PAA

This is the development version of PAA; for the stable release version, see PAA.

PAA (Protein Array Analyzer)

Bioconductor version: Development (3.23)

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>

Citation (from within R, enter citation("PAA")):

Installation

To install this package, start R (version "4.6") and enter:


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

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("PAA")

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

Documentation


Reference Manual	PDF

Need some help? Ask on the Bioconductor Support site!

Details

biocViews	Classification, Microarray, OneChannel, Proteomics, Software
Version	1.45.0
In Bioconductor since	BioC 3.0 (R-3.1) (11 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
System Requirements	C++ software package Random Jungle
URL	http://www.ruhr-uni-bochum.de/mpc/software/PAA/

Suggests	BiocStyle, RUnit, BiocGenerics, vsn
Linking To	Rcpp
Enhances
Depends On Me
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Build Report	Build Report

Package Archives

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

Source Package
Windows Binary (x86_64)
macOS Binary (x86_64)
macOS Binary (arm64)
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