SCFIA: a statistical corresponding feature identification algorithm for LC/MS.

ABSTRACT:

Background

Identifying corresponding features (LC peaks registered by identical peptides) in multiple Liquid Chromatography/Mass Spectrometry (LC-MS) datasets plays a crucial role in the analysis of complex peptide or protein mixtures. Warping functions are commonly used to correct the mean of elution time shifts among LC-MS datasets, which cannot resolve the ambiguity of corresponding feature identification since elution time shifts are random. We propose a Statistical Corresponding Feature Identification Algorithm(SCFIA) based on both elution time shifts and peak shape correlations between corresponding features. SCFIA first trains a set of statistical models, and then, all candidate corresponding features are scored by the statistical models to find the maximum likelihood soluti

SUBMITTER: Cui J 

PROVIDER: S-EPMC3233610 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature