proDA
Differential Abundance Analysis of Label-Free Mass Spectrometry Data
Bioconductor version: 3.23 · Package version: 1.26.0
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.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("proDA")Details
| Maintainer | Constantin Ahlmann-Eltze <artjom31415@googlemail.com> |
| Author | Constantin Ahlmann-Eltze [aut, cre] (ORCID: <https://orcid.org/0000-0002-3762-068X>), Simon Anders [ths] (ORCID: <https://orcid.org/0000-0003-4868-1805>) |
| License | GPL-3 |
| URL | https://github.com/const-ae/proDA |
| Bug Reports | https://github.com/const-ae/proDA/issues |
| Downloads rank | 491 |
| Source branch | RELEASE_3_23 |
| biocViews | Bayesian, DifferentialExpression, MassSpectrometry, Normalization, Proteomics, QualityControl, Regression, Software |
Documentation
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Dependencies
Imports: stats, utils, methods, BiocGenerics, SummarizedExperiment, S4Vectors, extraDistr
Suggests: testthat (>= 2.1.0), MSnbase, dplyr, stringr, readr, tidyr, tibble, limma, numDeriv, pheatmap, knitr, rmarkdown, BiocStyle