Package Recipe 'bioconductor-pmp'

recipe bioconductor-pmp

Peak Matrix Processing and signal batch correction for metabolomics datasets

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/pmp.html
License:: GPL-3
Recipe:: /bioconductor-pmp/meta.yaml

Methods and tools for (pre-)processing of metabolomics datasets (i.e. peak matrices), including filtering, normalisation, missing value imputation, scaling, and signal drift and batch effect correction methods. Filtering methods are based on: the fraction of missing values (across samples or features); Relative Standard Deviation (RSD) calculated from the Quality Control (QC) samples; the blank samples. Normalisation methods include Probabilistic Quotient Normalisation (PQN) and normalisation to total signal intensity. A unified user interface for several commonly used missing value imputation algorithms is also provided. Supported methods are: k-nearest neighbours (knn), random forests (rf), Bayesian PCA missing value estimator (bpca), mean or median value of the given feature and a constant small value. The generalised logarithm (glog) transformation algorithm is available to stabilise the variance across low and high intensity mass spectral features. Finally, this package provides an implementation of the Quality Control-Robust Spline Correction (QCRSC) algorithm for signal drift and batch effect correction of mass spectrometry-based datasets.

package bioconductor-pmp¶

Versions:

1.22.1-0, 1.18.0-0, 1.14.0-0, 1.12.0-0, 1.10.0-0, 1.6.0-0, 1.4.0-0, 1.2.1-0, 1.2.0-1,

1.22.1-0, 1.18.0-0, 1.14.0-0, 1.12.0-0, 1.10.0-0, 1.6.0-0, 1.4.0-0, 1.2.1-0, 1.2.0-1, 1.2.0-0, 1.0.0-0

Depends:

on bioconductor-impute >=1.84.0,<1.85.0
on bioconductor-pcamethods >=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 r-base >=4.5,<4.6.0a0
on r-ggplot2
on r-matrixstats
on r-missforest
on r-reshape2

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-pmp

to add into an existing workspace instead, run:

pixi add bioconductor-pmp

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-pmp

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-pmp

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-pmp:<tag>

(see bioconductor-pmp/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-pmp/README.html)