recipe bioconductor-fastseg

fastseg - a fast segmentation algorithm

Homepage:

https://bioconductor.org/packages/3.20/bioc/html/fastseg.html

License:

LGPL (>= 2.0)

Recipe:

/bioconductor-fastseg/meta.yaml

Links:

biotools: fastseg

fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data. The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi 2001). The speed-up arises from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments' first and higher order moments.

package bioconductor-fastseg

(downloads) docker_bioconductor-fastseg

Versions:
1.56.0-01.52.0-01.48.0-11.48.0-01.46.0-01.44.0-11.44.0-01.40.0-21.40.0-1

1.56.0-01.52.0-01.48.0-11.48.0-01.46.0-01.44.0-11.44.0-01.40.0-21.40.0-11.40.0-01.38.0-01.36.0-11.36.0-01.34.0-01.32.0-01.30.0-11.28.0-11.28.0-01.26.0-01.24.0-01.22.0-01.20.0-11.20.0-0

Depends:
  • on bioconductor-biobase >=2.70.0,<2.71.0

  • on bioconductor-biobase >=2.70.0,<2.71.0a0

  • on bioconductor-biocgenerics >=0.56.0,<0.57.0

  • on bioconductor-biocgenerics >=0.56.0,<0.57.0a0

  • on bioconductor-genomicranges >=1.62.0,<1.63.0

  • on bioconductor-genomicranges >=1.62.1,<1.63.0a0

  • on bioconductor-iranges >=2.44.0,<2.45.0

  • on bioconductor-iranges >=2.44.0,<2.45.0a0

  • on bioconductor-s4vectors >=0.48.0,<0.49.0

  • on bioconductor-s4vectors >=0.48.0,<0.49.0a0

  • on libblas >=3.9.0,<4.0a0

  • on libgcc >=14

  • on liblapack >=3.9.0,<4.0a0

  • on liblzma >=5.8.2,<6.0a0

  • on libstdcxx >=14

  • on libzlib >=1.3.1,<2.0a0

  • on r-base >=4.5,<4.6.0a0

Additional platforms:
linux-aarch64

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-fastseg

to add into an existing workspace instead, run:

pixi add bioconductor-fastseg

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-fastseg

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-fastseg

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-fastseg:<tag>

(see bioconductor-fastseg/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.

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