R functions for automation of biomarker discovery based on processing downstream of peak picking softwares.
The MetMSLine workflow scripts and associated functions have now migrated to package form. The first 3 data processing scripts as discussed in the publication (PreProc.QC.LSC.R, Auto.PCA.R, Auto.MV.Regress.R) are incorporated within this package and explained in the two accompanying vignettes. The 4th and 5th scripts (Auto.MS.MS.match.R, DBAnnotate.R) which are concerned with metabolite annotation have now been largely supplanted by the compMS2Miner package. The original scripts discussed in the publication can still be found in the MetMSLine_Scripts repository.
MetMSLine combined with the compMS2Miner package are intended to facilitate autonomous metabolomic data analysis. The MetMSLine and compMS2Miner packages combined with the xcms and CAMERA R packages can achieve a complete and largely autonomous metabolomic workflow.
If you find MetMSLine useful please cite us:
MetMSLine: an automated and fully integrated pipeline for rapid processing of high-resolution LC-MS metabolomic datasets. William Matthew Bell Edmands, Dinesh Kumar Barupal, Augustin Scalbert Bioinformatics 2015; 31 (5): 788-790. DOI: 10.1093/bioinformatics/btu705
The workflow consists of 4 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/statistical analysis, biomarker discovery for high resolution LC-MS data with multiple Y variables.
-
Concerned with unknown LC-MS feature annotation has now been largely supplanted by the compMS2Miner package. ...
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')
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.
The MetMSLine package is licenced under the GPLv3 (http://www.gnu.org/licenses/gpl.html).