Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum
| Autor: | Sushmita Roy, Keith Joho, Christopher H. Becker, Markus Anderle, Hua Lin |
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| Rok vydání: | 2004 |
| Předmět: |
Proteomics
Quality Control Serum Statistics and Probability Differential proteomics Constant coefficients Bioinformatics Mass spectrometry Models Biological Sensitivity and Specificity Biochemistry Mass Spectrometry Quadratic equation Liquid chromatography–mass spectrometry Humans Sample preparation Molecular Biology Mathematics Stochastic Processes Reproducibility Models Statistical Reproducibility of Results Blood Proteins Serum samples Computer Science Applications Computational Mathematics Models Chemical Computational Theory and Mathematics Sample Size Biological system Algorithms Blood Chemical Analysis Chromatography Liquid |
| Zdroj: | Bioinformatics. 20:3575-3582 |
| ISSN: | 1367-4811 1367-4803 |
| DOI: | 10.1093/bioinformatics/bth446 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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