An empirical Bayes model using a competition score for metabolite identification in gas chromatography mass spectrometry.

ABSTRACT:

Background

Mass spectrometry (MS) based metabolite profiling has been increasingly popular for scientific and biomedical studies, primarily due to recent technological development such as comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS). Nevertheless, the identifications of metabolites from complex samples are subject to errors. Statistical/computational approaches to improve the accuracy of the identifications and false positive estimate are in great need. We propose an empirical Bayes model which accounts for a competing score in addition to the similarity score to tackle this problem. The competition score characterizes the propensity of a candidate metabolite of being matched to some spectrum based on the metabolite's similarity s

SUBMITTER: Jeong J 

PROVIDER: S-EPMC3228553 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature