recipe bioconductor-hicexperiment

Bioconductor class for interacting with Hi-C files in R






R generic interface to Hi-C contact matrices in `.(m)cool`, `.hic` or HiC-Pro derived formats, as well as other Hi-C processed file formats. Contact matrices can be partially parsed using a random access method, allowing a memory-efficient representation of Hi-C data in R. The `HiCExperiment` class stores the Hi-C contacts parsed from local contact matrix files. `HiCExperiment` instances can be further investigated in R using the `HiContacts` analysis package.

package bioconductor-hicexperiment

(downloads) docker_bioconductor-hicexperiment



depends bioconductor-biocgenerics:


depends bioconductor-biocio:


depends bioconductor-biocparallel:


depends bioconductor-genomeinfodb:


depends bioconductor-genomicranges:


depends bioconductor-interactionset:


depends bioconductor-iranges:


depends bioconductor-rhdf5:


depends bioconductor-s4vectors:


depends r-base:


depends r-dplyr:

depends r-matrix:

depends r-strawr:

depends r-vroom:



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-hicexperiment

and update with::

   mamba update bioconductor-hicexperiment

To create a new environment, run:

mamba create --name myenvname bioconductor-hicexperiment

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<tag>

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

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