Optimizing the Use of Quality Control Samples for Signal Drift Correction in Large-Scale Urine Metabolic Profiling Studies.
Analytical Chemistry (Impact Factor: 5.83). 02/2012;
Conference Paper: Evaluation of normalization methods for analysis of LC-MS data[Show abstract] [Hide abstract]
ABSTRACT: The purpose of normalization of data generated by liquid chromatography coupled with mass spectrometry (LC-MS) is to reduce bias due to differences in sample collection, biomolecule extraction, and instrument variability. In this paper several normalization methods are reviewed and evaluated based on LC-MS data acquired from experimental and quality control (QC) samples. Specifically, LC-MS data from a metabolomic study aimed at discovering liver cancer biomarkers are analyzed to evaluate the performance of the normalization methods. ANOVA models are used for identification of ions with statistically significant peak intensities between liver cancer and cirrhotic controls. Also, LC-MS data from QC samples are analyzed to assess the ability of the normalization methods in decreasing the variability of ion intensity measurements in multiple runs. Significant run to run variability is observed despite normalizing the LC-MS data by various methods. Thus, it is important to select a suitable normalization method for each data set, as it is difficult to find a method that is applicable for all types of LC-MS data.Bioinformatics and Biomedicine Workshops (BIBMW), 2012 IEEE International Conference on; 01/2012
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ABSTRACT: Abstract Gas chromatography/mass spectrometry (GC/MS) is one of the major analytical drivers in metabonomics. Metabonomics involves the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification. Through the analyses of metabolites, researchers endeavor to unravel novel biomarkers that facilitate diagnosis or prognosis of diseases, prediction of therapeutic and toxicological outcomes, and assessment of potential drug candidates. The high sensitivity, high peak resolution, and reproducibility of GC/MS combined with the availability of databases and spectral libraries for metabolite identification are unique attributes that facilitate its application in metabonomics. The technical maturity of GC/MS has culminated in its application in large-scale metabonomic studies. In this chapter, we provide an overview of GC/MS-based metabonomics, its applications in biomarker discovery, and specific strategies adopted in large-scale metabonomic investigations.01/2013: pages 131-144; Academic Press., ISBN: 9780123944467
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ABSTRACT: Differences in sample collection, biomolecule extraction, and instrument variability introduce bias to data generated by liquid chromatography coupled with mass spectrometry (LC-MS). Normalization is used to address these issues. In this paper, we introduce a new normalization method using the Gaussian process regression model (GPRM) that utilizes information from individual scans within an extracted ion chromatogram (EIC) of a peak. The proposed method is particularly applicable for normalization based on analysis order of LC-MS runs. Our method uses measurement variabilities estimated through LC-MS data acquired from quality control samples to correct for bias caused by instrument drift. Maximum likelihood approach is used to find the optimal parameters for the fitted GPRM. We review several normalization methods and compare their performance with GPRM. To evaluate the performance of different normalization methods, we consider LC-MS data from a study where metabolomic approach is utilized to discover biomarkers for liver cancer. The LC-MS data were acquired by analysis of sera from liver cancer patients and cirrhotic controls. In addition, LC-MS runs from a quality control (QC) sample are included to assess the run to run variability and to evaluate the ability of various normalization method in reducing this undesired variability. Also, ANOVA models are applied to the normalized LC-MS data to identify ions with intensity measurements that are significantly different between cases and controls. One of the challenges in using label-free LC-MS for quantitation of biomolecules is systematic bias in measurements. Several normalization methods have been introduced to overcome this issue, but there is no universally applicable approach at the present time. Each data set should be carefully examined to determine the most appropriate normalization method. We review here several existing methods and introduce the GPRM for normalization of LC-MS data. Through our in-house data set, we show that the GPRM outperforms other normalization methods considered here, in terms of decreasing the variability of ion intensities among quality control runs.Proteome Science 11/2013; 11(Suppl 1):S13. DOI:10.1186/1477-5956-11-S1-S13 · 1.88 Impact Factor
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