Package index • metabom8

Import 1D NMR data

read1d() read1d_proc(): Import 1D NMR spectra (TopSpin processed)

read1d_raw(): Read Raw 1D NMR FIDs and Process to Spectra

Visualising spectra

matspec(): Plot Overlayed NMR Spectra

spec(): Plot a Single NMR Spectrum

specOverlay(): Overlay NMR Spectra Using ggplot2

Pre-processing and quality control

alignSegment(): Align NMR Spectra via Cross-Correlation

binning(): Spectral data binning

bline() bcor(): Baseline Correction for 1D NMR Spectra

calibrate(): Chemical Shift Calibration

excise1d(): Excise Standard Chemical Shift Regions from NMR Spectra

get_idx() get.idx(): Select Indices for a Chemical Shift Region

lw(): Full Width at Half Maximum (FWHM) Estimation

noise.est(): Estimate Noise Level in 1D NMR Spectra

normErectic(): Normalize Spectra Using ERETIC Signal

pqn(): Probabilistic Quotient Normalisation (PQN)

Scalings, effect size

es_cdelta(): Cliff's Delta Effect Size

minmax(): Min-Max Scaling to \([0,1]\)

scRange(): Min-Max Scaling to Arbitrary Range

PCA and O-PLS modelling and interpretation

cvanova(): Cross-validated ANOVA for O-PLS models

dmodx(): Distance to the Model in X-Space (DModX)

eruption(): Eruption Plot for OPLS-DA Model Interpretation

opls(): #' @title Orthogonal Partial Least Squares (O-PLS) #' #' @description #' Fits an Orthogonal Projections to Latent Structures (O-PLS) model for regression or classification. #' The number of orthogonal components is automatically selected using internal cross-validation. #' To avoid underfitting, components are added incrementally, while overfitting is prevented by requiring that each new component improves predictive performance beyond a defined threshold (ΔQ² / ΔAUROC > 0.05). This ensures the model captures relevant structure without modelling noise or irrelevant variation.

opls_perm(): OPLS Model Validation via Y-Permutation

pca(): Principal Component Analysis (PCA)

ppick(): Find Local Extrema in NMR Spectra (Peak Picking)

plotload(): Visualize PCA or OPLS Loadings for NMR Data

plotscores(): Plot PCA, PLS or OPLS Model Scores

pls(): Partial Least Squares (PLS)

predict_opls(): Predict New Data Using an OPLS Model

specload(): Overlay Loadings with NMR Spectra

Metabolite identification

stocsy(): Statistical Total Correlation Spectroscopy (STOCSY)

storm(): Subset Optimisation by Reference Matching (STORM)

plotStocsy(): Plot Statistical Total Correlation Spectroscopy (STOCSY)

Class definitions

OPLS_metabom8-class: OPLS model object from metabom8

PLS_metabom8-class: PLS model object from metabom8

PCA_metabom8-class: PCA model object from metabom8

stocsy1d_metabom8-class: STOCSY model object from metabom8

Datasets

covid: COVID-19 blood plasma proton NMR spectra (processed)

covid_raw: COVID-19 blood plasma proton NMR spectra (raw)