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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

61 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings of the Pacific Symposium on Biocomputing 2006, PSB 2006
Pages315-326
Number of pages12
StatePublished - 2006
Externally publishedYes
Event11th Pacific Symposium on Biocomputing 2006, PSB 2006 - Maui, HI, United States
Duration: 3 Jan 20067 Jan 2006

Publication series

NameProceedings of the Pacific Symposium on Biocomputing 2006, PSB 2006

Conference

Conference11th Pacific Symposium on Biocomputing 2006, PSB 2006
Country/TerritoryUnited States
CityMaui, HI
Period3/01/067/01/06

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