Work

A bayesian based functional mixed-effects model for analysis of LC-MS data

Year: 2009

Type: conference-paper

Abstract: A Bayesian multilevel functional mixed-effects model with group specific random-effects is presented for analysis of liquid chromatography-mass spectrometry (LC-MS) data. The proposed framework allows alignment of LC-MS spectra with respect to both retention time (RT) and mass-to-charge ratio (m/z). Affine transformations are incorporated within the model to account for any variability along the RT and m/z dimensions. Simultaneous posterior inference of all unknown parameters is accomplished via Markov chain Monte Carlo method using the Gibbs sampling algorithm. The proposed approach is computationally tractable and allows incorporating prior knowledge in the inference process. We demonstrate the applicability of our approach for alignment of LC-MS spectra based on total ion count profiles derived from two LC-MS datasets.

Source: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

DOI: 10.1109/iembs.2009.5332859

Authors: G.K. Befekadu, M.G. Tadesse, H.W. Ressom

Institutions: Georgetown University, Georgetown Lombardi Comprehensive Cancer Center

Language: English

Topic: Metabolomics and Mass Spectrometry Studies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funder: National Cancer Institute

Awards: Novel Machine Learning Methods for Analysis of MALDI-TOF Mass Spectrometry Data, Peptide Biomarker Discovery by Mass Spectrometry for Early Detection of Liver Can