Multi-class alignment of LC-MS data using probabilistic-based mixture regression models.
In this paper, a framework of probabilistic-based mixture regression models (PMRM) is presented for multi-class alignment of liquid chromatography-mass spectrometry (LC-MS) data. The proposed framework performs the alignment in both time and measurement spaces of the LC-MS spectra. The expectation maximization (EM) algorithm is used to estimate the joint parameters of spline-based mixture regression models and prior transformation densities. The latter are incorporated to account for variability in time and measurement spaces of the data. As a proof of concept, the proposed method is applied to align a single-class replicate LC-MS spectra generated from proteins of lysed E.coli cells. Its performance is compared with the dynamic time warping (DTW) and continuous profile model (CPM) approaches.
Befekadu
Getachew K
GK
Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA.
Tadesse
Mahlet G
MG
Hathout
Yetrib
Y
Ressom
Habtom W
HW
D000465:Algorithms; D002853:Chromatography, Liquid / Q000379:methods*; D001330:Electronic Data Processing; D004926:Escherichia coli / Q000378:metabolism; D013058:Mass Spectrometry / Q000379:methods*; D015233:Models, Statistical; D008962:Models, Theoretical; D016011:Normal Distribution; D011336:Probability; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors