- recipe bioconductor-hiccompare
HiCcompare: Joint normalization and comparative analysis of multiple Hi-C datasets
- Homepage:
https://bioconductor.org/packages/3.20/bioc/html/HiCcompare.html
- License:
MIT + file LICENSE
- Recipe:
- Links:
biotools: HiCcompare, doi: 10.1186/s12859-018-2288-x
HiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. HiCcompare operates on processed Hi-C data in the form of chromosome-specific chromatin interaction matrices. It accepts three-column tab-separated text files storing chromatin interaction matrices in a sparse matrix format which are available from several sources. HiCcompare is designed to give the user the ability to perform a comparative analysis on the 3-Dimensional structure of the genomes of cells in different biological states.`HiCcompare` differs from other packages that attempt to compare Hi-C data in that it works on processed data in chromatin interaction matrix format instead of pre-processed sequencing data. In addition, `HiCcompare` provides a non-parametric method for the joint normalization and removal of biases between two Hi-C datasets for the purpose of comparative analysis. `HiCcompare` also provides a simple yet robust method for detecting differences between Hi-C datasets.
- package bioconductor-hiccompare¶
- Versions:
1.32.0-0,1.28.0-0,1.24.0-0,1.22.1-0,1.20.0-0,1.16.0-0,1.14.0-0,1.12.0-1,1.12.0-0,1.32.0-0,1.28.0-0,1.24.0-0,1.22.1-0,1.20.0-0,1.16.0-0,1.14.0-0,1.12.0-1,1.12.0-0,1.10.0-0,1.8.0-0,1.6.0-1,1.4.0-0,1.2.0-0,1.0.0-0- Depends:
on bioconductor-biocparallel
>=1.44.0,<1.45.0on bioconductor-genomicranges
>=1.62.0,<1.63.0on bioconductor-interactionset
>=1.38.0,<1.39.0on bioconductor-iranges
>=2.44.0,<2.45.0on bioconductor-rhdf5
>=2.54.0,<2.55.0on bioconductor-s4vectors
>=0.48.0,<0.49.0on r-base
>=4.5,<4.6.0a0on r-data.table
on r-dplyr
on r-ggplot2
on r-gridextra
on r-gtools
on r-kernsmooth
on r-mgcv
on r-pheatmap
- 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-hiccompare
to add into an existing workspace instead, run:
pixi add bioconductor-hiccompare
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-hiccompare
Alternatively, to install into a new environment, run:
conda create -n envname bioconductor-hiccompare
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-hiccompare:<tag>
(see bioconductor-hiccompare/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:
[](http://bioconda.github.io/recipes/bioconductor-hiccompare/README.html)
