DOI: 10.18129/B9.bioc.pmp    

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

Peak Matrix Processing and signal batch correction for metabolomics data sets

Bioconductor version: Development (3.11)

Tools for scaling, normalisation and filtering of metabolomics data sets after raw data processing. Functionality includes: data filtering based on fraction of missing values across samples or features, blank sample filter and feature filter based on %RSD values using Quality Control (QC) samples. A unified user interface for several commonly used missing value imputation algorithms is also provided. Supported methods are: k-nearest neighbours (knn), random forests (rf), Bayesian PCA missing value estimator (bpca), mean or median value of the given feature and constant small value. Variance stabilising generalised logarithm transformation (glog) algorithm is implemented to help minimise contributions from unwanted technical variation. Probabilistic quotient normalisation (PQN) and normalisation to total signal intensity are also included. Additionally this package provides implementation of Quality Control-Robust Spline Correction (QCRSC) signal batch effect correction method for mass spectrometry data sets.

Author: Andris Jankevics and Ralf Weber

Maintainer: Andris Jankevics <a.jankevics at>

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biocViews BatchEffect, MassSpectrometry, Metabolomics, QualityControl, Software
Version 0.99.2
In Bioconductor since BioC 3.11 (R-4.0)
License GPL-3
Depends R (>= 4.0)
Imports stats, impute, pcaMethods, missForest, ggplot2, methods, SummarizedExperiment, S4Vectors, matrixStats, grDevices, reshape2, utils
Suggests testthat, covr, knitr, rmarkdown, BiocStyle, gridExtra, magick
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