Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments.
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Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments. / Schulz-Trieglaff, Ole; Machtejevas, Egidijus; Reinert, Knut; Schlüter, Hartmut; Thiemann, Joachim; Unger, Klaus.
In: BIODATA MIN, Vol. 2, No. 1, 1, 2009, p. 4.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments.
AU - Schulz-Trieglaff, Ole
AU - Machtejevas, Egidijus
AU - Reinert, Knut
AU - Schlüter, Hartmut
AU - Thiemann, Joachim
AU - Unger, Klaus
PY - 2009
Y1 - 2009
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.
AB - 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.
U2 - 10.1186/1756-0381-2-4
DO - 10.1186/1756-0381-2-4
M3 - SCORING: Zeitschriftenaufsatz
VL - 2
SP - 4
JO - BIODATA MIN
JF - BIODATA MIN
SN - 1756-0381
IS - 1
M1 - 1
ER -
