MetMSLine
R functions for automation of biomarker discovery based on processing downstream of peak picking softwares.
Overview
The workflow consists of 5 stages:
pre-processing. Performs all multiparametric preprocessing steps for large-scale high-resolution LC-MS metabolomic datasets.
PCA outlier removal and cluster identification. Principal Component Analysis (PCA) of pre-processed LC-MS data with iterative automatic outlier removal based on a user defined Hotelling’s T2 ellipse expansion and PCA scores cluster identification (using PAM clustering and regression).
Objective univariate statistical analysis based on covariate type. Multiparametric, automatic regression analysis, biomarker discovery, hierarchical clustering analysis and cluster ion and isotope identification for high resolution LC-MS data with multiple continuous Y variables.
Biomarker identification...
...
Installation
install directly from github using the devtools package. First install devtools, instructions can be found here: https://github.com/hadley/devtools
library(devtools)
install_github('WMBEdmands/MetMSLine')
Getting started
After MetMSLine installation has completed read the package vignette "MetMSLineBasics"
for some tips on getting started and also the vignette MetMSLine for further
examples of the package functions.
Just type vignette('MetMSLineBasics') or vignette('MetMSLine') to view
the pdf versions of the vignettes.
The package also includes some example data to illustrate the workflow.
licence
The MetMSLine package is licenced under the GPLv3 (http://www.gnu.org/licenses/gpl.html).