title: Probabilistic mixture regression models for alignment of LC-MS data.
abstract: A novel framework of a probabilistic mixture regression model (PMRM) is presented for alignment of liquid chromatography-mass spectrometry (LC-MS) data with respect to retention time (RT) points. The expectation maximization algorithm is used to estimate the joint parameters of spline-based mixture regression models and prior transformation density models. The latter accounts for the variability in RT points and peak intensities. The applicability of PMRM for alignment of LC-MS data is demonstrated through three data sets. The performance of PMRM is compared with other alignment approaches including dynamic time warping, correlation optimized warping, and continuous profile model in terms of coefficient variation of replicate LC-MS runs and accuracy in detecting differentially abundant peptides/proteins.
authors.affiliation: Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, NW, Washington, DC 20057, USA. gkb8@georgetown.edu
authors.forename: Getachew K
authors.initials: GK
authors.lastname: Befekadu
authors.forename: Mahlet G
authors.initials: MG
authors.lastname: Tadesse
authors.forename: Tsung-Heng
authors.initials: TH
authors.lastname: Tsai
authors.forename: Habtom W
authors.initials: HW
authors.lastname: Ressom
mesh_terms: D000818:Animals; D002853:Chromatography, Liquid / Q000379:methods* / Q000592:standards; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods* / Q000592:standards; D008956:Models, Chemical; D011506:Proteins / Q000737:chemistry*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D011506:Proteins