed to be separated by. |
step |
The step size, or the number of bases a contact matrix cell represents. |
qn |
Whether to apply quantile normalization on each band matrix. Defaults to TRUE. |
bstart |
The first band to normalize. Defaults to 2. |
nbands |
The last band to normalize. Defaults to |
mod |
A model matrix specifying which sample information is to be preserved by ComBat. Optional. |
mean.only |
Whether ComBat should not correct for batch effect in the variances of band matrix rows. Defaults to FALSE, which means variances are corrected. Set to TRUE if there is only one observation per batch. |
tol |
The number of significant digits for which the mean value of a band matrix must be greater than 0 to be processed by ComBat. |
verbose |
Should the function print progress? |
Normalization and batch correction is performed in a band-wise manner, correcting all samples' observations of one matrix off-diagonal (which we refer to as a matrix "band") at a time. For each matrix band, we collect all samples' observations into a single matrix. We then apply quantile normalization to ensure distributional similarity across samples. Finally, we perform batch effect correction using ComBat on this matrix. Each samples' matrix band is then replaced with its corrected version. We refer to this process of Band-Wise Normalization and Batch Correction as BNBC.
This function applies BNBC to the set of contact matrices and returns
a ContactGroup object with matrix bands bstart:nbands
corrected. For those rows in the matrix bands which cannot be
corrected we set all elements to 0.
Very high bands contain little data in Hi-C experiments, and we don't
recommend to analyze those or apply this function to high bands, see
the nbands argument to the function.
We recommend performing bnbc on contact matrices which have been converted to log-CPM and smoothed, see the example.
A ContactGroup object for which matrix bands bstart:nbands have
had BNBC applied.
Johnson, W.E., Li, C. and Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007, 8:118-127. doi:10.1093/biostatistics/kxj037
Fletez-Brant et al. Distance-dependent between-sample normalization for Hi-C experiments. In preparation.
ContactGroup, logCPM, boxSmoother, band
data(cgEx) batches <- colData(cgEx)$Batch cgEx.cpm <- logCPM(cgEx) cgEx.smooth <- boxSmoother(cgEx, 5, mc.cores=1) cgEx.bnbc <- bnbc(cgEx.smooth, batches, 1e7, 4e4, bstart=2, nbands=4)data(cgEx) batches <- colData(cgEx)$Batch cgEx.cpm <- logCPM(cgEx) cgEx.smooth <- boxSmoother(cgEx, 5, mc.cores=1) cgEx.bnbc <- bnbc(cgEx.smooth, batches, 1e7, 4e4, bstart=2, nbands=4)
These functions are apply-type functions for ContactGroup objects.
cgApply(cg, FUN, mc.cores=1, ...) cgBandApply(cg, FUN, nbands=NULL, mc.cores=1, bstart=2, ...)cgApply(cg, FUN, mc.cores=1, ...) cgBandApply(cg, FUN, nbands=NULL, mc.cores=1, bstart=2, ...)
cg |
A ContactGroup object. |
FUN |
A function to be applied. For |
mc.cores |
The number of cores to be used. Defaults to 1 |
bstart |
The first band to apply a function to. Defaults to 2.
Only applicable to |
nbands |
The last band to apply a function to. Default is
|
... |
Passed to |
These methods make it easy to apply functions to either all contact
matrices or a set of bands in all contact matrices. Both methods
accept a function FUN. For cgApply, the first argument
should be cg, the contact group itself. For
cgBandApply, the first argument should also be cg, and
the second argument should be a specific band number. Additionally,
the bands to be iterated are specified through bstart:nbands: bstart
indicates the starting band, and nbands indicates the
last band.
For cgApply, a ContactGroup object. For cgBandApply, a
list whose elements are the returned value of FUN.
ContactGroup,
getBandMatrix,
band
data(cgEx) cgEx.1 <- cgApply(cgEx, FUN=function(xx){ xx + 1 }) band.matrix.list <- cgBandApply(cgEx, FUN=getBandMatrix, bstart=2, nbands=5)data(cgEx) cgEx.1 <- cgApply(cgEx, FUN=function(xx){ xx + 1 }) band.matrix.list <- cgBandApply(cgEx, FUN=getBandMatrix, bstart=2, nbands=5)
This is a sample ContactGroup object representing observations on
chr22 from 3 1k Genomes trios' lymphoblastoid cell lines (LCL). colData(cgEx) gives the cell
line name (CellLine), the ethnicity of the individual
(Population), the family (Family), the gender
(Gender), the relationship of the individuals within a trio
(Role), the replicate number (Tech) and each sample's
batch (Batch).
These data were generated by the dilution Hi-C method using HindIII (Lieberman-Aiden et al.). Hi-C contact matrices were generated by tiling the genome into 40kb bins and counting the number of interactions between bins.
These data have undergone no preprocessing.
The data is an object of class ContactGroup.
Raw data are available from the 4D nucleome data portal (https://data.4dnucleome.org) under accessions 4DNESYUYFD6H, 4DNESVKLYDOH, 4DNESHGL976U, 4DNESJ1VX52C, 4DNESI2UKI7P, 4DNESTAPSPUC, 4DNES4GSP9S4, 4DNESJIYRA44, 4DNESE3ICNE1.
Lieberman-Aiden, E, et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 2009, 326:289-293. doi:10.1126/science.1181369
"ContactGroup"
The ContactGroup class represents a collection of contact matrices which are observations from different samples on the same set of genomic loci.
ContactGroup(rowData, contacts, colData)ContactGroup(rowData, contacts, colData)
rowData |
Object of class |
contacts |
Object of class |
colData |
Object of class |
The ContactGroup class contains a set of contact matrices in the slot 'contacts'. All matrices are required to be of the same dimensionality. 'ContactGroup()' expects a list of symmetric matrices to be passed to the contstructor. Data about these contact matrices is held in two other slots. Data about the genomic loci represented in the ContactGroup is found in the 'rowData' slot as a GenomicRanges objects, and sample-level information is located in the 'colData' slot as a DataFrame.
A ContactGroup object.
In the code snippets below, x is a ContactGroup object.
signature(x = "ContactGroup", i = "ANY", j = "ANY",
drop = "ANY"):
Allows for subsetting the contact matrices
through use of i or of samples through j.
signature(x = "ContactGroup"): Get
sample-level information about samples in x
signature(x = "ContactGroup", value =
"DataFrame"): Set sample-level information about samples in x.
value is expected to be a DataFrame object.
signature(x = "ContactGroup"): Obtain the
dimensions of a ContactGroup. Returns 2 values: one
representing the number of bins in the contact matrices and
another representing the number of samples.
signature(x = "ContactGroup"): Get the
GenomicRanges object describing the loci in the ContactGroup.
value is expected to be a GenomicRanges object.
signature(x = "ContactGroup"): Set the
GenomicRanges object describing the bins in the ContactGroup.
value is expected to be a GenomicRanges object.
signature(object = "ContactGroup"): Method to
display summary information about a ContactGroup: the number of
bins, the width of the bins and the number of samples.
signature(x = "ContactGroup"): Method to
compute the library size of each contact matrix in x.
Library size is defined to be the sum of the upper triangle of a
contact matrix.
signature(x = "ContactGroup"): Method to
transform each contact matrix to logCPM scale.
contacts(x), contacts(x) <- value: Method to
extract the list of contact matrices from a
ContactGroup. value is expected to be a list object.
signature(cg = "ContactGroup",
threshold="ANY", step="ANY"): Method to identify which matrix bands
are no more than threshold bins apart, where each bin
represents step base pairs.
Law, C.W., Chen, Y., Shi, W. and Smyth G.K. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology 2014, 15:R29. doi:10.1186/gb-2014-15-2-r29.
data(cgEx) cgEx[1,] cgEx[,1] cd <- colData(cgEx) colData(cgEx) <- cd gr <- rowData(cgEx) rowData(cgEx) <- gr cgEx cl <- contacts(cgEx) contacts(cgEx) <- cl d.idx <- distanceIdx(cgEx, 1e7, 4e4) libs <- librarySize(cgEx) cgEx.cpm <- logCPM(cgEx) ## below, upper.mats.list is a list of upper triangular matrices ## SampleData is a DataFrame of sample data and LociData is a GenomicRanges objec ## Not run: MatsList <- lapply(upper.mats.list, function(M) M[lower.tri(M)] = M[upper.tri(M)]) cg <- ContactGroup(LociData, MatsList, SampleData) ## End(Not run)data(cgEx) cgEx[1,] cgEx[,1] cd <- colData(cgEx) colData(cgEx) <- cd gr <- rowData(cgEx) rowData(cgEx) <- gr cgEx cl <- contacts(cgEx) contacts(cgEx) <- cl d.idx <- distanceIdx(cgEx, 1e7, 4e4) libs <- librarySize(cgEx) cgEx.cpm <- logCPM(cgEx) ## below, upper.mats.list is a list of upper triangular matrices ## SampleData is a DataFrame of sample data and LociData is a GenomicRanges objec ## Not run: MatsList <- lapply(upper.mats.list, function(M) M[lower.tri(M)] = M[upper.tri(M)]) cg <- ContactGroup(LociData, MatsList, SampleData) ## End(Not run)
These are a set of methods for working with data in cooler file format.
getChrIdx(chr.length, chr, step) getChrCGFromCools(files, chr, step, index.gr, work.dir, exp.name, coldata, norm.factor=NULL) cg2bedgraph2(cg, out.dir, prefix)getChrIdx(chr.length, chr, step) getChrCGFromCools(files, chr, step, index.gr, work.dir, exp.name, coldata, norm.factor=NULL) cg2bedgraph2(cg, out.dir, prefix)
chr.length |
The length of a chromosome. |
step |
The resolution of the data inside the cooler file. |
files |
A vector of cooler file names. |
chr |
The target chromosome to be read. |
index.gr |
A |
work.dir |
Directory for saving temporary files. |
exp.name |
The name of the experiment, will be appended all output file names. |
coldata |
A |
cg |
A |
out.dir |
A directory in which individual bedgraph2 (BG2) files are to be written. |
prefix |
A prefix for all output files; e.g. "treatment_study_". |
norm.factor |
The normalization factor |
These methods allow for the normalization of cooler files. Users must
create their own index, for which we provide getChrIdx, which
is an input into getChrCGFromCools, which uses HiCBricks to
access the cooler files, and returns a ContactGroup
object. Users can then follow the standard pipeline, and save their
data in bedgraph2 (BG2) format using cg2bedgraph2. cooler
provides a tool to convert this format to cooler and users are
encouraged to make use of this tool. Note that HiCBricks expects
multiple resolutions in the cooler file.
For getChrIdx a GRanges object with coordinates
for each bin. For getChrCGFromCools, a
ContactGroup object. There is nothing returned by
cg2bedgraph2.
## Not run: coolerDir <- system.file("cooler", package = "bnbc") cools <- list.files(coolerDir, pattern="cool$", full.names=TRUE) step <- 4e4 ixns <- bnbc:::getGenomeIdx(seqlengths(BSgenome.Hsapiens.UCSC.hg19)["chr22"], step) data(cgEx) cool.cg <- bnbc:::getChrCGFromCools(files = cools, chr = "chr22", step=step, index.gr=ixns, work.dir="tmp.dir", exp.name="example_case", colData = colData(cgEx)[1:2,]) all.equal(contacts(cgEx)[[1]], contacts(cool.cg)[[1]]) ## End(Not run)## Not run: coolerDir <- system.file("cooler", package = "bnbc") cools <- list.files(coolerDir, pattern="cool$", full.names=TRUE) step <- 4e4 ixns <- bnbc:::getGenomeIdx(seqlengths(BSgenome.Hsapiens.UCSC.hg19)["chr22"], step) data(cgEx) cool.cg <- bnbc:::getChrCGFromCools(files = cools, chr = "chr22", step=step, index.gr=ixns, work.dir="tmp.dir", exp.name="example_case", colData = colData(cgEx)[1:2,]) all.equal(contacts(cgEx)[[1]], contacts(cool.cg)[[1]]) ## End(Not run)
Get the indices corresponding to a matrix band.
getBandIdx(n, band.no)getBandIdx(n, band.no)
n |
The number of rows/columns of a contact matrix |
band.no |
Integer specifying which matrix band. |
This function is used in subsetting contact matrices, primarily in getBandMatrix. However, users wishing to extract band matrices directly may find this useful
A matrix with 2 columns and as many rows as entries in the matrix band.
ContactGroup, getBandMatrix, band
data(cgEx) b2.idx <- getBandIdx(nrow(cgEx), 2)data(cgEx) b2.idx <- getBandIdx(nrow(cgEx), 2)
Get band matrix from ContactGroup.
getBandMatrix(cg, band.no=1)getBandMatrix(cg, band.no=1)
cg |
A ContactGroup object. |
band.no |
Integer specifying which matrix band. |
A band matrix is a matrix whose columns are the band.no-th
off-diagonal of each sample's contact matrix. If there are k
samples and matrix band band.no has r entries, then the
returned band matrix is of dimension r x k.
A matrix with one column per sample in the ContactGroup and number of rows equal to the length of the matrix band.
ContactGroup, getBandIdx, band
data(cgEx) b2 <- getBandMatrix(cgEx, 2)data(cgEx) b2 <- getBandMatrix(cgEx, 2)
These functions find and remove rows in the set of contact matrices for which all elements in the row are 0 in all samples.
getGroupZeros(cg) dropGroupZeros(cg, g0s)getGroupZeros(cg) dropGroupZeros(cg, g0s)
cg |
A ContactGroup object. |
g0s |
A list of elements identified as group zeros. |
Group zeros are those rows for which all elements of the row in all samples are 0. These can impact estimation of features such as A/B compartment status and so should be removed for many analyses.
A ContactGroup object with the group zeros removed from all rows in all contact matrices.
data(cgEx) g0s <- getGroupZeros(cgEx) cgEx <- dropGroupZeros(cgEx, g0s)data(cgEx) g0s <- getGroupZeros(cgEx) cgEx <- dropGroupZeros(cgEx, g0s)
These functions apply a smoothing kernel to all contact matrices in a ContactGroup object.
boxSmoother(cg, h, mc.cores) gaussSmoother(cg, radius, sigma, mc.cores)boxSmoother(cg, h, mc.cores) gaussSmoother(cg, radius, sigma, mc.cores)
cg |
A ContactGroup object. |
h |
The desired smoother radius. Only applies to box smoother. This is an integer. |
radius |
The desired smoother width. Only applies to Gaussian smoother. This is an integer. |
sigma |
The desired smoother standard deviation. Only applies to Gaussian smoother. This is a positive number. |
mc.cores |
The number of cores to be used. |
boxSmoother applies a square smoothing kernel of radius h to
all contact matrices in a ContactGroup object. Specifying radius
h implies that the width of the kernel is matrix
cells.
gaussSmoother applies a square Gaussain smoothing kernel of width
radius with standard deviation sigma to
all contact matrices in a ContactGroup object.
A ContactGroup object is returned that contains the smoothed matrices.
data(cgEx) cgEx.smooth <- boxSmoother(cgEx, h=5, mc.cores=1) cgEx.smooth <- gaussSmoother(cgEx, radius=3, sigma=0.5, mc.cores=1)data(cgEx) cgEx.smooth <- boxSmoother(cgEx, h=5, mc.cores=1) cgEx.smooth <- gaussSmoother(cgEx, radius=3, sigma=0.5, mc.cores=1)