enviPick, ggplot2, scales, utils, stats, graphics,grDevices
Manuel David Peris Diaz <manuel.perisdiaz@uwr.edu.pl>
Automatic metabolite annotation from Liquid Chromatography-Mass Spectrometry (LC-MS and LC-MS/MS DIA) analysis.

enviPick, ggplot2, scales, utils, stats, graphics,grDevices,
dplyr

Simulating dynamic (longitudinal, time-resolved) metabolomics data based on an underlying biological network. The network is initiating with certain concentrations and evolves over a simulated time period. Optionally external influxes (concentration drivers) can be added.

Normalize Metabolomic Data using QC Signal
Takes QC signal for each day and normalize metabolomic
data that has been acquired in a certain period of time. At least
three QC per day are required.

Probabilistic latent variable models for metabolomic data.
Nyamundanda Gift, Isobel Claire Gormley and Lorraine Brennan.
Fits probabilistic principal components analysis,
probabilistic principal components and covariates analysis and
mixtures of probabilistic principal components models to
metabolomic spectral data.
mclust, mvtnorm, gtools, gplots, hacks, grDevices

mclust, mvtnorm, gtools, gplots, ellipse, grDevices

mclust, mvtnorm, ellipse, gtools, gplots

Probabilistic Latent Variable Models for Metabolomic Data

A Biomarker Validation Approach for Classification and
Predicting Survival Using Metabolomics Signature
An approach to identifies metabolic biomarker signature for metabolic data by discovering predictive metabolite for predicting survival and classifying patients into risk groups.
Classifiers are constructed as a linear combination of predictive/important metabolites, prognostic factors and treatment effects if necessary.
Several methods were implemented to reduce the metabolomics matrix such as the  principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9> ,
the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, the
elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>.
Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified.
Large scale cross validation can be performed in  order to investigate the mostly selected predictive metabolites and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.
superpc, glmnet, matrixStats, survminer, survival, rms, tidyr,
pls, Rdpack, methods, stats, gplots, ggplot2
Olajumoke Evangelina Owokotomo [aut, cre],
Ziv Shkedy [aut]