Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments. Export

@article{citeulike:5046822, abstract = {ABSTRACT: BACKGROUND: Quality assessment methods, that are common place in engineering and industrial production, are not widely spread in large-scale proteomics experiments. But modern technologies such as Multi-Dimensional Liquid Chromatography coupled to Mass Spectrometry (LC-MS) produce large quantities of proteomic data. These data are prone to measurement errors and reproducibility problems such that an automatic quality assessment and control become increasingly important. RESULTS: We propose a methodology to assess the quality and reproducibility of data generated in quantitative LC-MS experiments. We introduce quality descriptors that capture different aspects of the quality and reproducibility of LC-MS data sets. Our method is based on the Mahalanobis distance and a robust Principal Component Analysis. CONCLUSION: We evaluate our approach on several data sets of different complexities and show that we are able to precisely detect LC-MS runs of poor signal quality in large-scale studies.}, author = {Schulz-Trieglaff, O. and Machtejevas, E. and Reinert, K. and Schl\"{u}ter, H. and Thiemann, J. and Unger, K.}, citeulike-article-id = {5046822}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1756-0381-2-4}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19351414}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19351414}, doi = {10.1186/1756-0381-2-4}, issn = {1756-0381}, journal = {BioData mining}, keywords = {bioinformatics, chromatography, proteomics}, number = {1}, posted-at = {2009-07-03 15:18:32}, priority = {2}, title = {Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments.}, url = {http://dx.doi.org/10.1186/1756-0381-2-4}, volume = {2}, year = {2009} }

TY - JOUR ID - citeulike:5046822 L3 - citeulike-article-id:5046822 N2 - ABSTRACT: BACKGROUND: Quality assessment methods, that are common place in engineering and industrial production, are not widely spread in large-scale proteomics experiments. But modern technologies such as Multi-Dimensional Liquid Chromatography coupled to Mass Spectrometry (LC-MS) produce large quantities of proteomic data. These data are prone to measurement errors and reproducibility problems such that an automatic quality assessment and control become increasingly important. RESULTS: We propose a methodology to assess the quality and reproducibility of data generated in quantitative LC-MS experiments. We introduce quality descriptors that capture different aspects of the quality and reproducibility of LC-MS data sets. Our method is based on the Mahalanobis distance and a robust Principal Component Analysis. CONCLUSION: We evaluate our approach on several data sets of different complexities and show that we are able to precisely detect LC-MS runs of poor signal quality in large-scale studies. IS - 1 JF - BioData mining SN - 1756-0381 TI - Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments. VL - 2 KW - bioinformatics KW - chromatography KW - proteomics AU - Schulz-Trieglaff, O AU - Machtejevas, E AU - Reinert, K AU - Schlüter, H AU - Thiemann, J AU - Unger, K PY - 2009/// UR - http://dx.doi.org/10.1186/1756-0381-2-4 ER -