Package Recipe 'bioconductor-multihiccompare'

recipe bioconductor-multihiccompare

Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are available

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/multiHiCcompare.html
License:: MIT + file LICENSE
Recipe:: /bioconductor-multihiccompare/meta.yaml

multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner.

package bioconductor-multihiccompare¶

Versions:

1.28.0-0, 1.24.0-0, 1.20.0-0, 1.18.1-0, 1.16.0-0, 1.12.0-0, 1.10.0-0, 1.8.0-1, 1.8.0-0,

1.28.0-0, 1.24.0-0, 1.20.0-0, 1.18.1-0, 1.16.0-0, 1.12.0-0, 1.10.0-0, 1.8.0-1, 1.8.0-0, 1.6.0-0, 1.4.0-0, 1.2.0-1, 1.0.0-1, 1.0.0-0

Depends:

on bioconductor-biocparallel >=1.44.0,<1.45.0
on bioconductor-edger >=4.8.0,<4.9.0
on bioconductor-genomeinfodb >=1.46.0,<1.47.0
on bioconductor-genomeinfodbdata >=1.2.0,<1.3.0
on bioconductor-genomicranges >=1.62.0,<1.63.0
on bioconductor-hiccompare >=1.32.0,<1.33.0
on r-aggregation
on r-base >=4.5,<4.6.0a0
on r-data.table
on r-dplyr
on r-pbapply
on r-pheatmap
on r-qqman

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-multihiccompare

to add into an existing workspace instead, run:

pixi add bioconductor-multihiccompare

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-multihiccompare

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-multihiccompare

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/bioconductor-multihiccompare:<tag>

(see bioconductor-multihiccompare/tags for valid values for <tag>).

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-multihiccompare/README.html)