Title: Probabilistic Latent Variable Models for Metabolomic Data

Packaged: 2012-08-29 13:29:24 UTC; clairegormleyoldmac

Title: A Biomarker Validation Approach for Classification and
Predicting Survival Using Metabolomics Signature
Maintainer: Olajumoke Evangelina Owokotomo <olajumoke.owokotomo@uhasselt.be>
Description: 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> ,
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.
Imports: superpc, glmnet, matrixStats, survminer, survival, rms, tidyr,
pls, Rdpack, methods, stats, gplots, ggplot2
Packaged: 2019-01-21 06:25:34 UTC; Olajumoke
Author: Olajumoke Evangelina Owokotomo [aut, cre],

pls, Rdpack(>= 0.11-0), methods, stats, gplots, ggplot2

pls, Rdpack, methods, stats, ggplot2, dplyr

Title: Basic Functions to Investigate Metabolomics Data Matrices
Description: A set of functions to investigate raw data from (metabol)omics experiments intended to be used on a raw data matrix, i.e. following peak picking and signal deconvolution. Functions can be used to normalize data, detect biomarkers and perform sample classification.
Imports: C50, caret, e1071, mixOmics, pcaMethods, plyr, rpart, rattle