pmp - Peak Matrix Processing and signal batch correction for
metabolomics datasets
Methods and tools for (pre-)processing of metabolomics
datasets (i.e. peak matrices), including filtering,
normalisation, missing value imputation, scaling, and signal
drift and batch effect correction methods. Filtering methods
are based on: the fraction of missing values (across samples or
features); Relative Standard Deviation (RSD) calculated from
the Quality Control (QC) samples; the blank samples.
Normalisation methods include Probabilistic Quotient
Normalisation (PQN) and normalisation to total signal
intensity. 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 a constant small value. The
generalised logarithm (glog) transformation algorithm is
available to stabilise the variance across low and high
intensity mass spectral features. Finally, this package
provides an implementation of the Quality Control-Robust Spline
Correction (QCRSC) algorithm for signal drift and batch effect
correction of mass spectrometry-based datasets.