Name
Title | ||
|---|---|---|
Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments. |
Abstract | ||
|---|---|---|
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.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.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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1186/1756-0381-2-4 | BioData mining |
Keywords | Field | DocType |
measurement error,algorithms,bioinformatics,applied statistics,liquid chromatography mass spectrometry,liquid chromatography,industrial production,mass spectrometry,outlier detection,mahalanobis distance,principal component analysis | Data mining,Anomaly detection,Proteomics,Liquid chromatography–mass spectrometry,Computer science,Mass spectrometry,Bioinformatics | Journal |
Volume | Issue | ISSN |
2 | 1 | 1756-0381 |
Citations | PageRank | References |
5 | 0.47 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ole B Schulz-Trieglaff | 1 | 263 | 23.38 |
| Egidijus Machtejevas | 2 | 5 | 0.47 |
| Knut Reinert | 3 | 1020 | 105.87 |
| Hartmut Schlüter | 4 | 5 | 1.15 |
| Joachim Thiemann | 5 | 6 | 0.82 |
| Klaus Unger | 6 | 5 | 0.47 |