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MetaboLyzer: A Novel Statistical Workflow for Analyzing Postprocessed LC–MS Metabolomics Data
journal contribution
posted on 2014-01-07, 00:00 authored by Tytus D. Mak, Evagelia
C. Laiakis, Maryam Goudarzi, Albert J. FornaceMetabolomics, the global study of
small molecules in a particular system, has in the past few years
risen to become a primary -omics platform for the study of metabolic
processes. With the ever-increasing pool of quantitative data yielded
from metabolomic research, specialized methods and tools with which
to analyze and extract meaningful conclusions from these data are
becoming more and more crucial. Furthermore, the depth of knowledge
and expertise required to undertake a metabolomics oriented study
is a daunting obstacle to investigators new to the field. As such,
we have created a new statistical analysis workflow, MetaboLyzer,
which aims to both simplify analysis for investigators new to metabolomics,
as well as provide experienced investigators the flexibility to conduct
sophisticated analysis. MetaboLyzer’s workflow is specifically
tailored to the unique characteristics and idiosyncrasies of postprocessed
liquid chromatography–mass spectrometry (LC–MS)-based
metabolomic data sets. It utilizes a wide gamut of statistical tests,
procedures, and methodologies that belong to classical biostatistics,
as well as several novel statistical techniques that we have developed
specifically for metabolomics data. Furthermore, MetaboLyzer conducts
rapid putative ion identification and putative biologically relevant
analysis via incorporation of four major small molecule databases:
KEGG, HMDB, Lipid Maps, and BioCyc. MetaboLyzer incorporates these
aspects into a comprehensive workflow that outputs easy to understand
statistically significant and potentially biologically relevant information
in the form of heatmaps, volcano plots, 3D visualization plots, correlation
maps, and metabolic pathway hit histograms. For demonstration purposes,
a urine metabolomics data set from a previously reported radiobiology
study in which samples were collected from mice exposed to γ
radiation was analyzed. MetaboLyzer was able to identify 243 statistically
significant ions out of a total of 1942. Numerous putative metabolites
and pathways were found to be biologically significant from the putative
ion identification workflow.
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3 D visualization plotsHMDBinvestigatorcorrelation mapsradiobiology studyMetaboLyzerion identification workflowmolecule databasesanalysis workflowNovel Statistical Workflowdemonstration purposesKEGGLipid Mapsγ radiationomics platformmetabolomics datametabolomic researchLCvolcano plotsurine metabolomics dataion identification
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