title: Normalization regarding non-random missing values in high-throughput mass spectrometry data.
abstract: We propose a two-step normalization procedure for high-throughput mass spectrometry (MS) data, which is a necessary step in biomarker clustering or classification. First, a global normalization step is used to remove sources of systematic variation between MS profiles due to, for instance, varying amounts of sample degradation over time. A probability model is then used to investigate the intensity-dependent missing events and provides possible substitutions for the missing values. We illustrate the performance of the method with a LC-MS data set of synthetic protein mixtures.
authors.affiliation: Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. pwang@fhcrc.org
authors.forename: Pei
authors.initials: P
authors.lastname: Wang
authors.forename: Hua
authors.initials: H
authors.lastname: Tang
authors.forename: Heidi
authors.initials: H
authors.lastname: Zhang
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Whiteaker
authors.forename: Amanda G
authors.initials: AG
authors.lastname: Paulovich
authors.forename: Martin
authors.initials: M
authors.lastname: Mcintosh
mesh_terms: D002853:Chromatography, Liquid; D019295:Computational Biology; D013058:Mass Spectrometry / Q000706:statistics & numerical data*; D015233:Models, Statistical; D011336:Probability; D011506:Proteins / Q000302:isolation & purification
chemical_list: D011506:Proteins