bnbc-package {bnbc} | R Documentation

## Bandwise normalization and batch correction of Hi-C data

### Description

Tools to normalize (several) Hi-C data from replicates.

### Details

The DESCRIPTION file:

Package: | bnbc
Version: | 1.2.0
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 = "cafletezbrant@gmail.com"), person(c("Kasper", "Daniel"), "Hansen", role = "aut"))
Depends: | R (>= 3.4.0), methods, BiocGenerics, SummarizedExperiment, GenomicRanges
Suggests: | BiocStyle, knitr, rmarkdown, RUnit
Imports: | Rcpp (>= 0.12.12), IRanges, GenomeInfoDb, S4Vectors, matrixStats, preprocessCore, sva, parallel, EBImage, utils
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
Author: | Kipper Fletez-Brant [cre, aut], Kasper Daniel Hansen [aut]
Maintainer: | Kipper Fletez-Brant <cafletezbrant@gmail.com>

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
    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`.

### Author(s)

NA

Maintainer: NA

### References

Fletez-Brant et al. Distance-dependent between-sample normalization for Hi-C experiments. In preparation.

### See Also

`bnbc`, `ContactGroup`, `band`, `cgApply`.

### Examples

    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)

[Package bnbc version 1.2.0 Index]