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PubMed · 17094249

Normalization regarding non-random missing values in high-throughput mass spectrometry data.

Pei Wang·Hua Tang·Heidi Zhang·Jeffrey Whiteaker·Amanda G Paulovich·Martin Mcintosh
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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.

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Chromatography, LiquidComputational BiologyMass SpectrometryModels, StatisticalProbabilityProteinsJournal ArticleResearch Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov't

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Pei Wang, Hua Tang, Heidi Zhang, Jeffrey Whiteaker, Amanda G Paulovich, Martin Mcintosh. 2006. Normalization regarding non-random missing values in high-throughput mass spectrometry data.. https://pubmed.ncbi.nlm.nih.gov/17094249/

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