NMR of biofluids and pattern recognition: assessing the impact of NMR parameters on the principal component analysis of urine from rat and mouse

Potts, Barbara C. M.; Deese, Alan; Stevens, Gregory J.; Reily, Michael D.; Robertson, Donald G.; Theiss, Jeffrey C.

doi:10.1016/s0731-7085(01)00430-7

Cited by 76 publications

(52 citation statements)

References 14 publications

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“…The success of PCA of NMR metabolomics data is intrinsically tied to the consistency in the handling, preparation, collection, and processing of the NMR data [17]. Problems in phasing, referencing, baseline correction or instrument stability would easily lead to the observed scatter and the outlier seen along PC2.…”

Section: Principal Component Analysis Of Simulated Metabolomic Datamentioning

confidence: 99%

“…The success of the application of PCA in the analysis of NMR metabolomic data is intrinsically dependent on the consistency of sample and data handling [17]. Any observed variations in the NMR data should be related to the state of the cell and organism, as opposed to subtle changes in chemical shifts, line-widths, baseline or artifacts from processing.…”

Section: Introductionmentioning

confidence: 99%

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Negative impact of noise on the principal component analysis of NMR data

Halouska

Powers

2006

Journal of Magnetic Resonance

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Section: Principal Component Analysis Of Simulated Metabolomic Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Negative impact of noise on the principal component analysis of NMR data

Halouska

Powers

2006

Journal of Magnetic Resonance

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“…The region 6.0-9.0 ppm consisted of signals arising from the aromatic amino acids such as tyrosine, histidine, phenylalanine and formate, and in a few cases signals arising from isoniazid and pyrazinamide-based drug-excipients were characterized. The presence of these signals was 57 in all CSF samples, although the presence of urea in CSF using other biochemical methods was documented earlier. 58 The cyclopropane signal assigned at 0.05 ppm was observed in a significant proportion of tuberculous meningitis patients and formed an important finding in our study.…”

Section: Metabolite Namementioning

confidence: 55%

Proton MR CSF analysis and a new software as predictors for the differentiation of meningitis in children

et al. 2004

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This article describes proton MR spectroscopic analysis of cerebrospinal fluid of 167 children suffering from meningitis and 24 control cases. Quantification of 12 well-separated and commonly observed cerebrospinal fluid metabolites viz., -hydroxybutyrate, lactate, alanine, acetate, acetone, acetoacetate, pyruvate, glutamine, citrate, creatine/creatinine, glucose (total) and urea was carried out using Bruker's NMRQUANT software with respect to a known concentration of sodium-3-(trimethylsilyl)-2,2,3,3-d 4 -propionate (TSP), serving as an external reference. The assignment of urea in CSF is reported for the first time by NMR. The presence of cyclopropane, observed for the first time in tuberculous meningitis overall in 85.1% of cases, acts as a finger-print marker for the differential diagnosis. Multivariate discriminant function analysis was carried out for the proton MR-detected metabolite information and the clinical symptoms data of the meningitis and control cases to find the important descriptors for classification, followed by a re-validation of the entire database. It was found that the control could be differentiated from the disease group with a success rate of 96.4%, followed by the differential diagnosis of tuberculous meningitis with a corresponding value of 77.2%. Excluding the presence of cyclopropane, bacterial meningitis could be classified 84.4% correct and viral meningitis with a rate of 83.3%. It is proposed that the NMR spectroscopic information, along with other routine clinical features, may serve as an additional diagnostic tool for the differential diagnosis of meningitis in children.

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“…Specifically, in Table 2, the estimates of μ 2 (= 2) and μ 3 (= 2) are 1.802 and 1.780 for condition (2), and 1.727 and 1.403 for condition (3). In Table 3, the estimates of μ 2 (= 2.25) and μ 3 (= 2.5) are 2.038 and 2.261 for condition (5), and 1.952 and 1.868 for condition (6). We only report the estimate of μ i s. The other estimates (τ i ,ρ i , andλ i ) are omitted.…”

Section: A Numerical Studymentioning

confidence: 99%

“…However such an assumption is likely violated due to the overlapping peaks, which is further complicated by differences of peak position, line width and baseline correction. Hence bin integration does not necessarily reflect the true changes in spectral areas (Crockford, et al, 2005;Potts, et al, 2001); the concentration information is incoherently evaluated, and is potentially biased.…”

Section: Introductionmentioning

confidence: 99%

Spin–spin coupling information is crucial for unbiased NMR analysis in metabonomics

Hsieh

2011

Statistics and Its Interface

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We address the critical use of spin-spin coupling information for extracting unbiased concentration information of metabolites based on NMR spectroscopy. This coupling information reveals a truncating status due to binning and baseline correction, termed as vertical and horizontal truncations, which are typical operations needed in bin integration for area under spectral peaks. The likelihood function incorporating truncation status of the involved peak areas is analytically derived. We demonstrate that the maximum likelihood estimation (MLE) provides unbiased estimates of metabolite concentrations. When the information of truncation status is neglected, the extent of resultant bias is substantial. These results bear fundamental implications on reliability and validity of most of the popular statistical methodology in metabolomics, including the analysis of variance (ANOVA), principal component analysis (PCA) and multiple testings. For a multiple-response one-way ANOVA, a test statistic is proposed and implemented through a numerical study.

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NMR of biofluids and pattern recognition: assessing the impact of NMR parameters on the principal component analysis of urine from rat and mouse

Cited by 76 publications

References 14 publications

Negative impact of noise on the principal component analysis of NMR data

Negative impact of noise on the principal component analysis of NMR data

Proton MR CSF analysis and a new software as predictors for the differentiation of meningitis in children

Spin–spin coupling information is crucial for unbiased NMR analysis in metabonomics

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