Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum.
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.
SurroMed, Inc., 1430 O'Brien Drive, Menlo Park, CA 94025, USA. manderle@surromed.com <manderle@surromed.com>
Markus
M
Anderle
Sushmita
S
Roy
Hua
H
Lin
Christopher
C
Becker
Keith
K
Joho
D000465:Algorithms; D001774:Blood Chemical Analysis / Q000379:methods*; D001798:Blood Proteins / Q000032:analysis*; D002853:Chromatography, Liquid / Q000379:methods*; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D008954:Models, Biological; D008956:Models, Chemical; D015233:Models, Statistical; D040901:Proteomics / Q000379:methods*; D011786:Quality Control; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D044967:Serum / Q000737:chemistry*; D013269:Stochastic Processes
D001798:Blood Proteins