Interdependence of Signal Processing and Analysis of Urine 1H NMR Spectra for Metabolic Profiling

被引:43
作者
Zhang, Shucha [1 ]
Zheng, Cheng [2 ]
Lanza, Ian R. [3 ]
Nair, K. Sreekumaran [3 ]
Raftery, Daniel [1 ]
Vitek, Olga [2 ]
机构
[1] Purdue Univ, Dept Chem, W Lafayette, IN 47907 USA
[2] Purdue Univ, Dept Stat, W Lafayette, IN 47907 USA
[3] Mayo Clin Coll Med, Div Endocrinol, Rochester, MN 55905 USA
关键词
PRINCIPAL COMPONENT ANALYSIS; TIME-DOMAIN ALGORITHM; BASE-LINE CORRECTION; MASS-SPECTROMETRY; NMR; NORMALIZATION; STRATEGIES; DISCOVERY; DISEASE; MOUSE;
D O I
10.1021/ac900424c
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Metabolic profiling of urine presents challenges because of the extensive random variation of metabolite concentrations and the dilution resulting from changes in the overall urine volume. Thus statistical analysis methods play a particularly important role; however, appropriate choices of these methods are not straightforward. Here we investigate constant and variance-stabilization normalization of raw and peak picked spectra, for use with exploratory analysis (principal component analysis) and confirmatory analysis (ordinary and Empirical Bayes t-test) in H-1 NMR-based metabolic profiling of urine. We compare the performance of these methods using urine samples spiked with known metabolites according to a Latin square design. We find that analysis of peak picked and logarithm-transformed spectra is preferred, and that signal processing and statistical analysis steps are interdependent. While variance-stabilizing transformation is preferred in conjunction with principal component analysis, constant normalization is more appropriate for use with a t-test. Empirical Bayes t-test provides more reliable conclusions when the number of samples in each group is relatively small. Performance of these methods is illustrated using a clinical metabolomics experiment on patients with type 1 diabetes to evaluate the effect of insulin deprivation.
引用
收藏
页码:6080 / 6088
页数:9
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[1]   StePSIM -: a method for stepwise peak selection and identification of metabolites in 1H NMR spectra [J].
Ammann, L. P. ;
Merritt, M. .
METABOLOMICS, 2007, 3 (01) :1-11
[2]   Gaussian binning: a new kernel-based method for processing NMR spectroscopic data for metabolomics [J].
Anderson, Paul E. ;
Reo, Nicholas V. ;
DelRaso, Nicholas J. ;
Doom, Travis E. ;
Raymer, Michael L. .
METABOLOMICS, 2008, 4 (03) :261-272
[3]  
[Anonymous], 2005, BIOINFORMATICS COMPU
[4]   Urinary creatinine concentrations in the US population: Implications for urinary biologic monitoring measurements [J].
Barr, DB ;
Wilder, LC ;
Caudill, SP ;
Gonzalez, AJ ;
Needham, LL ;
Pirkle, JL .
ENVIRONMENTAL HEALTH PERSPECTIVES, 2005, 113 (02) :192-200
[5]   CONTROLLING THE FALSE DISCOVERY RATE - A PRACTICAL AND POWERFUL APPROACH TO MULTIPLE TESTING [J].
BENJAMINI, Y ;
HOCHBERG, Y .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1995, 57 (01) :289-300
[6]   Statistical strategies for avoiding false discoveries in metabolomics and related experiments [J].
Broadhurst, David I. ;
Kell, Douglas B. .
METABOLOMICS, 2006, 2 (04) :171-196
[7]   Robust baseline correction algorithm for signal dense NMR spectra [J].
Chang, David ;
Banack, Cory D. ;
Shah, Sirish L. .
JOURNAL OF MAGNETIC RESONANCE, 2007, 187 (02) :288-292
[8]   A benchmark for affymetrix GeneChip expression measures [J].
Cope, LM ;
Irizarry, RA ;
Jaffee, HA ;
Wu, ZJ ;
Speed, TP .
BIOINFORMATICS, 2004, 20 (03) :323-331
[9]   Scaling and normalization effects in NMR spectroscopic metabonomic data sets [J].
Craig, A ;
Cloareo, O ;
Holmes, E ;
Nicholson, JK ;
Lindon, JC .
ANALYTICAL CHEMISTRY, 2006, 78 (07) :2262-2267
[10]   Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data [J].
Cruz-Marcelo, Alejandro ;
Guerra, Rudy ;
Vannucci, Marina ;
Li, Yiting ;
Lau, Ching C. ;
Man, Tsz-Kwong .
BIOINFORMATICS, 2008, 24 (19) :2129-2136