Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum

Authors:
Markus Anderle;Sushmita Roy;Hua Lin;Christopher Becker;Keith Joho
Affiliations:
SurroMed, Inc., 1430 O'Brien Drive, Menlo Park, CA 94025, USA;SurroMed, Inc., 1430 O'Brien Drive, Menlo Park, CA 94025, USA;SurroMed, Inc., 1430 O'Brien Drive, Menlo Park, CA 94025, USA;SurroMed, Inc., 1430 O'Brien Drive, Menlo Park, CA 94025, USA;SurroMed, Inc., 1430 O'Brien Drive, Menlo Park, CA 94025, USA
Venue:
Bioinformatics
Year:
2004

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Abstract

Summary: Using replicated human serum samples, we applied an error model for proteomic differential expression profiling for a high-resolution liquid chromatography-mass spectrometry (LC-MS) platform. The detailed noise analysis presented here uses an experimental design that separates variance caused by sample preparation from variance due to analytical equipment. An analytic approach based on a two-component error model was applied, and in combination with an existing data driven technique that utilizes local sample averaging, we characterized and quantified the noise variance as a function of mean peak intensity. The results indicate that for processed LC-MS data a constant coefficient of variation is dominant for high intensities, whereas a model for low intensities explains Poisson-like variations. This result leads to a quadratic variance model which is used for the estimation of sample preparation noise present in LC-MS data.