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QFeatures

Quantitative features for mass spectrometry data

Bioconductor version: 3.23 · Package version: 1.22.0

The QFeatures infrastructure enables the management and processing of quantitative features for high-throughput mass spectrometry assays. It provides a familiar Bioconductor user experience to manages quantitative data across different assay levels (such as peptide spectrum matches, peptides and proteins) in a coherent and tractable format.

Installation

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

BiocManager::install("QFeatures")

Details

MaintainerLaurent Gatto <laurent.gatto@uclouvain.be>
AuthorLaurent Gatto [aut, cre] (ORCID: <https://orcid.org/0000-0002-1520-2268>), Christophe Vanderaa [aut] (ORCID: <https://orcid.org/0000-0001-7443-5427>), Karolína Kryštofová [ctb] (ORCID: <https://orcid.org/0009-0004-2896-2188>), Léopold Guyot [ctb]
LicenseArtistic-2.0
URLhttps://rformassspectrometry.github.io/QFeatures
Bug Reportshttps://github.com/rformassspectrometry/QFeatures/issues
Downloads rank3948
Source branchRELEASE_3_23
biocViewsInfrastructure, MassSpectrometry, Metabolomics, Proteomics, Software

Documentation

Download

Dependencies

Depends: R (>= 4.1), MultiAssayExperiment (>= 1.33.6)

Imports: methods, stats, utils, S4Vectors, IRanges, SummarizedExperiment, BiocGenerics (>= 0.53.4), ProtGenerics (>= 1.35.1), AnnotationFilter, lazyeval, Biobase, MsCoreUtils (>= 1.7.2), igraph, grDevices, plotly, tidyr, tidyselect, reshape2

Suggests: SingleCellExperiment, MsDataHub (>= 1.11.5), arrow, Matrix, HDF5Array, msdata, ggplot2, gplots, dplyr, limma, DT, shiny, shinydashboard, testthat, knitr, BiocStyle, rmarkdown, vsn, preprocessCore, matrixStats, imputeLCMD, pcaMethods, impute, norm, ComplexHeatmap

Reverse dependencies

Depends On Me (4): hdxmsqc, msqrob2, scp, scpdata

Imports Me (6): MetaboAnnotation, MsExperiment, mspms, omicsGMF, proBatch, PSMatch

Suggests Me (1): MsDataHub