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Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA.

ABSTRACT: Statistical tools enable unified analysis of data from multiple global proteomic experiments, producing unbiased estimates of normalization terms despite the missing data problem inherent in these studies. The modeling approach, implementation, and useful visualization tools are demonstrated via a case study of complex biological samples assessed using the iTRAQ relative labeling protocol.

SUBMITTER: Oberg AL 

PROVIDER: S-EPMC2528956 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature

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Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA.

Oberg Ann L AL   Mahoney Douglas W DW   Eckel-Passow Jeanette E JE   Malone Christopher J CJ   Wolfinger Russell D RD   Hill Elizabeth G EG   Cooper Leslie T LT   Onuma Oyere K OK   Spiro Craig C   Therneau Terry M TM   Bergen H Robert HR  

Journal of proteome research 20080104 1

Statistical tools enable unified analysis of data from multiple global proteomic experiments, producing unbiased estimates of normalization terms despite the missing data problem inherent in these studies. The modeling approach, implementation, and useful visualization tools are demonstrated via a case study of complex biological samples assessed using the iTRAQ relative labeling protocol. ...[more]

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