prolfqua
This is the development version of prolfqua; to use it, please install the devel version of Bioconductor.
Proteomics Label Free Quantification
Bioconductor version: Development (3.24)
prolfqua covers the steps of a mass spectrometry based label-free quantification (LFQ) analysis: quality control, normalization, aggregation of peptides to proteins, modelling, hypothesis testing and sample size estimation. A single function, build_contrast_analysis(), fits a model per protein and computes contrasts with one of many methods: linear, robust and rank-based regression, limma and limma-voom, DEqMS variance moderation, limpa's detection probability model, Firth logistic and binomial models of detection, mixed models and ROPECA on peptide-level data, a hurdle model combining intensity and detection, and variants that impute groups without observations at the limit of detection. All methods return their results through the same interface and columns, so they can be compared and combined. prolfqua models simple designs with one explanatory variable as well as experiments with several factors, and was developed while analysing dozens of proteomics datasets at the Functional Genomics Center Zurich.
Author: Witold Wolski [aut] 
, Functional Genomics Center Zurich [fnd], Christian Panse [cre]
Maintainer: Christian Panse <cp at fgcz.ethz.ch>
citation("prolfqua")):Witold Wolski. prolfqua: Proteomics Label Free Quantification. doi:10.18129/B9.bioc.prolfqua, R package version 0.99.4, https://bioconductor.org/packages/prolfqua.
Generated from the package metadata; it may differ from the package's own citation.
Installation
To install this package, start R (version "4.6") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
## The following initializes the development version of Bioconductor
BiocManager::install(version = "devel")
BiocManager::install("prolfqua")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("prolfqua")| Comparing Two Groups with prolfqua | HTML | R Script |
| Introduction to prolfqua | HTML | R Script |
| Reference Manual | ||
| NEWS | Text | |
| LICENSE | Text |
Details
| biocViews | MassSpectrometry, Metabolomics, Normalization, Preprocessing, Proteomics, QualityControl, Software |
| Version | 0.99.4 |
| In Bioconductor since | BioC 3.24 (R-4.6) |
| License | MIT + file LICENSE |
| Depends | R (>= 4.5) |
| Imports | circlize, ComplexHeatmap, dplyr, forcats, ggplot2, ggbeeswarm, ggrepel, limma, lme4, lmerTest, logistf, MASS, methods, plotly, progress, purrr, rlang, stringi, tidyselect, tidyr, tibble, UpSetR, withr |
| System Requirements | |
| URL | https://github.com/fgcz/prolfqua https://fgcz.github.io/prolfqua/ |
| Bug Reports | https://github.com/fgcz/prolfqua/issues |
See More
| Suggests | BiocCheck, BiocStyle, corrplot, covr, crosstalk, cyclocomp, devtools, goodpractice, gridExtra, KernSmooth, knitr, limpa, lintr, pkgdown, preprocessCore, rcmdcheck, Rfit, rmarkdown, roxygen2, S4Vectors, SummarizedExperiment, testthat, vsn |
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| Depends On Me | |
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| Build Report | Build Report, r-universe |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | prolfqua_0.99.4.tar.gz |
| Windows Binary (x86_64) | prolfqua_0.99.4.zip |
| macOS Binary (big-sur-x86_64) | prolfqua_0.99.4.tgz |
| macOS Binary (sonoma-arm64) | prolfqua_0.99.4.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/prolfqua |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/prolfqua |
| Package Short Url | https://bioconductor.org/packages/prolfqua/ |
| Package Downloads Report | Download Stats |