PAA

DOI: 10.18129/B9.bioc.PAA    

PAA (Protein Array Analyzer)

Bioconductor version: Release (3.13)

PAA imports single color (protein) microarray data that has been saved in gpr file format - esp. ProtoArray data. After preprocessing (background correction, batch filtering, normalization) univariate feature preselection is performed (e.g., using the "minimum M statistic" approach - hereinafter referred to as "mMs"). Subsequently, a multivariate feature selection is conducted to discover biomarker candidates. Therefore, either a frequency-based backwards elimination aproach or ensemble feature selection can be used. PAA provides a complete toolbox of analysis tools including several different plots for results examination and evaluation.

Author: Michael Turewicz [aut, cre], Martin Eisenacher [ctb, cre]

Maintainer: Michael Turewicz <michael.turewicz at rub.de>, Martin Eisenacher <martin.eisenacher at rub.de>

Installation

To install this package, start R (version "4.1") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("PAA")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("PAA")

 

PDF	R Script	PAA tutorial
PDF		PAA_1.7.1.pdf
PDF		Reference Manual
Text		README
Text		NEWS
Text		LICENSE

Details

biocViews	Classification, Microarray, OneChannel, Proteomics, Software
Version	1.26.0
In Bioconductor since	BioC 3.0 (R-3.1) (7 years)
License	BSD_3_clause + file LICENSE
Depends	R (>= 3.2.0), Rcpp (>= 0.11.6)
Imports	e1071, gplots, gtools, limma, MASS, mRMRe, randomForest, ROCR, sva
LinkingTo	Rcpp
Suggests	BiocStyle, RUnit, BiocGenerics, vsn
SystemRequirements	C++ software package Random Jungle
Enhances
URL	http://www.ruhr-uni-bochum.de/mpc/software/PAA/
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	PAA_1.26.0.tar.gz
Windows Binary	PAA_1.26.0.zip
macOS 10.13 (High Sierra)	PAA_1.26.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/PAA
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/PAA
Package Short Url	https://bioconductor.org/packages/PAA/
Package Downloads Report	Download Stats
Old Source Packages for BioC 3.13	Source Archive