proDA

DOI: 10.18129/B9.bioc.proDA    

This is the development version of proDA; to use it, please install the devel version of Bioconductor.

Differential Abundance Analysis of Label-Free Mass Spectrometry Data

Bioconductor version: Development (3.10)

Account for missing values in label-free mass spectrometry data without imputation. The package implements a probabilistic dropout model that ensures that the information from observed and missing values are properly combined. It adds empirical Bayesian priors to increase power to detect differentially abundant proteins.

Author: Constantin Ahlmann-Eltze [aut, cre] , Simon Anders [ths]

Maintainer: Constantin Ahlmann-Eltze <artjom31415 at googlemail.com>

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

Installation

To install this package, start R (version "3.6") and enter:

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

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

BiocManager::install("proDA")

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("proDA")

 

HTML R Script Data Import
HTML R Script Introduction
PDF   Reference Manual
Text   NEWS

Details

biocViews Bayesian, DifferentialExpression, MassSpectrometry, Normalization, Proteomics, QualityControl, Regression, Software
Version 0.99.11
In Bioconductor since BioC 3.10 (R-3.6)
License GPL-3
Depends
Imports stats, utils, methods, BiocGenerics, SummarizedExperiment, S4Vectors, extraDistr
LinkingTo
Suggests testthat (>= 2.1.0), MSnbase, dplyr, stringr, readr, tidyr, tibble, limma, DEP, numDeriv, pheatmap, knitr, rmarkdown
SystemRequirements
Enhances
URL https://github.com/const-ae/proDA
BugReports https://github.com/const-ae/proDA/issues
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 proDA_0.99.11.tar.gz
Windows Binary proDA_0.99.11.zip
Mac OS X 10.11 (El Capitan) proDA_0.99.11.tgz
Source Repository git clone https://git.bioconductor.org/packages/proDA
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/proDA
Package Short Url https://bioconductor.org/packages/proDA/
Package Downloads Report Download Stats

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