Abstract
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.
Dates
Type | When |
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Created | 21 years, 1 month ago (July 29, 2004, 8:17 p.m.) |
Deposited | 2 years, 7 months ago (Jan. 25, 2023, 11:48 a.m.) |
Indexed | 5 months, 1 week ago (March 22, 2025, 4:45 a.m.) |
Issued | 21 years, 1 month ago (July 29, 2004) |
Published | 21 years, 1 month ago (July 29, 2004) |
Published Online | 21 years, 1 month ago (July 29, 2004) |
Published Print | 20 years, 8 months ago (Dec. 12, 2004) |
@article{Anderle_2004, title={Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum}, volume={20}, ISSN={1367-4803}, url={http://dx.doi.org/10.1093/bioinformatics/bth446}, DOI={10.1093/bioinformatics/bth446}, number={18}, journal={Bioinformatics}, publisher={Oxford University Press (OUP)}, author={Anderle, Markus and Roy, Sushmita and Lin, Hua and Becker, Christopher and Joho, Keith}, year={2004}, month=jul, pages={3575–3582} }