recipe bioconductor-tcc

TCC: Differential expression analysis for tag count data with robust normalization strategies

Homepage:

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

License:

GPL-2

Recipe:

/bioconductor-tcc/meta.yaml

This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages.

package bioconductor-tcc

(downloads) docker_bioconductor-tcc

Versions:
1.50.0-0,  1.46.0-0,  1.42.0-0,  1.38.0-0,  1.34.0-0,  1.32.0-0,  1.30.0-1,  1.30.0-0,  1.28.0-0, 

1.50.0-0,  1.46.0-0,  1.42.0-0,  1.38.0-0,  1.34.0-0,  1.32.0-0,  1.30.0-1,  1.30.0-0,  1.28.0-0,  1.26.0-0,  1.24.0-1,  1.22.0-0,  1.20.1-0,  1.18.0-0

Depends:

  • on bioconductor-deseq2 >=1.50.0,<1.51.0

  • on bioconductor-edger >=4.8.0,<4.9.0

  • on bioconductor-roc >=1.86.0,<1.87.0

  • on r-base >=4.5,<4.6.0a0

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-tcc

to add into an existing workspace instead, run:

pixi add bioconductor-tcc

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-tcc

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-tcc

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-tcc:<tag>

(see bioconductor-tcc/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 install-with-bioconda 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-tcc/README.html)