Package Recipe 'bioconductor-prone'

recipe bioconductor-prone

The PROteomics Normalization Evaluator

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/PRONE.html
License:: GPL (>= 3)
Recipe:: /bioconductor-prone/meta.yaml

High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed.

package bioconductor-prone¶

Versions:

1.4.0-0, 1.0.0-0

Depends:

on bioconductor-biobase >=2.70.0,<2.71.0
on bioconductor-complexheatmap >=2.26.0,<2.27.0
on bioconductor-deqms >=1.28.0,<1.29.0
on bioconductor-edger >=4.8.0,<4.9.0
on bioconductor-limma >=3.66.0,<3.67.0
on bioconductor-msnbase >=2.36.0,<2.37.0
on bioconductor-normalyzerde >=1.28.0,<1.29.0
on bioconductor-poma >=1.20.0,<1.21.0
on bioconductor-preprocesscore >=1.72.0,<1.73.0
on bioconductor-rots >=2.2.0,<2.3.0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on bioconductor-vsn >=3.78.0,<3.79.0
on r-base >=4.5,<4.6.0a0
on r-circlize
on r-complexupset
on r-data.table
on r-dendsort
on r-dplyr
on r-ggplot2
on r-ggtext
on r-gprofiler2
on r-gtools
on r-magrittr
on r-mass
on r-matrixstats
on r-plotroc
on r-purrr
on r-rcolorbrewer
on r-reshape2
on r-scales
on r-stringr
on r-tibble
on r-tidyr
on r-upsetr
on r-vegan

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-prone

to add into an existing workspace instead, run:

pixi add bioconductor-prone

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-prone

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-prone

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-prone:<tag>

(see bioconductor-prone/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:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-prone/README.html)