recipe bioconductor-multihiccompare

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

Homepage:

https://bioconductor.org/packages/3.18/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:

1.20.0-0,  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, 

1.20.0-0,  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 bioconductor-biocparallel:

>=1.36.0,<1.37.0

depends bioconductor-edger:

>=4.0.0,<4.1.0

depends bioconductor-genomeinfodb:

>=1.38.0,<1.39.0

depends bioconductor-genomeinfodbdata:

>=1.2.0,<1.3.0

depends bioconductor-genomicranges:

>=1.54.0,<1.55.0

depends bioconductor-hiccompare:

>=1.24.0,<1.25.0

depends r-aggregation:

depends r-base:

>=4.3,<4.4.0a0

depends r-data.table:

depends r-dplyr:

depends r-pbapply:

depends r-pheatmap:

depends r-qqman:

requirements:

additional platforms:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-multihiccompare

and update with::

   mamba update bioconductor-multihiccompare

To create a new environment, run:

mamba create --name myenvname bioconductor-multihiccompare

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

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

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

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